Magic Quadrant for Data Integration Tools

8 December 2025- ID G00827564- 71 min read

By Michele Launi, Nina Showell, and 2 more

Data integration tools remain a fundamental architectural component as organizations increasingly seek improved capabilities to support their operational, analytical and AI use cases. This research helps data and analytics leaders make their decisions by analyzing 20 vendors competing in this market.

Strategic Planning Assumption

By 2027, AI assistants and AI-enhanced workflows within data integration tools will reduce manual effort by 60% and enable self-service data management.

Market Definition/Description

The market for data integration tools consists of stand-alone software products that enable organizations to combine data from multiple sources and perform tasks related to data access, transformation, enrichment and delivery. They enable use cases such as data engineering, delivering modern data architectures, self-service data integration, operational data integration and supporting AI projects. Data management leaders procure data integration tools for their teams, including data engineers and data architects, or for other users, such as business analysts or data scientists. These products are primarily consumed as SaaS or deployed on-premises, in public or private cloud, or in hybrid configurations.

The common use cases or business problems addressed by data integration tools include:

Data engineering — Data integration by technical user personas to develop, manage and optimize data pipelines, mostly for analytical use cases
Delivering modern data management architectures — Data integration to deliver modern data management design patterns, such as lakehouse, data fabric and data mesh, and deliverables, such as data products
Self-service data integration — Data integration activities by less-technical user personas for various analytical demands of data, such as analytics and business intelligence (ABI), and data science use cases
Operational data integration — Data integration to implement various operational data integration use cases, such as consolidation of master data, delivery and use of data hubs, interenterprise and partner data sharing, and application integration
Supporting AI projects — Data integration to support AI projects with complex requirements, such as building chatbots or recommendation systems, and to support delivery of AI-ready data

Mandatory Features

Data integration tools must:

Support extraction, ingestion and delivery of data using multiple common data integration styles, including bulk/batch data movement, data replication and synchronization (including change data capture), stream data integration and data virtualization. This feature includes the availability of out-of-the-box and configurable connectors to seamlessly access data sources and targets.

Support data transformation, including basic (e.g., string manipulation and calculation), intermediate (e.g., data source merging and data aggregation) and advanced (e.g., complex parsing, text mining and multischema data modeling) data transformations. This feature can be delivered through prebuilt, reusable, configurable, or custom components and pipelines.

Common Features

Common features for data integration tools include:

Augmentation features that leverage generative AI (GenAI) and prepackaged machine learning (ML) algorithms to autogenerate data pipeline code and documentation, optimize data integration operations (e.g., anomaly detection and autorecovery), and use natural language to query and transform data. These features simplify and streamline tasks and/or mask complexity to improve ease of use.
Metadata management features that support the extensive discovery, access, use, and sharing of technical and operational metadata (e.g., usage data, transaction logs and system workloads) and business metadata (e.g., glossary). These features operate by either leveraging embedded data catalogs or exposing metadata to well-integrated external systems.
Data governance features that assist data governance mandates (e.g., data quality, data lineage, policy enforcement, masking and annotation) while handling data to meet specific use cases (e.g., master data management and data sharing).

DataOps features that support data pipeline operations. These include change management of data-related artifacts (e.g., Git integration of data pipelines and data model management), automation (e.g., automated testing), and orchestration of data delivery (e.g., continuous integration/continuous delivery [CI/CD] pipelines).
FinOps features to iteratively monitor, control and optimize spending, understand product performance, and make choices regarding price-to-performance trade-offs for data integration workloads, resulting in optimal allocation of resources.
AI-focused features, including processing unstructured data, ingesting data into vector databases and training AI models, such as large language models (LLMs). These features include support for implementing retrieval-augmented generation (RAG) architectures and interacting via interfaces such as the Model Context Protocol (MCP).

Magic Quadrant

Figure 1: Magic Quadrant ,for Data Integration Tools

Vendor Strengths and Cautions

Ab Initio

Ab Initio is a Leader in this Magic Quadrant. It offers Ab Initio Data Platform as its data integration tool, which clients mainly use for their end-to-end data management needs, such as data integration, data quality, metadata management and data governance policy execution.

Ab Initio’s operations are geographically diverse, with clients across sectors, including financial services, telecom and healthcare. Ab Initio is investing in multiple AI capabilities to simplify complex data management tasks.

Strengths

Reliable execution: Ab Initio remains privately held with stable leadership, long-standing enterprise customers, highly skilled support engineers and a resilient platform, ensuring that integration jobs remain fully operational on modern infrastructure. It is one of four recipients of the 2025 Customer Choice award for data integration tools, based on Gartner Peer Insights data.
Metadata-driven automation: Data engineers can automate data integration tasks with a metadata-driven, templated approach. Its platform offers blueprints for data acquisition, replication, normalization, quality enhancements, governance policy executions and reconciliation.
AI-centric innovation: Its agentic framework, called AI Central, augments data management tasks by offering agents that can perform various tasks, such as identifying appropriate data, understanding data, automatically creating pipelines and communicating with business users in natural language.

Cautions

Poor marketing strategy: Ab Initio is less known among noncustomers and suffers from lower market visibility than other Leaders in this Magic Quadrant. Its website does not disclose any of its thought leadership. Ab Initio says it has expanded its marketing team with dedicated content creators focused on producing solution briefs, customer stories and industry content.
Steep learning curve and limited community support: Gartner Peer Insights data highlights that the Ab Initio platform user interface is clunky. Gartner clients tell us that recruiting skilled Ab Initio developers is challenging, and developers require significant training. Additionally, there is minimal community support, making it difficult to find help, tutorials or troubleshooting resources outside of official channels.
Pricing complexity with high cost perception: In Gartner client inquiries, small to medium enterprise customers often reported high license costs. Its on-premises deployments are rigid and inflexible. In competitive situations, we have noticed Ab Initio’s high license cost as the primary barrier for small and medium businesses, and to some extent, among government clients.

Amazon Web Services

Amazon Web Services (AWS) is a Leader in this Magic Quadrant. Its data integration tools include AWS Glue, Amazon Managed Workflows for Apache Airflow (MWAA), Amazon Kinesis, Amazon Managed Streaming for Apache Kafka (MSK), Amazon Managed Service for Apache Flink, and Amazon Athena. AWS offers Amazon SageMaker as its main data management platform, which includes many of the data integration services listed above.

AWS’ operations are geographically diverse, and it supports customers across all industries.

AWS is investing in expanding its source connectivity, improving its support for streaming use cases, and providing a unified experience to its customers through Amazon SageMaker.

Strengths

Tight product integration: AWS data integration tools’ availability within Amazon SageMaker provides a smoother experience with other Amazon data, analytics and AI services (e.g., Amazon S3, Amazon Redshift and Amazon Bedrock) through shared metadata and governance. AWS also offers zero-ETL, a set of fully managed, configurable integrations to simplify data ingestion from several AWS sources (e.g., Amazon Aurora and Amazon DynamoDB) and SaaS applications (e.g., Salesforce, ServiceNow and Zendesk).
AI initiatives enablement: AWS provides robust capabilities for preparing and processing data for large language models (LLMs) and generative AI (GenAI), including automated multimodal workflows, built-in vectorization, fine-tuning of training datasets and fitness-for-use validation. These features ensure data is properly managed, validated and governed for AI use cases.
Fast-paced product strategy: AWS has adopted a customer-driven approach to evolving its data integration offerings, both reducing the gaps with other players and innovating on most data integration capabilities. This has led to significant advancements, particularly in stream data integration, advanced data transformation and data governance support.

Cautions

High cost perception continues: On Gartner Peer Insights, the cost of AWS data integration tools is the most frequent concern. Customers report unexpected cost escalations, especially for large-scale, long-running or unoptimized workloads. They demand more transparent billing practices and better cost management tools.
AWS ecosystem centricity: AWS data integration tools mainly target AWS as the pipeline destination, with limited target connectors, which in some cases could hinder multicloud or hybrid setups. Third-party source connectors are more numerous, but often lack configuration flexibility, requiring workarounds for complex scenarios, for example, with nonstandard source system implementations.
Challenging maintenance and troubleshooting: AWS customers report that managing and debugging data pipelines is often complex and time-consuming, with overly technical processes and limited self-healing features. Inconsistent documentation and the necessity of trial-and-error methods frequently prolong resolution times. These factors hinder the autonomy of business stakeholders with limited technical skills.

Boomi

Boomi is a Niche Player in this Magic Quadrant. It offers Boomi Data Integration and Boomi Data Hub as stand-alone components within Boomi Enterprise Platform (BEP), a data management suite that integrates data, APIs and AI agents.

Boomi is an established vendor in the integration platform as a service (iPaaS) market and has recently gained data integration capabilities through the acquisition of Rivery in December 2024, which became the Boomi Data Integration product. Rivery was an Honorable Mention in last year’s Magic Quadrant. Boomi’s operations and client base are geographically diverse. Boomi is investing in positioning its products as components of a unified platform for AI-driven automation, and in adding features, such as an AI knowledge base to feed agents built in Boomi or externally.

Strengths

Vision for a comprehensive platform: Boomi supports organizations in connecting data, applications, APIs and AI agents. It offers a holistic approach that resonates with many customers, especially those who are seeking data integration plus additional data management and automation capabilities, such as building and orchestrating AI agents.
Industry-specific and business-user focus: Boomi’s experience in the iPaaS market gives it a deeper understanding of industry-specific requirements and business user needs compared to many competitors. For instance, Boomi can integrate ERP systems, supply chain management tools and IoT devices. The platform also provides prebuilt starter templates to accelerate development.
Strong change data capture (CDC): Boomi offers strong data replication technology that enables customers to move data with near-real-time latency. This includes both one-to-one and one-to-many replication, using log-based CDC, query-based CDC and data synchronization.

Cautions

Limited feature depth: Boomi’s data integration capabilities are relatively new. Some features, such as metadata management, are not as fully developed as those offered by long-standing competitors. Boomi may also be less suitable for highly complex data transformations or scenarios where FinOps is important.
New entrant to the data integration market: Boomi’s relative newness in the data integration space presents challenges, especially regarding its fit within an organization’s broader data management ecosystem. While Boomi offers value to companies that are looking for an integrated data integration and iPaaS solution, those that are looking for a best-of-breed approach and care solely about data integration may be less likely to choose Boomi.
Developing global support: Although Boomi offers global support and its customer base is geographically diverse, its strongest presence is in the U.S., and its support model is still closely tied to its iPaaS product. While Rivery’s customers have historically reported strong support, postacquisition consistency has yet to be fully proven. Minor support and account management issues are common after acquisitions and are typically resolved within two years.

CData Software

CData Software is a Niche Player in this Magic Quadrant. It offers CData Sync, CData Connect AI, CData Virtuality, CData Arc, and CData Platform as its data integration tools.

Its operations are focused in North America and Europe, with a growing presence in APAC. CData serves clients in various sectors, including services, manufacturing and financial services. Organizations predominantly use CData for its connectivity and replication solutions. CData has expanded its data virtualization capabilities and is investing in CData Platform with new GenAI and data governance capabilities.

Strengths

Strong data connectivity: CData’s data integration tools use a wide range of connectors for apps, databases, and cloud services. It is known for direct connections, such as Workday to Snowflake and Power BI to SAP HANA. Both businesses and technology vendors (through OEM agreements) use its connectors.
Low TCO and predictable pricing: Customers prefer CData because it offers clear, predictable pricing and lower overall costs for data integration. Its connection-based pricing model outweighs the competitors, who often charge based on usage or data volume.
AI-centric innovation: CData Connect AI lets organizations connect their systems to AI models and agentic platforms like Claude, ChatGPT, Microsoft Copilot Studio and n8n. This allows customers to get real-time insights from their data using natural language queries. It also enables AI agents to autonomously access, analyze and execute actions across connected systems. Its Vibe Querying with MCP content series is highly relevant for organizations exploring AI-ready data use cases.

Cautions

Limited differentiation in CData Platform: CData is shifting from a connectivity provider to a data integration platform provider. However, only a small percentage of CData customers are using its latest CData Platform, which mainly repackages existing components (CData Sync, CData Virtuality and CData Connect AI) without any significant data integration features.
Customer support experience: Gartner Peer Insights data indicate that some users find CData’s documentation as unclear, and troubleshooting job logs can be difficult, which creates dependency on CData customer support. CData premium or enterprise support plan is required to overcome support response delays.
Weak vertical strategy: CData is a general-purpose data integration vendor, and it does not offer any industry solutions. It lacks dedicated industry experts as part of its sales teams, making it less viable in regulatory scenarios. However, CData Virtuality offers some predefined industry models and compliance frameworks.

Confluent

Confluent is a Challenger in this Magic Quadrant. It offers Confluent Cloud, Confluent Platform and WarpStream as its data integration tools. Organizations predominantly use Confluent for data stream processing to meet their operational data integration needs.

Its operations are geographically diverse, with clients primarily in the banking and financial services, healthcare and public sectors. Confluent is investing in several key features for its customers, including Streaming Agents on Flink and unified monitoring of Confluent Platform instances within Confluent Cloud.

IBM announced its intention to acquire Confluent on 8 December 2025. At the time of publication of this document, both Confluent and IBM met the inclusion criteria and continued to operate as separate entities. Gartner will provide additional insight and research to clients as more details become available.

Strengths

Strong brand awareness for data streaming: Confluent comes up frequently in our Gartner client inquiries for stream data integration. It enables real-time data streaming and processing using Apache Kafka and Flink natively. Confluent’s regional customer advisory boards, organized by industry verticals, add to its brand awareness and help gather feedback on product enhancements and steer the product roadmap.
Partnership ecosystem: Confluent has several co-sell, co-develop and OEM technology partners, including major cloud providers, application vendors and database vendors. It has several certified global and regional system integrators (SIs) for implementations, such as implementation support for migrating Apache Kafka workloads to Confluent.
AI-centric innovation with vertical solutions: Confluent introduced AI model inference with direct LLM calls from its native Flink SQL component. Its real-time inferencing simplifies operations for a variety of industry-specific use cases, such as financial services (e.g., fraud detection and credit scoring), insurance (e.g., claims automation and incident response) and retail (e.g., supply chain optimization and demand prediction).

Cautions

Limited suitability beyond streaming: Confluent products are not designed to provide a data virtualization layer and are primarily used for streaming. Recent additions, such as Flink snapshot queries (for batch processing) and Tableflow (to materialize topics as open tables within lakehouses), enable limited batch scenarios.
Lagging in augmented data integration: Confluent lags behind its competitors in augmented and GenAI-enabled features in both its products and product roadmap. Although Confluent provides an AI chat assistant within Flink to guide developers, customers will not find optimized pipeline generation or active metadata-driven automation in its products.
Usability challenges: Gartner client interactions indicate a clunky product interface and missing standard data integration features, such as extract, transform and load (ETL), and extract, load and transform (ELT). Additionally, Gartner clients reported time-consuming initial deployment and upgrade challenges for Confluent Platform.

Denodo

Denodo is a Leader in this Magic Quadrant. It offers the Denodo Platform, both on-premises and on all public clouds, and Agora as a cloud-based, fully managed deployment option of the Denodo Platform. Organizations predominantly use Denodo for logical or distributed data architectures in hybrid and multicloud deployments.

Its operations are geographically diverse, with customers primarily in the financial services, manufacturing and public sectors. Denodo is investing in several AI features, such as Denodo Assistant, Denodo DeepQuery and Denodo AI Software Development Kit (SDK).

Strengths

Strong brand awareness for data virtualization: Denodo comes up frequently in Gartner client inquiries for data virtualization. Denodo Platform uses virtual models, distributed query processing with an embedded Presto parallel processing engine, and AI-based smart caching for optimized performance and costs.
Simplified data access: Denodo simplifies access to heterogeneous data with fine-grained access control and eases self-service with natural language queries for business users. It evolved its native data catalog into a data product marketplace with a guided experience for the full life cycle of data product management. It is one of four recipients of the 2025 Customer Choice award for data integration tools, based on Gartner Peer Insights data.
AI-centric innovation: Denodo Assistant boosts data engineer productivity with autodescriptions and autotagging of sensitive data, while Denodo DeepQuery answers open-ended business questions using reasoning models and its strong semantic understanding of an organization’s internal data.

Cautions

Physical data movement limitations: Denodo is rarely used for batch integration, streaming or data replication, especially when there are strict performance SLAs around handling high data volumes. It has recently introduced features for batch data integration, such as writeback capability using Apache Arrow Flight and schema evolution, and log-based CDC using Debezium.
Denodo supplemented with other vendors: Clients expressed the need to adopt other data integration tools, in addition to Denodo, to fully cover all types of data integration, outside of data virtualization. We have also observed that Gartner clients often supplement the Denodo Platform with additional third-party solutions for their complex data transformation requirements.
Operational challenges: Gartner Peer Insights data highlighted several operational challenges with Denodo Platform, such as difficulties in resolving third-party software integration issues, setting up single sign-on in complex deployments, the need for occasional manual driver installations, and inadequate native monitoring capabilities.

Fivetran

Fivetran is a Challenger in this Magic Quadrant. It offers Fivetran, its main data integration platform, HVR for database replication, and Census for reverse ETL.

Its operations are primarily focused in North America and Europe, with a growing presence in APAC. The company supports various sectors, including technology, financial services and manufacturing. Fivetran has invested in delivering data for its customers’ AI projects via unstructured file replication for PDF and other document types, and a Quickstart model named Unified RAG for chunking and vectorization into Snowflake apps. It has been running a “Data Readiness for AI” campaign to shed light on these new capabilities.

Fivetran closed its acquisition of Tobiko Data (SQLMesh) on 3 September 2025 and announced its intention to merge with dbt on 13 October 2025. At the time of publication of this document, Fivetran met the inclusion criteria and continued to operate as a separate entity. Gartner will provide additional insight and research to clients as more details become available.

Strengths

Enhancements in connectivity: Fivetran offers over 700 fully managed connectors with automated schema drift handling, an SDK for custom connector creation, and over 50 Quickstart models to convert data from SaaS apps into analytics-ready tables. Recent enhancements include target connectivity to cloud data lakes through its Managed Data Lake Service, built on open table formats, and to enterprise apps like Salesforce and Zendesk via its Census Data acquisition. A Connector AI Agent is on the roadmap.
Flexible pricing options: Fivetran has adjusted its “monthly active rows processed” pricing structure so that charges are now calculated separately for each connector, facilitating more precise cost attribution among business units. Fivetran’s enterprise license agreements provide price predictability, while time-bound free usage per connector adds further value.
Increased deployment options: Fivetran provides SaaS deployment, self-hosted HVR, hybrid deployment (with separation of control and data planes), and Snowflake native apps. Customers can use Fivetran-hosted dbt Core for a more complete SaaS experience across data ingestion and transformation.

Cautions

Basic metadata management: Fivetran captures technical metadata, such as sync durations and user actions, but does not use this metadata to provide intelligent recommendations. Advanced capabilities rely on the Fivetran Platform Connector, exposing metadata to third-party solutions, including vendors such as Alation, Atlan and Collibra.
Lagging data governance support: Fivetran does not support governance operations such as policy and compliance management. Its data governance capabilities are focused on data encryption, column blocking/hashing and role-based access control, not natively supporting broader compliance, lineage and data privacy needs.
Limited data delivery styles: Fivetran lacks support for data virtualization and real-time data integration patterns. While the Census acquisition gives users the ability to merge data across multiple cloud data warehouses, key features such as smart query acceleration are missing. For streaming, Fivetran SaaS typically does not support sync frequencies below one minute, which limits it to support near-real-time use cases only.

Google

Google is a Leader in this Magic Quadrant. Its data integration products include Cloud Data Fusion for visual pipelines, Datastream for data replication through change data capture, Dataflow for streaming, Cloud Composer for orchestration, and BigQuery Data Engineering Agent for augmenting and automating pipeline development within BigQuery.

Its operations are geographically diverse, and it supports customers across all industries.

Google is investing in embedding AI into its data integration offering and simplifying the experience for business users, focusing on autonomous agents’ contextualization and trustworthiness.

Strengths

AI use cases support: Google’s data integration tools closely align with its comprehensive AI vision, offering multimodal data processing, AI model validation and scalable, efficient vectorization. Seamless integration with Vertex AI ensures access to Google’s full suite of AI capabilities.
Broad usability: Google’s data integration tools offer a user-friendly design, meeting the needs of diverse personas. Business users benefit from visual pipelines and drag-and-drop components; data scientists benefit from data wrangling, preparation and notebook integration; and data engineers leverage Google’s code-first and orchestration tools.
Agentic data engineering: Google’s BigQuery Data Engineering Agent automates pipeline creation and management based on user inputs and metadata. It can automatically detect issues and inefficiencies, suggesting solutions or resolving them autonomously, significantly reducing manual effort. However, its use is currently limited to BigQuery and does not extend to the pipelines implemented with the rest of Google’s data integration offerings.

Cautions

Fragmented product portfolio: Google’s data integration features span over ten separate tools, leading to a fragmented user experience (UX) and limiting seamless transitions between integration styles. Advanced features are unevenly rolled out, resulting in inconsistent access to their benefits across users.
Narrow reach: Google’s data integration tools are marketed and designed primarily for use within the broader Google Cloud ecosystem, making them most appealing to organizations that have already chosen Google Cloud as their primary cloud platform. This focus can create barriers for those seeking hybrid or multicloud flexibility, as well as for organizations without Google Cloud expertise, limiting adoption among clients outside the Google Cloud environment.
Limited supporting capabilities: To utilize the full breadth of support capabilities, such as data governance and metadata management, Google clients need to complement their data integration tools with other Google Cloud products (e.g., BigQuery and Dataplex Universal Catalog), though these are tightly integrated with its data integration offering.

IBM

IBM is a Leader in this Magic Quadrant. IBM supports data integration through IBM watsonx.data integration, a streamlined product that includes IBM DataStage for bulk/batch data movement, IBM Data Replication for data replication and synchronization, and IBM StreamSets for stream data integration. These capabilities are also provided as part of the broader data management platform called IBM watsonx.data.

IBM has a diverse customer base, with a strong presence in North America, Europe and APAC. It supports various sectors, including government, financial services and telecom. IBM has been investing in support for migrations from traditional DataStage to next-gen DataStage using code conversion utilities, IBM Expert Labs and business partner consulting services, and usage waivers for migrations.

Strengths

Streamlined data integration offering: IBM combines all its data integration capabilities into watsonx.data integration under a unified control plane. This enables users to decouple pipeline design from the integration style, which can be selected at runtime and deployed across multiple hybrid cloud environments. Databand integration strengthens data pipeline observability.
Strong metadata management support: IBM’s data integration offering is well-integrated with IBM watsonx.data intelligence (a stand-alone product), which includes IBM Knowledge Catalog, Data Lineage, and Data Product Hub. This enables strong data lineage and discoverability of data products published using watsonx.data integration.
Enhanced unstructured data integration: IBM offers the Unstructured Data Integration service within its products. Users can extract, transform, chunk and embed data, both natively and via embedded Granite and Slate models. The embeddings are loaded into the vector store Milvus, which is a part of watsonx.data. Customers can connect unstructured data pipelines to Model Context Protocol (MCP)-compliant APIs using IBM watsonx Orchestrate.

Cautions

Limited traction beyond DataStage modernization: Among Gartner inquiries, IBM watsonx.data integration rarely appears in competitive situations and modern data architecture blueprints unless organizations already use legacy DataStage. Clients must invest in adjacent products, such as wastonx.data and watsonx.data intelligence, to unlock all the required capabilities.
High cost perception: Gartner inquiries and Peer Insights indicate clients find IBM’s data integration products more expensive than comparable vendor solutions. Unified Resource Unit pricing for watsonx.data integration will further this perception, especially for small and midsize businesses (SMBs) seeking agile products.
Uneven integration styles adoption: While watsonx.data integration supports different data integration styles, IBM is rarely shortlisted by organizations requiring specialist tools that are not part of an integrated offering (e.g., data virtualization or data replication), especially when IBM mainframes or databases are not the primary data source.

Informatica

Informatica is a Leader in this Magic Quadrant. It offers Cloud Data Integration (CDI) as its primary data integration service within its Intelligent Data Management Cloud (IDMC) platform. IDMC is Informatica’s broader data management platform that also integrates applications, APIs, data and AI agents.

Its operations are geographically diverse, and its clients are typically large enterprises across various industries. Informatica is focusing on evolving its data integration capabilities to support AI agent engineering use cases.

Salesforce closed its acquisition of Informatica on 18 November 2025. During our evaluation phase, Informatica met the inclusion criteria and was evaluated for this Magic Quadrant. Gartner will provide additional insight and research to clients as more details become available.

Strengths

Strong brand awareness and enterprise focus: Informatica is frequently in Gartner client inquiries for data integration. Its IDMC platform offers enterprise-scale data integration with built-in capabilities for metadata management, data governance, quality and security. The platform supports many data sources and targets, covering multiple use cases and styles of data integration.
Strong AI and GenAI capabilities: Informatica shows a clear vision for AI-ready data. It enables organizations to integrate enterprise data with LLMs through its capabilities such as unstructured data chunking, vector embedding and RAG inferencing. It uses conversational interfaces — CLAIRE Copilot and CLAIRE GPT — for generating, documenting and troubleshooting data pipelines. Its Agent Engineering service offers GenAI recipes, MCP endpoints and multiagent orchestration.
Broad partnership ecosystem: Informatica has several technology partnerships for co-engineering, co-marketing, and co-selling with hyperscalers and major players, such as Amazon Web Services, Databricks, Microsoft, Oracle and Snowflake. It has hundreds of long-standing global and regional SIs with go-to-market sales plays, such as PowerCenter modernization and industry playbooks, creating high talent availability.

Cautions

Uncertainty around acquisition: In November 2025, Salesforce closed its acquisition of Informatica. While near-term impact on Informatica customers using the latest IDMC platform is expected to be minimal, there are concerns about the long-term IDMC roadmap, pricing and licensing under Salesforce leadership.
End of support and maintenance: Informatica will end standard support for PowerCenter 10.5 by March 2026. Customers must migrate to Informatica Cloud Data Integration for PowerCenter, IDMC or purchase extended support for an additional year or two. Gartner client inquiries indicate that some customers are exploring alternatives.
Slower growth than competitors: Informatica’s growth lags behind other vendors in this Magic Quadrant, with a relative decline in market share. Gartner client inquiries indicate a shift toward using native data integration capabilities provided by hyperscalers and data integration capabilities offered by smaller vendors that offer niche point solutions, challenging its growth.

K2view

K2view is a Visionary in this Magic Quadrant. It offers the K2view Data Product Platform as its data integration tool.

K2view’s customer base is primarily in North America and Europe. The vendor supports various sectors, including telecom, banking, retail and insurance. K2view is investing in partner-led growth, which includes launching a new partner portal and expanding its partner certification program called K2Alpinist to help extend its partner network across global and regional system integrators.

Strengths

Support for data product initiatives: K2view organizes data by business domains, such as customer, product, and order, through its Micro-Database architecture. Each Micro-Database can be separately governed, persisted, or virtualized, and is equipped with its own CDC listener to ingest data in real time and detect schema changes. This supports customers’ data product initiatives.
Strong GenAI and good agentic AI support: K2view grounds GenAI apps with integrated customer data through its GenAI Data Fusion offering. Micro-Databases support vector data and text-to-SQL. Users can design agentic orchestration workflows via K2view’s support for MCP and Agent2Agent protocols, and create Data GPT models tailored to their own datasets.
Built for operational scale: K2view can run parallel ETL jobs for individual Micro-Database targets to support scalability and high throughput. Data transformations defined in Broadway, K2view’s low-code data flow designer, are performed in memory and processed in near real time. For these reasons, operational use cases, such as master data management and data hub, are well-supported by K2view.

Cautions

Low adoption for analytical data integration workloads: Based on Gartner client inquiries, K2view is rarely evaluated as a data integration tool for populating data lakes and warehouses, as the focus is often on operational use cases. K2view lacks comprehensive support for open table formats and advanced pushdown options for popular cloud data platforms.
Less suited for SMBs: K2view makes most of its revenue from large enterprises that require managing massive data volumes and real-time needs. Therefore, K2view’s pricing, while transparent, is considered more suitable for enterprise-grade use cases in large organizations and is deemed less suitable for SMBs with limited integration requirements.
Steep learning curve: Some customers report that K2view’s documentation for complex functionalities fails to keep up with the rapid pace of new features. For instance, users want better guidance on usage scenarios for the various ‘Actors’ (K2view’s data transformations) that are continually added to Broadway. Additionally, processes for tool setup and version upgrades are complex.

Matillion

Matillion is a Challenger in this Magic Quadrant. It provides Matillion ETL as its data integration tool, Matillion Data Loader as its data ingestion tool, Maia, a collection of AI agents, and Data Productivity Cloud (DPC) as its cloud data integration platform.

Matillion’s customer base is broad, with organizations primarily in North America and Europe. The vendor supports various sectors, including financial services, healthcare and retail. Matillion is investing in expanding DPC’s capabilities for multiple integration patterns, user types, deployment models and improved productivity.

Strengths

Augmented GenAI capabilities: Matillion has launched Maia to augment data engineering tasks such as building, testing and monitoring data pipelines. Users can also use Maia to migrate pipeline logic from other tools into DPC.
Multipersona UX: Matillion has added support for embedding SQL and Python libraries directly into pipelines, along with enhanced DataOps support for code version control. Maia enables nontechnical users to build pipelines with natural language interactions. Combined with Matillion’s longstanding low-code visual canvas UX, these capabilities enable organizations to empower a diverse user base.
Improvement in partner integrations: Matillion continues to expand its partnerships with popular data management vendors, cloud service providers (CSPs), and global SIs. Of note are its support for Snowflake AI Services, such as Cortex Parse Document and Snowflake Vector Upsert, via its hybrid SaaS deployment within Snowpark. Matillion is one of four recipients of the 2025 Customer Choice award for data integration tools, based on Gartner Peer Insights.

Cautions

Lack of innovation beyond GenAI: Matillion’s support for data lakehouses is limited, as integration with open table formats is still in the roadmap. Lack of native active metadata support limits Matillion’s ability to provide dynamic recommendations to improve pipeline design, although sharing metadata with other platforms via its Metadata APIs serves as a reasonable interim solution.
Pricing not ideal for smaller enterprises: Some smaller enterprises have noted that Matillion DPC’s starting tier price is high and limits the number of users. DPC’s FinOps capabilities, such as configurable cost limits at the task level and dynamic compute allocation based on row count, help with long-term cost optimization.
Lacks advanced data virtualization features: Matillion’s data virtualization capabilities are limited to external table objects, pushdown ELT queries, and the use of cloud-native third-party data virtualization services via JDBC connectors. Advanced features, such as query acceleration, dynamic query optimization and caching are missing.

Microsoft

Microsoft is a Leader in this Magic Quadrant. It offers Data Factory within its broader data management platform, Microsoft Fabric. It also offers Microsoft Azure Data Factory (ADF), SQL Server Integration Services (SSIS), Power Query, and Azure Synapse Link as its data integration tools.

Its operations are geographically diverse, serving a broad client base. Microsoft continues to invest in expanding Microsoft Fabric’s data integration, governance and AI capabilities, including support for database mirroring into OneLake, real-time intelligence for streaming data, and Fabric data agents, allowing users to interact with their data using natural language.

Strengths

Broad data management vision: Microsoft’s vision for Fabric as a converged data management platform is a key differentiator, enabling customers to manage data integration, analytics, governance and AI within a single environment. The platform supports real-time intelligence and streaming, with support for both technical and business users through self-service features.
Stable growth: Microsoft maintains strong market momentum, with widespread adoption of Microsoft Fabric across industries, including strong adoption of the data integration features within the platform. Its extensive partner ecosystem, global reach and new feature development are driving strong growth.
Strong streaming capabilities: Microsoft Fabric features a dedicated module called Real-Time Intelligence, enabling users to ingest, route, process and activate streaming data. Microsoft recognizes the growing demand for real-time data integration and has included these capabilities in a way that is accessible and user-friendly, as reported by Gartner clients.

Cautions

Product still maturing: Data Factory in Microsoft Fabric is still a relatively new offering, and despite rapid progress, some features remain under development or refinement and may not be suitable for production use. For example, capabilities such as enhanced continuous integration/continuous delivery support and easier data copying have only recently been improved from a previously low-maturity state.
Limited stand-alone adoption: Like other hyperscalers, Microsoft’s data integration tools are most effective for organizations that are already committed to the Azure or Microsoft Fabric ecosystem. These tools are less commonly adopted as a stand-alone data integration solution, which limits their appeal for organizations that have narrower requirements.
Limited on-premises capabilities: While Microsoft continues to support on-premises data integration through tools like SSIS and on-premises data gateway, its primary focus is on expanding cloud-native features within Fabric. Organizations with significant on-premises requirements may find the available tools less suitable for their needs, especially as innovation is centered on cloud-based features.

Oracle

Oracle is a Leader in this Magic Quadrant. It offers Oracle Cloud Infrastructure (OCI) as its main data management platform, which includes OCI GoldenGate and OCI Data Integration. It also offers Oracle Data Integrator (ODI) and Oracle Autonomous Database Data Studio as its data integration tools.

Oracle’s operations are geographically diverse. Its clients are in a broad range of industries, including banking and financial services, retail, and telecom. Oracle is investing significantly in deeper capabilities for AI, ML, RAG and agentic AI. It is also continuing to invest in operational data integration, focusing on performance, reliability, scalability, and other enhancements for both on-prem and cloud deployments.

Strengths

Support for on-prem, cloud and hybrid architectures: As other vendors slowly decommission their support for on-prem architectures, Oracle remains dedicated to supporting customers’ deployment needs. Oracle provides robust solutions for multicloud scenarios, offering support for more than 200 cloud regions and native integrations with every hyperscaler.
Remarkable data replication/synchronization: Oracle continues to lead the market in CDC, with GoldenGate providing robust, real-time replication and high availability. Its proven reliability supports complex operational data integration scenarios, and it remains a trusted solution for many of the world’s largest financial services organizations.
Strong augmentation and AI execution: Oracle maintains consistently strong execution in delivering AI-related features, with a focus on semantics and pattern recognition, which are foundational for building a data fabric. Oracle has a strong agentic framework for building AI agents and supporting LLM integrations, and we expect this work will continue.

Cautions

Less focus on analytical data integration: Oracle’s strength in operational data integration often causes the market to overlook its capabilities for analytics. While Gartner agrees that Oracle products can unify operational and analytical data processing, GoldenGate sometimes overshadows other OCI data integration capabilities, and customers often do not always consider Oracle for analytical data integration.
Reduced customer interest: Oracle’s data integration tools are mentioned less frequently in Gartner client inquiries, indicating lower customer interest compared to previous years. Customer retention numbers are below market average, without a growth in the total number of data integration customers, which remained substantially stable.
Perception of high costs: As in previous years, the perception of high costs for Oracle’s products remains a challenge. While Oracle has introduced a free pricing tier to address these concerns, pricing transparency is still lacking, and customers often remain concerned about the potential for unexpectedly high expenses.

Precisely

Precisely is a Niche Player in this Magic Quadrant. It offers Precisely Data Integrity Suite as its main data management platform, which includes data integration services. It also offers Precisely Connect for traditional batch ETL and data replication (mostly from legacy sources) and Precisely Ironstream for integrating IBM Z mainframes and IBM i systems into modern platforms.

Its operations are geographically diverse, and it supports clients mostly in the financial services and insurance, government and retail sectors. Precisely is investing in augmenting UX for the Precisely Data Integrity Suite, with autonomous agents and a context-aware coding assistant in its roadmap.

Strengths

Legacy modernization: Precisely’s data integration tools efficiently connect to legacy operating systems and mainframes, enabling seamless data migration to modern data architectures. This unlocks data governance, cataloging, observability, enrichment and quality checks, otherwise challenging to achieve.
Operations and partners: Precisely has a robust operational structure, offering reliable technical support, product documentation, mentoring and training to upskill client teams. Its extensive global partner network includes technology providers, SIs, industry-specific solution providers and hundreds of referral and reseller partners, further expanding its reach.
Customer-centric vision: Precisely emphasizes customer value through professional and consulting services valued by clients, account management and customer success programs. Its flexible pricing and licensing let clients adopt only the needed platform capabilities. This tailored approach contributes to its high customer retention.

Cautions

Niche offering: Precisely focuses more on the breadth of its Data Integrity Suite than the depth of its data integration capabilities. Its products are mainly selected for expertise with legacy sources and rarely for broader integration needs, resulting in relatively low market visibility and new customer acquisition.
Lagging innovation: Precisely has shown a slow adaptation to market trends, despite decent R&D investments. Its roadmap does not sufficiently address key gaps in core data integration capabilities, such as stream data integration, data virtualization and supporting features like DataOps and FinOps, limiting its overall competitiveness.
Scarcity of AI features: Precisely’s data integration tools currently lack embedded AI capabilities — including augmentation, automation and intelligent optimization — and do not offer dedicated features to support AI projects. Innovative elements, such as AI agents and a context-aware assistant, are on the roadmap but have not yet been delivered, placing Precisely at a disadvantage compared to most competitors in the market.

Qlik

Qlik is a Leader in this Magic Quadrant. Its data integration products include Qlik Talend Cloud, Talend Data Fabric, and Qlik Replicate. The majority of product development is centered on Qlik’s latest product offering, Qlik Talend Cloud.

Qlik’s operations are geographically diverse, with a substantial presence in Europe and North America, and it supports customers across all industries. Qlik’s current priorities include assisting customers with end-to-end data management initiatives, such as supporting lakehouse modernization and building trusted data products. It is also continuing to invest in new AI features, such as agentic AI for building data pipelines.

Strengths

Strong data replication/synchronization: Qlik’s data replication and synchronization capabilities, including advanced log-based CDC, are among the strongest in the market. The platform delivers high-performance, high-speed log-based CDC for near-real-time data movement, ensuring timely and reliable data delivery. These capabilities support projects such as database migrations, real-time analytics and hybrid cloud scenarios.
Holistic vision: Qlik is driven by a stable and compelling vision for data management, with a strong emphasis on supporting capabilities, such as metadata management and data governance, which other vendors tend to deprioritize. The recent launch of Qlik Open Lakehouse (part of Qlik Talend Cloud), following the acquisition of Upsolver, also demonstrates Qlik’s commitment to empowering customers to build forward-looking modern lakehouse architectures on Apache Iceberg.
Thorough product capabilities: Qlik provides robust bulk/batch data movement, advanced transformation features, and a broad array of connectors to diverse sources and targets. As a long-standing leader in the market, Qlik demonstrates advanced maturity in data integration, with few notable product weaknesses.

Cautions

Customer frustration with pricing changes: Qlik is transitioning to a consumption-based pricing model for Qlik Talend Cloud, but is facing challenges in effectively communicating this change to customers. Gartner inquiries indicate that clients are confused by the new pricing structure and discouraged by unexpected price increases. The lack of a publicly available price list adds to customer frustration.
Uncertain innovation trajectory: Qlik’s R&D pace slowed following the Talend acquisition. Potential buyers should ensure that Qlik’s roadmap aligns with their future needs. While its innovation speed has recently improved, it remains uncertain whether these advancements will continue steadily and enable Qlik to keep pace with the other market leaders.
Adequate augmented UX: While Qlik provides a solid foundational UX, its augmented capabilities are not advanced. Features such as intelligent recommendations for pipeline design and the ability to adapt and optimize existing transformations proactively are limited. Users seeking a more automated, AI-enhanced experience will find Qlik’s products adequate, but not comprehensive.

Safe Software

Safe Software is a Niche Player in this Magic Quadrant. It offers the FME platform as its data integration tool.

Safe Software’s operations are primarily concentrated in the EMEA and North America regions, serving clients mainly in government, energy, utilities and transportation sectors. Safe Software is investing in expanding its capabilities beyond geospatial integration, with a recent emphasis on adding AI and data virtualization features.

Strengths

Exceptional customer satisfaction: Safe Software receives consistently favorable ratings for its product, technical support, sales experience and pricing, leading to high overall customer satisfaction. Customers find FME easy to use, and Safe Software’s smaller company size allows for closer collaboration, enabling customers to provide meaningful input into product development and future enhancements. Safe Software is one of four recipients of the 2025 Customer Choice award for data integration tools, based on Gartner Peer Insights data.
Expanded market focus: Safe Software has broadened its scope from geospatial data integration to an “all-data, any AI” approach, repositioning itself as a comprehensive solution for all data integration challenges.
Continuous product improvements: Safe Software improves its product by introducing new features and capabilities, including last year’s addition of data virtualization. Safe Software’s roadmap includes support for MCP, expanded support for vector databases, and the development of intent-based, nondeterministic workflows with human-in-the-loop processes, reflecting its dedication to advancing its technology portfolio.

Cautions

Limited brand awareness: Despite having a strong product and excellent customer satisfaction, Safe Software’s brand is not widely recognized. This limited mind share means Safe Software is often not included in competitive evaluations, which restricts its ability to grow its customer base and compete with larger, more established vendors.
Metadata and governance limitations: Safe Software provides adequate metadata management and governance capabilities, but lags behind competitors in advanced features such as data contracts, knowledge graph representations and governance-focused workflow management processes. Organizations with complex metadata management and/or governance requirements may need to supplement FME with third-party solutions.
Small company size: Safe Software remains a relatively small company compared to many of its competitors. While this allows for agility and close customer relationships, it can also present challenges in terms of resource allocation, scalability and long-term investment. Safe Software may face difficulties keeping pace with larger vendors that have greater financial and technical resources.

SAP

SAP is a Visionary in this Magic Quadrant. It offers SAP Datasphere as its main data integration tool, which is part of its main data management platform, SAP Business Data Cloud. It also offers SAP Data Services for on-premises deployments and SAP Landscape Transformation Replication Server (SLT) for real-time data replication.

Its operations are geographically diverse, with a majority of its customers in the EMEA and North America regions. Its clients are varied, with a majority in the manufacturing, utilities and technical services sectors. SAP is strengthening SAP Datasphere’s data integration, orchestration, deployment options and AI capabilities, while also expanding its partner ecosystem.

Strengths

Expanded ecosystem and partnerships: SAP Datasphere’s availability within SAP Business Data Cloud enables seamless integration with other data and analytics services, such as SAP Analytics Cloud for business intelligence and SAP BW for warehousing. This tight integration extends to SAP Databricks due to a recent partnership, enabling data science and ML use cases.
Adaptability to data product delivery: SAP Datasphere supports the delivery of SAP data as data products, enabling users to leverage prebuilt business logic and industry expertise, minimizing redundant development efforts. Users can also create and share custom data products tailored to specific needs from third-party data, promoting reusability and knowledge retention across use cases.
Data governance support: SAP provides robust data governance capabilities, including cataloging, lineage tracking, impact analysis, quality enforcement, and semantic enrichment leveraging business metadata and knowledge graphs. These features ensure consistent, semantically aligned data assets and enable comprehensive governance, discoverability and streamlined consumption.

Cautions

SAP-centric solutions: SAP clients typically select its data integration solutions to streamline data extraction from SAP systems and to integrate data across the SAP ecosystem. Although SAP’s tools offer connectors for non-SAP systems, these clients often supplement them with third-party solutions to achieve comprehensive coverage of their broader data integration requirements.
Lagging product roadmap: SAP has fallen behind the market in advancing key capabilities, including data integration styles, like bulk/batch and stream data integration, and supporting features, like DataOps and FinOps, in favor of prioritizing data products. Some clients have expressed concerns about roadmap uncertainty and timely feature releases, despite SAP’s decent market understanding and vision.
Limited AI capabilities: SAP data integration tools provide minimal support for AI projects and offer limited augmented development experience. They lack features for connecting to AI solutions or preparing data, such as multimodal data processing, vectorization and model training. Joule, SAP’s AI assistant, is mainly used for AI-assisted search, semantic generation and infrastructure tasks, and cannot create, modify or optimize data pipelines.

SnapLogic

SnapLogic is a Visionary in this Magic Quadrant. It offers the SnapLogic Agentic Platform as its data integration offering.

SnapLogic’s operations are primarily focused in North America and the EMEA region. It has clients from diverse sectors, including financial services, healthcare, and manufacturing. SnapLogic is investing in its vision of supporting AI projects and enhancing revenue growth opportunities via geographic and vertical expansion, as well as its modernization service, SnapLogic Intelligent Modernizer, which uses AI to automate ETL code migration from other products.

Strengths

Enhanced GenAI and agentic AI support: SnapLogic’s AgentCreator (a service to build AI agents and agentic workflows) now includes a Prompt Composer for refining prompts and validating LLM responses, while SnapGPT, SnapLogic’s integration copilot, is available in a free version. Users can use ML Snaps (prebuilt connectors or transforms) that embed LLM integrations for tasks such as sentiment analysis and vector embedding. MCP client support is available, with server support on the roadmap.
Business user enablement: SnapLogic provides its customers with no-code data ingestion capabilities through SnapLogic AutoSync and no-code data wrangling within the platform through SnapLogic AutoPrep. AutoSuggest offers the functionality to leverage AI and provide the next best Snap when designing data pipelines. SnapLogic AutoLink automatically maps fields between source and target tables.
Converged integration platform offering: SnapLogic provides an iPaaS that combines data, application/API, and agent creation and orchestration patterns natively in the same platform via Snaps. The platform supports several data integration styles and allows users to convert scheduled or triggered pipelines into streaming pipelines.

Cautions

Debugging and performance challenges: Some SnapLogic users report difficulties in debugging pipeline errors, poor documentation and tutorials, and vague error messages in the pipeline dashboard. Some users also report performance lags, such as pipeline changes, platform updates, or too many Snaps in a pipeline, occasionally impacting pipeline performance.
Limited data governance and metadata support: SnapLogic’s Asset Catalog’s capabilities are limited to passive capture of metadata with tagging, classification and search. Data governance features are limited to access controls and data masking. For data lineage, users need to import the OpenLineage specification (which SnapLogic provides) into more comprehensive metadata management tools.
Low market visibility: Gartner continues to receive fewer inquiry volumes from end-user clients about SnapLogic compared to most of its competitors in this Magic Quadrant. SnapLogic’s deepening co-sell partnerships with AWS, Microsoft and Cognizant are expected to improve its presence in cloud data ecosystems and improve overall mind share.

Workato

Workato is a Visionary in this Magic Quadrant. It offers Workato Data Orchestration, its main data integration tool, and Workato Event streams, for stream data integration, both components of its Workato One platform. It also offers Workato IDP for intelligent document processing.

Workato’s operations are geographically diverse, with a majority of its customers in North America. Workato is an established vendor in the iPaaS market that, in 2023, launched its data integration offering. Workato was an Honorable Mention in last year’s Magic Quadrant. It supports various sectors, including financial services, retail and technology. Workato is reinforcing its core data integration capabilities, such as data virtualization and advanced transformations, and enhancing its AI and metadata management support.

Strengths

Innovation roadmap: Workato’s roadmap for its data integration offering extends beyond core capabilities, reflecting a forward-thinking vision for the market. It recognizes the importance of metadata, governance and operationalization of data pipelines, with plans to leverage AI to deliver distinctive functionalities across these areas.
Support for AI projects: Workato provides extensive capabilities for AI initiatives, including multimodal data extraction from unstructured sources, knowledge graph creation, and out-of-the-box connectors to leading LLM and GenAI services. Additionally, it supports the creation of custom AI agents.
Ease of use and augmentation: Customers praise Workato’s intuitive, user-friendly interface and extensive library of prebuilt connectors, transformations and reusable templates. Along with an AI copilot that can generate, modify, and troubleshoot data pipelines, these features enhance the experience for both technical and less-technical users.

Cautions

Market positioning challenges: Workato entered the data integration tools market in 2023, leveraging its established integration capabilities and a wide range of native connectors from its iPaaS offering. While the company has an ambitious vision with advanced capabilities, it remains to be seen whether it can deliver on its roadmap and close the gap with more established competitors.
Capability shortfalls: Workato’s data integration capabilities are still evolving, with core features such as bulk/batch data movement, data virtualization and advanced data transformations scoring below the market average. Some users have reported challenges in building complex pipelines and meeting enterprisewide data integration needs using Workato exclusively.
Unsatisfactory pricing model: According to Gartner Peer Insights data, some customers criticize Workato’s pricing model. They note that the model is not fully consumption-based and includes baseline fees and extra charges for advanced features such as agentic offerings.

Vendors Added and Dropped

We review and adjust our inclusion criteria for Magic Quadrants as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant may change over time. A vendor's appearance in a Magic Quadrant one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.

Added

Boomi
Workato

Dropped

Palantir was dropped from this Magic Quadrant because of the ongoing shift of its offering from data integration to a more general-purpose platform focused on data, analytics and AI application development.
TIBCO was dropped from this Magic Quadrant because its offering is no longer aligned with the data integration tools market definition, and has lower customer interest in it compared to the data integration tools market.

Inclusion and Exclusion Criteria

Inclusion Criteria

The inclusion criteria represent the specific attributes that analysts believe are necessary for inclusion in this research.

To qualify for inclusion, the vendor’s data integration tool (or tools) must be a “stand-alone” product directly usable by the buyer. The vendor must offer a generally available software product that meets Gartner’s definition of a data integration tool. Organizations must be able to procure and use the tool as a stand-alone product, independent of the procurement/usage of other products from the same vendor. The tool must not be part of other solutions, such as an analytics and business intelligence (ABI) platform, database management system (DBMS), integration platform as a service (iPaaS) that primarily focuses on connectivity to applications, be part of other packaged SaaS applications, or be a general-purpose development platform or programming interface that requires extensive customization for data integration.

Vendors should adhere to the Market Definition.

In addition, vendors must satisfy the following quantitative requirements regarding their market penetration and customer base:

Revenue or customer count:
- Either generate at least $50 million in software revenue from data integration tools in the calendar year 2024 — that is, from perpetual license with maintenance, or subscription with support (which would include payment only for data integration software), or through a consumption-based licensing model where the consumption metrics are being used only for the data integration software (on an annual basis).
- Or maintain at least 500 subscription or maintenance paying customers, with at least 50 of those customers being new customers over the past year (where “customers” does not mean individual “user” license seats), for its data integration tools in production. (The number of downloads without license or maintenance revenue is informative, but not a qualifying piece of information.)
Geography and market presence: Have market presence in at least three of the following regions (regional market presence is defined as a minimum of 5% of the revenue of the verified production customer base, as well as the existence of dedicated sales offices or distribution partnerships in a specific region):
- North America (Canada, Mexico, and the U.S.)
- Central and South America
- Europe (including Western Europe and Eastern Europe)
- Middle East and Africa (including North Africa)
- Asia/Pacific region (Including Japan)
Customer Interest: Demonstrated customer interest will also be reviewed and can be assessed through Gartner end-user inquiries, Gartner Peer Insight reviews, Gartner.com search terms, external search engine trends, social media followers and web traffic analytics. A relative lack of customer interest could be a reason to exclude a product/service offering.
The data integration tool/service must be generally available as of 18 July 2025. This includes any new functionality added to the service by the specified date. We do not consider beta, “early access,” “technology preview,” or other not generally available functionality or services. Additionally:
- Any acquired product or service must be offered by the acquiring vendor as of 18 July 2025. Acquisitions closed after this date will be considered under their preacquisition identities, if appropriate, and are represented separately until the publication of the following year’s Magic Quadrant.

Exclusion Criteria

Vendors that focus on narrow use cases that are too specific for the broader data integration market were excluded. In the past, some vendor/supplier tools were excluded because:

They focused on only one horizontal data subject area — for example, the integration of customer data.
They focused on only a single vertical industry.
They provided data integration as a capability embedded within their broader data management/analytics/data science platform, but did not provide a stand-alone/independent or commercially off-the-shelf generally available data integration tool product.
Vendors that only provide support for open-source platforms/frameworks or development platforms, which need to be heavily engineered/customized for specific data integration tasks/use cases, and/or are specific to a single data integration/data delivery style (such as stream data integration only).
Vendors that provide adapters or drivers for various data and analytics sources and targets, thereby indirectly supporting data integration, but do not market a stand-alone data integration tool.
Vendors that only provide self-service data preparation tools for power users, analysts and line-of-business (LOB) users, but these tools do not have the ability to physically move data or operationalize these self-service data flows and models into production through data movement, governance and sharing, if and when needed.

Honorable Mentions

Airbyte is headquartered in California, U.S., and offers its eponymous data integration tool, Airbyte, through the Core, Standard, Plus, Pro, Enterprise Flex, and Enterprise Self-Managed plans. Customers can benefit from its vast library of connectors and its custom connector development framework. Airbyte provides unidirectional data replication from a data source to a target. It is often used by data engineers for building low-latency data pipelines with data transformations, such as data quality enhancements, and governance capabilities, such as data masking and encryption. It supports vector DB compatibility and integrations with RAG frameworks to accelerate AI application development. Airbyte did not meet our inclusion criteria for revenue and customer interest. It was therefore not included in this year’s Magic Quadrant.
Coalesce is headquartered in California, U.S., and offers Coalesce Transform for data transformation and Coalesce Catalog for metadata management (from the recent acquisition of CastorDoc). Clients like Coalesce for its intuitive and AI-based data transformation and modeling capabilities. Coalesce’s customers especially find its product architecture and its code generation capabilities very useful. Coalesce did not meet the inclusion criteria related to support for mandatory features, such as supporting the extraction, ingestion and delivery of data using multiple common data integration styles. It was therefore not included in this year’s Magic Quadrant.
dbt Labs is headquartered in Pennsylvania, U.S., and it offers dbt as a commercial product, which includes an open-source software called dbt Core to design data transformation pipelines and a source-available software called dbt Fusion as the SQL engine. Its customers like dbt for its SQL-based data transformation capabilities, paired with advanced SQL comprehension and parsing capabilities provided by dbt Fusion. Customers can also leverage capabilities such as a semantic layer, an embedded data catalog, dbt Copilot, and different ways of interacting with data, such as via VS Code, a canvas and a Studio IDE. dbt Labs did not meet the inclusion criteria related to support for mandatory features, such as supporting the extraction, ingestion and delivery of data using multiple common data integration styles. It was therefore not included in this year’s Magic Quadrant. dbt announced its intention to merge with Fivetran on 13 October 2025. At the time of publication of this Magic Quadrant, dbt did not meet the inclusion criteria and continued to operate as a separate entity. Gartner will provide additional insight and research to clients as more details become available.
Prophecy is headquartered in California, U.S., and offers the Prophecy AI Data Prep & Analysis Platform. The platform enables users to create data pipelines, with a recent emphasis on supporting business data teams through AI agent-driven data preparation and analysis. Prophecy features an AI-powered visual designer that spans the entire data life cycle, while keeping code as the foundation for more technical users. Prophecy did not meet the criteria for customer interest, revenue and customer count. It was therefore not included in this year’s Magic Quadrant.
Redpanda is headquartered in California, U.S., and it offers several versions of its streaming platform: Redpanda Serverless, Redpanda Bring Your Own Cloud, and Redpanda Self-Managed. Additionally, it offers Redpanda Connect, which provides hundreds of prebuilt connectors and change data capture (CDC) capabilities to integrate heterogeneous systems. Clients value Redpanda for its ability to simplify the development, deployment and maintenance of streaming pipelines. They also appreciate its advanced features that support AI initiatives, such as retrieval-augmented generation (RAG) workflows and the implementation of AI agents. Redpanda did not meet our inclusion criteria for revenue and customer interest. It was therefore not included in this year’s Magic Quadrant.
Rocket Software is headquartered in Massachusetts, U.S., and offers Rocket DataEdge as its data integration product, which includes data replication and synchronization, virtualization and data intelligence modules. Customers appreciate Rocket’s expertise in mainframe and legacy modernization projects, including its capabilities in bidirectional real-time synchronization across environments, data virtualization for secure, real-time data access, and data intelligence to scan, map and visualize data relationships and lineage across systems. Rocket provides comprehensive customer support and implementation services, understanding that legacy modernization projects are often complex. Rocket Software did not meet the criteria for customer interest. It was therefore not included in this year’s Magic Quadrant.
Stratio is headquartered in Spain and offers Stratio Generative AI Data Fabric to simplify data discovery and integration using AI. Clients value Stratio for enabling governed data consumption and enhancing the autonomy of business users. It supports data virtualization, automatic cataloging and governance. When new assets and relationships are found, metadata and semantic schemas are inferred and stored in a knowledge graph, leveraging industry-standard ontologies. A chat interface lets users transform data and ask questions in natural language. Stratio did not fully adhere to the Market Definition and did not meet our inclusion criteria for revenue and customer interest. It was therefore not included in this year’s Magic Quadrant.

Evaluation Criteria

Ability to Execute

Gartner analysts evaluate providers on the quality and efficacy of the processes, systems, methods or procedures that enable IT provider performance to be competitive, efficient and effective, and to positively impact revenue, retention and reputation within Gartner’s view of the market.

Product or Service: Core goods and services offered by the vendor that compete in/serve the defined market. This includes current product/service capabilities, quality, feature sets, skills, etc. Key indicators include (but are not limited to) tool availability and packaging, the critical capabilities, and additional differentiating capabilities specific to the offered data integration tool(s).

Overall Viability: Viability includes an assessment of the overall organization’s financial health, the financial and practical success of the business unit, and the likelihood of the individual business unit to continue to invest in the product, continue offering the product, and advance the state of the art within the organization’s portfolio of products. Key indicators include (but are not limited to) company revenue, market-specific revenue and the organization’s diversification by industry, geography and channels, among others.

Sales Execution/Pricing: The vendor’s capabilities in all presales activities and the supporting structure. This includes deal management, pricing and negotiation, presales support, and the overall effectiveness of the sales channel. Key indicators include (but are not limited to) the alignment of pricing model(s) to customers’ needs, customer growth rate, sales channels and pricing transparency.

Market Responsiveness and Track Record: Ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the vendor’s history of responsiveness. Key indicators include (but are not limited to) alignment with market direction, adherence to customer demand, introduction of features based on customer challenges, and the timeliness of implementing the promised roadmap.

Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization’s message in order to influence the market, promote the brand and business, increase awareness of the products, and establish a positive identification with the product/brand and organization in the minds of buyers. This mind share can be driven by a combination of publicity, promotional, thought leadership, word-of-mouth and sales activities. Key indicators include (but are not limited to) marketing strategies, marketing campaigns, marketing events and end-users’ interest metrics (both internal to Gartner and external).

Customer Experience: Relationships, products and services/programs that enable clients to be successful with the products evaluated. Specifically, this includes the methods by which customers receive technical support or account assistance. This can also include ancillary tools, customer support programs (and their quality), availability of user groups, service-level agreements, and other relevant factors. Key indicators include (but are not limited to) our interactions with end users in inquiries, Peer Insights data, surveys, customer reference calls, touchpoints across various Gartner and external events, community chatter, and vendor briefing data.

Operations: The ability of the organization to meet its goals and commitments. Factors include the quality of the organizational structure, including skills, experiences, programs, systems, and other vehicles that enable the organization to operate effectively and efficiently on an ongoing basis. Key indicators include (but are not limited to) statistics about the company’s FTEs, consulting and technical partners, and the impacts on operations of recent mergers, acquisitions and divestitures.

Ability to Execute Evaluation Criteria


Product or Service	High
Overall Viability	High
Sales Execution/Pricing	High
Market Responsiveness/Record	High
Marketing Execution	Medium
Customer Experience	High
Operations	Low

Source: Gartner (December 2025)

Completeness of Vision

Market Understanding: Ability of the vendor to understand buyers’ needs and translate these needs into products and services. Vendors that show the highest degree of vision listen to and understand buyers’ wants and needs, and can shape or enhance those wants with their added vision. Key indicators include (but are not limited to) awareness of market trends, alignment of current and future offerings with those trends, understanding of key competitors, and understanding of target customers and their needs.

Marketing Strategy: Ability of the vendor to provide a clear, differentiated set of messages consistently communicated throughout the organization and externalized through the website, advertising, customer programs and positioning statements. Key indicators include (but are not limited to) the vendor’s ability to evolve their marketing strategy and adapt and customize it for new buyer personas they are targeting, as well as for new marketing channels, partners and markets.

Sales Strategy: The vendor’s strategy for selling products that uses the appropriate network of direct and indirect sales, marketing, service and communication affiliates to extend the scope and depth of market reach, skills, expertise, technologies, services and the customer base. Key indicators include (but are not limited to) channel strategies and the provision of sales strategies tailored to specific buyer personas/segments.

Offering (Product) Strategy: The vendor’s approach to product development and delivery that emphasizes differentiation, functionality, methodology, and feature set as it maps to current and future requirements. Key indicators include (but are not limited to) the product roadmap, quality and diversity of planned product offerings, and methods and metrics for continually assessing and prioritizing product strategy and future plans.

Business Model: The soundness and logic of a vendor’s underlying business proposition. Key indicators include (but are not limited to) the value proposition offered (what problem does the business solve for its customers?), and planned evolution of the business model (including goals, growth plans, direction and positioning).

Vertical/Industry Strategy: The vendor’s strategy to direct resources, skills and offerings to meet the specific needs of individual market segments, including verticals. Key indicators include (but are not limited to) the strategy for providing differentiation across verticals and industries, as well as the strategy for offering line-of-business-specific differentiation.

Innovation: The vendor’s ability to create direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or preemptive purposes. Key indicators include (but are not limited to) planned future innovations, commitment to R&D and alignment of innovation to customer needs.

Geographic Strategy: The vendor’s strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the “home” or native geography, either directly or through partners, channels and subsidiaries, as appropriate for that geography and market. Key indicators include (but are not limited to) the strategy for global expansion/growth and the way the vendor is addressing geography-specific topics (e.g., standards/regulations/legislation).

Completeness of Vision Evaluation Criteria


Market Understanding	High
Marketing Strategy	Medium
Sales Strategy	Medium
Offering (Product) Strategy	High
Business Model	Low
Vertical/Industry Strategy	Low
Innovation	High
Geographic Strategy	Medium

Source: Gartner (December 2025)

Quadrant Descriptions

Leaders

Leaders in this market stand out for their comprehensive solutions. They typically offer a broad portfolio that goes beyond data integration, seamlessly incorporating data integration products and services into their larger data management platforms.

Leaders have a well-established market presence, robust operational structures and a global reach. They command significant mind share and proactively anticipate emerging market needs — often introducing new product features before there is widespread demand. By identifying evolving business challenges that data integration tools can address, Leaders consistently deliver meaningful business value.

Leaders have strong product offerings, balanced across critical capabilities. They provide robust foundational data integration, advanced supporting capabilities and innovative applications of AI, along with features designed to support AI initiatives. The gaps between Leaders on the execution axis are narrowing, as are the capabilities offered by their products. Several Leaders continue to lose market share to CSPs or smaller vendors that offer products tailored to specific use cases, attractive packaging and pricing, or go-to-market strategies targeting distinct business user segments.

Challengers

Challengers demonstrate a deep understanding of the current data integration tools market demand and possess the market visibility and operational structure to deliver effective solutions. Their maturity in specific capabilities and targeted use cases enables them to address client needs more rapidly and with a lower total cost of ownership compared to many competitors, including Leaders. Additionally, Challengers have established best practices for leveraging their core strengths through innovative delivery models.

Challengers serve substantial customer bases and experience growth rates that exceed the market average. They maintain a strong market presence, although their solutions are often deployed for individual projects or specialized niches within broader data integration requirements. By actively participating in data ecosystems, Challengers fill data integration gaps of other vendors — particularly cloud service providers (CSPs) — and frequently offer greater flexibility than their native tools by supporting multicloud and hybrid environments.

Although stronger in execution, Challengers are continually refining their strategic vision, moving toward platform-based offerings similar to those of Leaders. However, their market success is driven less by disruptive innovation and more by delivering solutions at competitive price points. Through creative pricing, licensing, packaging and go-to-market strategies, Challengers exert pressure on Leaders and secure significant deals.

Visionaries

Visionaries excel at identifying emerging technologies and business trends and at targeting specific market needs that differ significantly from standard practices, while offering capabilities poised for future growth. They have prioritized forward-thinking features and innovative go-to-market strategies to leverage their distinctive strengths.

Visionaries may have limited market recognition or credibility outside their core customer base, primary use cases or specialized industry verticals. They are often still expanding their partnerships with systems integrators, consulting firms and technology partners, and may not yet have the extensive installed base or global reach of larger vendors.

Visionaries may be established players in adjacent data management markets who lack the execution capabilities of the Leaders in this market. Their primary challenge is to translate their vision into effective execution within this space. Visionaries include smaller players who need to accelerate the transformation of vision into execution to match the pace of the Leaders, as well as former Leaders who have slowed down and are no longer able to keep up with others in terms of execution, even though they share a similar vision.

Niche Players

Niche Players typically deliver solid core functionality and features, but have not yet developed a distinct market strategy to broaden their appeal to a wider customer base or expand the use cases for their technology. In this mature data integration market, nearly all Niche Players meet standard expectations for capabilities, operational structure, innovation and pricing, though they still lag behind vendors in other quadrants.

Niche Players excel in addressing specific data integration challenges, such as specialized data delivery methods, source connectivity and use-case-specific features. Within these segments, they provide significant value to their customers. However, Niche Players are less likely to be considered for comprehensive or enterprise-scale data integration projects.

Niche Players are often chosen for their simplicity and cost-effectiveness. Their primary strength is delivering focused solutions that address specific data integration needs, making them well-suited for organizations with straightforward requirements. However, Niche Players may lack the advanced features required for more comprehensive data management needs.

Context

Data integration tools enable organizations to combine data from multiple sources and perform tasks related to data access, transformation, enrichment and delivery. Therefore, data and analytics (D&A) leaders are increasingly seeking a comprehensive range of foundational, advanced and innovative data integration capabilities to modernize their data, analytics and AI architectures.

D&A leaders must navigate a market filled with products that claim to address various data integration challenges. However, not all vendor solutions are equally equipped to deliver the full range of capabilities required across key data integration use cases. As a result, D&A leaders should carefully align the technical capabilities of the tools under consideration with the specific requirements of their data integration architectures. They should also evaluate whether multiple data integration tools are necessary to meet all their data integration needs completely.

D&A leaders must align vendor selection with their organization’s specific requirements. For instance, adopting a solution from a Leader may not be the best fit for companies with lower maturity levels or straightforward data integration needs. Conversely, while the cost-effectiveness of smaller vendors may be attractive, in enterprise scenarios, these solutions might struggle to scale or may not address all the complexities and specific requirements that a Leader can, potentially limiting enterprisewide adoption. This Magic Quadrant provides them with an understanding of the overall market, highlights the common characteristics of vendors in each of the four quadrants, and outlines the strengths and cautions for each individual vendor.

This research should not be used as the sole evaluation tool. Instead, it should be considered alongside other Gartner research and in conjunction with analyst inquiries to gain a comprehensive understanding of the requirements and select the most suitable solution. Relevant resources include its companion piece, Critical Capabilities for Data Integration Tools, as well as the Gartner Peer Insights-based report, Voice of the Customer for Data Integration Tools. D&A leaders must consider additional dimensions of data integration beyond technology, such as those outlined in How to Assess and Improve Your Data Integration Maturity, connect data integration with the overall data architecture strategy with Strategic Roadmap for the Data Fabric Architecture, and explore adjacent markets through analyses like the Magic Quadrant for Integration Platform as a Service and the Market Guide for Data Management Platforms.

Market Overview

Data integration tools are seeing continued evolution, underpinned by customers’ maturity, stable sales growth and widespread adoption. The market grew at a rate of 9.8% in 2024, the same growth rate as in 2023, and reached a total volume of $5.9 billion (see Market Share: Data and Analytics Software, Worldwide, 2024). A conservative estimate for the five-year compound annual growth rate (CAGR) for the 2024 to 2029 time frame forecast is 8.1%, which is lower than the current growth rate (see Forecast: Enterprise Infrastructure Software, Worldwide, 2023-2029, 3Q25 Update). The emerging market for data management platforms — where several stand-alone data management solutions, such as databases, metadata management solutions and data integration tools, are converging — is a major driver of this expected revenue growth slowdown. While data integration will remain a cornerstone of data and analytics architectures, D&A leaders recognize that having these foundational capabilities within a broader, well-integrated environment reduces the effort required to connect all the needed components (see Future of Data Management Markets: Converged Data Management Platforms Drive Tool Consolidation and Market Guide for Data Management Platforms).

Cloud data ecosystems and innovative vendors propel market growth amid leader share decline: The top four vendors in this market, which together hold a 44.5% market share, are all underperforming with respect to the overall market, with an aggregate growth of 3.8% (see Market Share: Data Integration Software, Worldwide, 2024). In contrast, two other categories of vendors are experiencing rapid growth. The first category consists of public cloud hyperscalers, which have achieved an aggregate growth rate of 25.8%. This growth is driven by D&A leaders increasingly migrating larger portions of their architectures to the cloud, while cloud service providers have invested heavily in enhancing their data integration offerings. As a result, clients’ needs are met more effectively, reducing the need for third-party solutions and encouraging customers to move their existing data integration pipelines to these platforms. The second category includes smaller vendors that focus on innovation — whether through unique offerings, creative packaging or novel go-to-market strategies. These companies provide more specialized, tailored solutions that appeal to customers seeking niche, tailored or cost-effective options.

Decentralized data integration requires enhanced user experiences: Technical personas, such as data engineers, are no longer the sole users of data integration tools. As data management and, consequently, data integration become increasingly decentralized, less-technical users — such as business analysts and line-of-business stakeholders — are seeking to perform data integration tasks independently (see What Makes Data Management Leaders and Teams Successful and Quick Answer: The Benefits of a Federated Data Management Practice). To enable this, they require low-code or no-code options for building data pipelines. The integration of generative AI features significantly lowers the technical skill barrier for pipeline development. More broadly, AI enables the autonomous optimization and automation of time-consuming tasks, freeing up valuable technical resources to focus on collaborating with business teams and driving business value (see Prepare for the Inevitable Rise of Self-Service Data Management).

Data integration tools are key to supporting AI initiatives: The growing demand to deliver AI projects has increasingly focused attention on data management. For AI projects to achieve their objectives, it is essential that they utilize AI-ready data — meaning that data must be representative of the use case, of every pattern, error, outlier and unexpected emergence needed to train or run the AI model for the specific use (see A Journey Guide to Deliver AI Success Through AI-Ready Data and Quick Answer: What Makes Data AI-Ready?). Modern data integration tools play a fundamental role in this process by providing capabilities for extracting information from multimodal sources, including parsing, preprocessing and contextualizing documents, images, audio and video. They also support the extraction, cataloguing and enrichment of metadata, as well as its organization into more consumable formats, such as knowledge graphs. Additionally, data integration tools can prepare data for streamlined consumption by AI systems through features such as semantic labeling and annotation, text chunking, vectorization, and the implementation of retrieval-augmented generation (RAG) workflows. Finally, many data integration tools also offer the ability to interact directly with LLMs, popular AI solutions, and agentic systems via dedicated connectors and communication interfaces (such as MCP, A2A or ACP). As a result, D&A leaders can rely on data integration tools as part of their strategy for implementing emerging AI use cases, including decision intelligence, chatbots, recommendation systems and the implementation of autonomous agents.

Ingin tahu siapa saja di Data Integration Tools

Magic Quadrant for Data Integration Tools

Strategic Planning Assumption

Market Definition/Description

Mandatory Features

Common Features

Magic Quadrant

Vendor Strengths and Cautions

Ab Initio

Amazon Web Services

Boomi

CData Software

Confluent

Denodo

Fivetran

Google

IBM

Informatica

K2view

Matillion

Microsoft

Oracle

Precisely

Qlik

Safe Software

SAP

SnapLogic

Workato

Vendors Added and Dropped

Added

Dropped

Inclusion and Exclusion Criteria

Inclusion Criteria

Exclusion Criteria

Honorable Mentions

Evaluation Criteria

Ability to Execute

Ability to Execute Evaluation Criteria

Completeness of Vision

Completeness of Vision Evaluation Criteria

Quadrant Descriptions

Leaders

Challengers

Visionaries

Niche Players

Context

Market Overview

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