Data Integration Software - Options 2026

Phil Turton
Jul 29
11 min read

Data Integration Software Options for 2026

Every organisation moving data between systems eventually hits the same wall: spreadsheets and point-to-point scripts do not scale, and the cost of unreliable data pipelines shows up everywhere from broken dashboards to failed AI initiatives. In 2026, the category has been reshaped by two of its biggest consolidation moves in years - Salesforce completing its acquisition of Informatica, and Fivetran and dbt Labs merging into a single data infrastructure company - both aimed squarely at making data trustworthy enough for AI agents to use directly.

This guide covers the leading data integration and ETL platforms available to enterprise, hyperscaler, and mid-market buyers in 2026, with independent commentary on where each fits. Viewpoint Analysis is a Technology Matchmaker, helping data and IT leaders find and select the right technology fast - aiming to be the place buyers go to understand the software and technology market before speaking to vendors.

Included Data Integration Software Vendors

This guide covers the following data integration platforms, evaluated independently across enterprise, hyperscaler, and cloud-native tiers. Our viewpoint on each vendor follows below.

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What is Data Integration and ETL Software?

Data integration and ETL (Extract, Transform, Load) software collects data from multiple systems, prepares it for analysis, standardises formats, and moves it into databases, cloud data warehouses, data lakes, and operational systems. Modern platforms increasingly combine ETL with ELT, streaming pipelines, orchestration, and workflow automation, and most now support reverse ETL and real-time event streaming as standard rather than premium features.

The category sits at the centre of the broader data stack, connecting operational applications, CRM and ERP systems, cloud warehouses, and analytics tools. For a wider view of the data technology landscape - covering governance, quality, master data management, and data lake platforms alongside integration - see the Viewpoint Analysis Data Technology page.

How to Find Data Integration Software

The data integration market spans everything from fully managed cloud ELT tools to deep enterprise platforms with decades of governance capability, which makes early-stage vendor discovery time-consuming to do alone. The free Longlist Builder at Viewpoint Analysis, powered by HUEY, the Viewpoint Analysis AI Technology Analysis Agent, generates a tailored longlist matched to your data architecture, cloud platform, and engineering maturity in minutes.

If your requirements are complex, or you would rather vendors came to you, the Technology Matchmaker Service works like Dragons' Den or Shark Tank for technology buyers. Viewpoint Analysis interviews your team, writes a Challenge Brief capturing your specific requirements, and invites the leading data integration vendors to pitch directly - giving you a credible shortlist fast, without the legwork of vendor outreach.

Enterprise Data Integration Software Options 2026

Informatica IDMC (Salesforce) Informatica Intelligent Data Management Cloud (IDMC) remains one of the most comprehensive data integration platforms on the market, covering ETL, ELT, data quality, governance, and metadata management in a single cloud suite. Following Salesforce's completed acquisition of Informatica in November 2025, the platform now sits within the Salesforce Data Cloud ecosystem, adding deep CRM and customer data context to its existing enterprise integration strength. IDMC is widely adopted by large and complex organisations managing hybrid or multi-cloud data architectures, and its AI-powered CLAIRE engine automates significant portions of pipeline design, monitoring, and data quality remediation. It remains a strong fit for organisations that need breadth across the full data management lifecycle rather than integration alone.

Our Viewpoint: A strong option for large organisations that want data integration, quality, and governance in one platform, particularly those already invested in or considering Salesforce for customer data.

Qlik (Talend and Qlik Data Integration) Qlik brings together two established integration product lines: Talend, acquired in 2023, providing ETL, data quality, and application integration, and Qlik Data Integration, formerly Attunity, which specialises in real-time data replication and change data capture for moving data into cloud warehouses and lakes with minimal latency. Together they give Qlik genuine depth across both batch ETL and real-time replication use cases, a combination few single vendors offer. The platform is used across large and mid-market organisations migrating from legacy on-premise databases to cloud data platforms, and its change data capture capability is well regarded for keeping downstream analytics current without heavy source-system load. Qlik's broader analytics suite gives organisations a natural path from integration through to reporting within a single vendor relationship.

Our Viewpoint: Worth evaluating for organisations that need both traditional ETL and real-time database replication, especially where migrating off legacy on-premise systems is a current priority.

IBM DataStage IBM DataStage is a long-established enterprise ETL platform used across large, complex environments that require highly controlled, auditable data pipelines at scale. It is available on IBM Cloud Pak for Data and integrates closely with IBM's broader data and AI portfolio, including watsonx, giving organisations a path to bring governed data pipelines directly into AI initiatives. DataStage's parallel processing engine suits high-volume batch workloads well, and many organisations continue to rely on it for mission-critical pipelines that predate more recent cloud-native tools. It remains a common choice in regulated industries such as financial services, insurance, and government, where governance and lineage requirements are exacting.

Our Viewpoint: A solid choice for large, regulated organisations that need proven, high-volume batch ETL with strong governance credentials and a path into the broader IBM data and AI stack.

Oracle Data Integrator Oracle Data Integrator (ODI) provides ELT-based data integration with tight native connectivity to Oracle databases, Oracle Fusion applications, and Oracle Cloud Infrastructure. Its ELT architecture pushes transformation processing down into the target database rather than a separate engine, which can deliver strong performance for organisations already running Oracle as their primary data platform. ODI is most compelling for organisations standardised on Oracle across their application and database estate, where native integration reduces the overhead of introducing a third-party tool. It also supports big data and cloud targets, giving Oracle-centric organisations a route to modern data architectures without leaving the Oracle ecosystem.

Our Viewpoint: The natural choice for Oracle-standardised organisations that want data integration built on the same platform as their applications and databases, minimising integration complexity.

SAP Datasphere SAP Datasphere is SAP's current data integration and federation platform, providing data cataloguing, modelling, and replication across SAP and non-SAP sources without always requiring physical data movement. It sits at the centre of SAP's newly consolidated Business Data Cloud strategy, which brings data federation, business context, and analytics together in a single architecture, with expanding native integrations to hyperscaler platforms including Databricks, Google BigQuery, and Microsoft Fabric arriving through 2026. For SAP customers, Datasphere offers a way to combine SAP data with external cloud data platforms while maintaining a single governed semantic layer, reducing the duplicate ETL pipelines many SAP shops have historically built to feed external analytics tools. It is best evaluated by organisations already running SAP S/4HANA or BW that want to extend their data estate into modern cloud analytics platforms.

Our Viewpoint: A strong option for SAP customers looking to connect their SAP data estate with external cloud data platforms while keeping data governance centralised within SAP's semantic layer.

Cloud Platform and Hyperscaler Data Integration Options 2026

Microsoft Azure Data Factory Microsoft Azure Data Factory is a cloud-native data integration service offering pipeline orchestration, ELT capability, and a broad connector library, tightly integrated with Azure Synapse Analytics and Microsoft Fabric. It is a natural choice for organisations standardising on Azure, offering a consistent experience across data movement, transformation, and analytics within a single cloud platform. Its visual pipeline designer and native integration with Microsoft's wider data and AI stack make it accessible to data engineering teams of varying experience levels, while still supporting complex enterprise-scale workloads. Increasing convergence with Microsoft Fabric is extending its role from pure data movement into a broader unified analytics experience.

Our Viewpoint: A strong fit for organisations already invested in Azure or Microsoft Fabric that want data integration built natively into their existing cloud data platform.

AWS Glue AWS Glue is a serverless data integration service offering ETL job authoring, workflow orchestration, and metadata cataloguing, with deep integration into the AWS data stack including S3, Redshift, Athena, and EMR. Its serverless architecture removes the need to provision or manage infrastructure, making it a popular choice for organisations building event-driven or highly variable data pipelines on AWS. Glue's data catalogue also plays a central role in AWS-based data lake architectures, providing a shared metadata layer that other AWS analytics services can query directly. It is most compelling for organisations that have standardised their data architecture on AWS and want integration that scales automatically with workload demand.

Our Viewpoint: Well suited to AWS-native organisations that want serverless, auto-scaling data integration tightly connected to the rest of their AWS data and analytics estate.

Google Cloud Dataflow and Data Fusion Google Cloud offers two complementary data integration services: Dataflow, a fully managed stream and batch processing engine built on Apache Beam, and Data Fusion, a visual, code-free integration tool aimed at teams that want faster pipeline development without deep engineering resource. Both integrate tightly with BigQuery, making them a natural fit for organisations building analytics and AI workloads on Google Cloud. Dataflow is particularly strong for large-scale, low-latency streaming use cases, while Data Fusion suits teams prioritising speed of delivery over custom pipeline logic. Together they give Google Cloud customers options across both engineering-led and lower-code integration approaches.

Our Viewpoint: A good fit for organisations building on Google Cloud and BigQuery, offering a choice between engineering-led streaming pipelines and faster, lower-code integration development.

Modern ELT and Cloud-Native Data Integration Options 2026

Fivetran + dbt Labs Fivetran is a fully managed ELT platform known for automated schema management and minimal pipeline maintenance overhead, and in June 2026 it completed an all-stock merger with dbt Labs, the company behind the widely used dbt transformation tool. The combined business now offers data movement and SQL-based transformation as a single connected platform, positioned around making data trustworthy enough for AI agents to use directly rather than purely serving human-built dashboards. dbt Core remains open source under the Apache licence following the merger, preserving the transformation layer's broad community adoption. For organisations that want managed data ingestion and governed transformation from one vendor relationship rather than stitching the two together themselves, the combined platform is a significant simplification.

Our Viewpoint: A strong option for data teams that want managed ingestion and SQL-based transformation from a single, tightly integrated platform, particularly those building toward AI agent use cases.

Matillion Matillion is a cloud-native ELT platform built specifically for Snowflake, BigQuery, Redshift, and Databricks, known for its visual pipeline builder and fast time to first pipeline. It appeals to data teams that want a low-code development experience without giving up the ability to build complex transformation logic when needed. Matillion's pricing and deployment model are geared toward cloud data warehouse-centric organisations rather than complex hybrid or on-premise estates, and it has continued to expand its AI-assisted pipeline development capabilities. It is a frequent shortlist candidate for growing data teams standardising on a modern cloud data warehouse.

Our Viewpoint: Well suited to data teams building primarily on Snowflake, BigQuery, Redshift, or Databricks who want fast, visual pipeline development without a steep learning curve.

Airbyte Airbyte is an open-source-first ELT platform with a rapidly growing connector catalogue, offering both a self-hosted open-source version and a managed cloud offering. Its open-source model has made it popular with data engineering teams that want the flexibility to build or modify connectors themselves rather than waiting on a vendor's roadmap, while the managed cloud version gives less technical teams a lower-maintenance option. Airbyte's connector development kit allows teams to build custom integrations relatively quickly, which has helped it build a broad long-tail connector catalogue beyond the systems that larger commercial platforms prioritise. It is commonly evaluated by organisations that want to avoid vendor lock-in or need connectivity to niche or internal systems.

Our Viewpoint: A strong choice for data engineering teams that want open-source flexibility, broad long-tail connector coverage, or the option to self-host their integration platform.

StreamSets (IBM) StreamSets is a data ingestion and pipeline monitoring platform built for hybrid, multi-cloud, and real-time data flows, and has been part of IBM since IBM completed its acquisition of StreamSets and webMethods from Software AG in July 2024. It specialises in building resilient, self-healing data pipelines that continue operating through schema drift and other structural changes in source systems, reducing the maintenance burden on data engineering teams. Since joining IBM, StreamSets has been positioned as the data ingestion layer feeding IBM's watsonx AI and data platform, giving organisations a path from raw data ingestion through to governed, AI-ready data. It is most relevant to organisations already using or considering the broader IBM data and AI portfolio.

Our Viewpoint: A good option for organisations that need resilient, drift-tolerant data pipelines across hybrid and multi-cloud environments, particularly those building on the IBM data and AI stack.

How to Select Data Integration Software

Start by being precise about your batch versus streaming requirements, and whether you need ETL, ELT, or both. Organisations with heavy real-time or event-driven use cases need genuine streaming capability, not a batch tool with a real-time label bolted on, while others are better served by simpler, scheduled ELT pipelines into a cloud warehouse.

Cloud and data warehouse alignment should shape your shortlist early. Platforms built natively for a specific cloud or warehouse - Azure Data Factory for Azure, AWS Glue for AWS, Matillion for Snowflake and Databricks - typically offer performance and integration advantages over generic tools retrofitted to work everywhere. Confirm which platform your data estate is actually standardising on before evaluating vendors.

Decide early whether you want a fully managed platform, an open-source foundation you control, or an enterprise suite with deep governance built in. Managed ELT tools like Fivetran and Matillion minimise engineering overhead; open-source platforms like Airbyte offer more control and flexibility at the cost of more hands-on maintenance; enterprise platforms like Informatica and IBM DataStage trade simplicity for depth in governance, lineage, and compliance.

Finally, assess how each platform prepares data for AI use, not just human-built dashboards. The Fivetran and dbt Labs merger and the continued build-out of governance layers across SAP Datasphere and Informatica IDMC both signal that AI-readiness - trusted, well-modelled, lineage-tracked data - is becoming a genuine differentiator, not a marketing label.

For a structured approach to running your evaluation, Technology Selection Services from Viewpoint Analysis cover Rapid RFI, Rapid RFP, and 30-Day Technology Selection. For a comprehensive guide to the full selection process, the Enterprise Software Selection Playbook 2026 covers methodology, vendor engagement, and decision governance in full.

If you need some help, check out the Viewpoint Analysis IT Buyer Help Services - everything from ad-hoc guidance to shortlist confirmation, and through to full selection process support.

Summary

Data integration software has moved well beyond simple ETL scheduling. In 2026, the market is being reshaped by consolidation at both ends of the spectrum - Salesforce's acquisition of Informatica, and the Fivetran and dbt Labs merger - alongside continued platform convergence at SAP, Microsoft, and IBM, all pointing toward the same goal: data that is trustworthy and well-governed enough for AI agents to use directly, not just for human-built dashboards.

Three takeaways are worth holding as you evaluate the market. First, cloud and warehouse alignment matters more than category labels - the best platform for your organisation is usually the one built closest to where your data already lives, not the one with the longest feature list. Second, the managed-versus-open-source decision is a genuine strategic choice, not just a budget line - it determines how much control your data engineering team retains over connectors and pipeline logic long term. Third, AI-readiness is becoming a real evaluation criterion - ask vendors specifically how their platform tracks lineage and governs data quality for AI consumption, not only for reporting.

Data Integration Buyer Help - Next Action

Viewpoint Analysis works with enterprise and mid-market organisations to find and select the right data integration software - independently, without vendor fees or influence.

If you are just starting out and want to understand what is in the market, the Longlist Builder is free, takes minutes, and gives you a tailored list of data integration platforms matched to your architecture and cloud strategy.
If you want vendors to come to you rather than the other way around, the Technology Matchmaker Service identifies the platforms most relevant to your situation and brings them directly to you to pitch.
If you are ready to run a structured selection and want to move quickly, our Technology Selection Services cover the full process, from Rapid RFI through Rapid RFP to a combined 30-Day Technology Selection.
If you already have a shortlist and want an independent view before committing, the Purchase Assurance package gives you an independent assessment of whether your preferred platform is the right choice before you sign a contract.

Talk to Viewpoint Analysis

If you are currently evaluating data integration or ETL software and would like an independent view on your options, we would be delighted to help - request a call here. If you are a vendor in this space and would like to be considered for future content and matchmaking opportunities, we would love to hear from you via the same page.