Matillion Data Productivity Cloud

Matillion Data Productivity Cloud is the most recent offering from Matillion. It is a platform solution designed for building and managing data pipelines in the cloud in service of a variety of use cases, including analytics, AI, and so on. Moreover, Matillion has developed a number of (generative) AI capabilities for the product that should significantly add to its appeal.

Customer Quotes

“Matillion enables our team to provide meaningful data insights quickly. And, because it’s built for modern cloud data warehouses, we can use native Snowflake functionality to transform our data.”
Cisco

Like Matillion ETL, Data Productivity Cloud offers a graphical, drag-and-drop, low-code development user interface for architecting your data pipelines. These pipelines can include several stripes of data integration, including ETL, ELT, and Reverse ETL. CDC (Change Data Capture) and RAG (Retrieval-Augmented Generation) pipelines are also supported, the latter of which will be particularly relevant to anyone building a generative AI solution. Pipeline templates and prebuilt transformation components are available, as are a full orchestration layer for managing your pipelines and a library of ready-made connectors. The product also offers “flex” connectors that are available from Matillion on request but can be (re)configured to accommodate additional use cases down the line, and custom connectors that can be created in minutes using a wizard-driven UI. Connectors for unstructured data sources are not currently available but are expected to arrive soon. Git integration is provided. The end result of all of these features is a valiant (and largely successful) effort to offer self-service data pipeline creation and management.

Architecturally, Data Productivity Cloud must be deployed on top of a cloud data warehouse. Specifically, Snowflake (over either AWS or Azure), Databricks, or Redshift. BigQuery is not currently supported, though we are told it is on the roadmap. For use cases not covered by this selection, Matillion ETL is still very much available. Data Productivity Cloud can integrate with the ‘big 3’ cloud service providers (AWS, Azure, and GCP) as well as knowledge graphs, data catalogues, LLMs (Large Language Models) and vector stores. These latter two will, again, be particularly interesting if you want to implement generative AI. The platform can be deployed as either a fully-managed SaaS or hybrid-SaaS solution. In either case, the platform leverages stateless microservices containers as agents – one for each of your data pipelines – either within your network or a managed Matillion environment depending on your chosen deployment method. These agents can be spun up or down as needed, seamlessly, and on an individual basis, making for great scalability and minimal performance overheads. Other architectural features of note include data lineage (leveraging open-source lineage standards) and extensive use of pushdown (such as the recently-added Python Pushdown feature, which allows you to execute Python scripts directly within Snowflake using its Snowpark service).

As mentioned above, Data Productivity Cloud has recently incorporated several features designed to support generative AI. We have already touched on its support for RAG pipelines, as well as its connectivity with LLMs and vector stores. Pipeline components that allow you to submit a prompt to an LLM from within a data pipeline are also available, as is prompt engineering, and the product offers lineage for AI processes, which may prove very important in the coming months and years as AI-oriented governmental regulations continue to spring up across the globe. Moreover, the platform is starting to offer features that directly take advantage of generative AI. At present, this includes a natural language AI copilot (currently in preview) that can help you to create your data pipelines (among other things), and automatic documentation of data pipelines and pipeline components, including the generation of readable business summaries.

Data Productivity Cloud is an advancement over Matillion ETL in several ways, and thus carries over many of the advantages of that product while enhancing them and adding its own. Most obviously, it is purpose-built to sit on top of a cloud data warehouse. But it also makes significant advancements over its predecessor in terms of usability and functionality, architecture, and (most excitingly) AI.

For example, ETL offers a user-friendly interface that incorporates low-code, drag-and-drop techniques for building data pipelines and integration processes. Data Productivity Cloud retains this tried-and-true base while also adding such things as a greater range of available data integration processes, custom (and flex) connectors, and soon an AI-driven copilot.

Architecturally, while the product is currently missing some of the compatibility offered by ETL (BigQuery users only have access to the older product, for instance, at least for now) its heavily distributed deployment approach of using many agents, each matched to an individual data pipeline, has a lot going for it in terms of scalability and performance. It also stands in contrast to some of the older products on the market, which tend towards a single, monolithic, and therefore often inefficient agent.

Finally, in terms of AI Data Productivity Cloud is an obvious step up, providing several options capable of supporting generative AI and even a small handful of features that leverage generative AI themselves. That said, it is still early days when it comes to generative AI – for everyone, not just Matillion – so we expect more and better things to come in the future.

The Bottom Line

Data Productivity Cloud takes what was already good about Matillion ETL and transplants it to a platform that has been built from the ground up to accommodate the cloud data warehouse. At the same time, it adds new features and improves old ones, not least of which by incorporating generative AI. In short, we are very impressed.