Data Integration

Data integration has seen some significant development over the past few years. There is now a significant focus on ease of use; for instance, it is very much the standard for solutions in the space to offer low- or no-code, visual, and often automated data pipeline and/or transformation building. Natural language interfaces (typically AI-driven – see below) are also fairly common, to the same ends. Self-healing pipelines and automated schema detection and management are increasingly common and play into this aspect as well, creating more resilient data pipelines that require less user effort to maintain.

It is typical for data integration products to support a wide range of connectivity, in terms of both data sources and targets, with dozens – if not hundreds – of ready-made connectors being the norm. Cloud coverage is ubiquitous, as is support for unstructured data. Some degree of API support is also practically guaranteed, although to a greater or lesser degree depending on the specific solution. Most solutions offer you a choice of ETL or ELT (although they may bias towards one or the other), and reverse ETL, CDC, and streaming capabilities are all fairly widespread. It is also increasingly common for data integration solutions to incorporate data governance, profiling, and/or quality functionality into their processes, either natively or by integrating with third-party products.

One of the more recent and novel market trends for data integration (indeed, for data in general) is the adoption of generative AI. For example, products in the space will often provide capabilities to help you feed or otherwise incorporate an LLM (Large Language Model, the basis for a generative AI solution) into your data integration processes, or to enable RAG (Retrieval-Augmented Generation, a technique for adding business context to an otherwise generic LLM). It is also increasingly common for data integration vendors to directly offer generative AI-driven capabilities within their solutions. For example, as mentioned above, several offer chatbot-style interfaces that allow you to interact with them using natural language (to create data pipelines, say).

In terms of market movement, there have been two notable acquisitions within recent memory. First, Talend – formerly a data integration vendor – was acquired by Qlik in 2023, and has since been incorporated into its portfolio. Second, Actian acquired Zeenea, a data discovery platform, in 2024, adding said data discovery capabilities to its solution.

Historically, we have divided data integration vendors into platform and point solutions, referring to their broad scope or narrow focus, respectively. This is no longer entirely appropriate: rather than a relatively clean division into two categories, what we have now is a continuum of products of different breadths. While we still have massive platforms on the one hand, there are few genuinely “pure-play” data integration solutions left: rather, almost all of the products featured in this report provide capabilities substantially beyond data integration. API and application integration, document processing, data virtualisation, data warehouse automation, data quality, and data governance are just some of the extended capabilities offered by the included data integration suppliers. Accordingly, your choice of supplier may depend just as much, if not more, on what it provides in addition to data integration as it does on its core data integration capabilities.

It may be illustrative to focus in on one particular aspect: AI, and more specifically, generative AI. Like product breadth, this is a spectrum, with every vendor investing in it to some extent. That said, it is clear that some vendors have invested more than others: in no particular order, we have identified Actian, Astera, Gathr, Matillion, Striim, and Informatica as forerunners in the adoption of, and support for, generative AI. Accordingly, if this is an area that interests you, it would be wise to consider these solutions first and foremost.

On a final note, a handful of vendors that we contacted for this report either did not respond to our requests for information, or only provided us with very limited information. This includes Software AG, Qlik, Hevo Data, TIBCO, and – albeit to a lesser extent – Informatica. The latter excepting, we have been forced to exclude these vendors from this report due to a lack of information.

Data integration is a highly mature space, and it shows in its vendors: well-developed data integration capabilities are commonplace, with meaningful differentiation found primarily in secondary areas, such as data quality, data governance, and generative AI. The latter, in particular, has had almost as profound an impact on data integration as it has had on data in general, with several major suppliers taking pains to adopt and/or support it at a deep level, some even going so far as to rebrand their products to more prominently feature it. This is no surprise, as data integration as a space has long leveraged machine learning and other AI technologies to its benefit, and the hype surrounding generative AI, although it may be tapering off, has certainly not died down.

In summary, if you want to invest in data integration – and there are many reasons to do so, from analytics to cloud migrations to generative AI – you can rest assured that each of the vendors featured in this report offers a highly capable data integration solution. To decide which vendor to invest in specifically, our suggestion is to examine what each candidate can do, and what you would most benefit from, outside of just data integration.