Matillion

Matillion is an ELT and data integration platform for cloud data teams, with a low-code pipeline canvas, SQL pushdown and consumption-based billing in credits.

Matillion moves data out of business applications and operational databases and shapes it inside the warehouse where analysts query it. The platform is the Data Productivity Cloud from Matillion Limited, the company that also makes Maia and the older Matillion ETL. Each pipeline compiles to native SQL for its target, so transformation runs inside Snowflake, Databricks, Redshift or BigQuery instead of on a separate integration server. Loading starts from a library of hundreds of pre-built connectors, with Flex connectors and a no-code REST API builder for sources outside it. Transformation pipelines are drawn on a low-code canvas that also takes SQL and Python, and shared pipelines bundle into a single custom component for reuse. Streaming change data capture reads changes from databases such as MySQL, PostgreSQL, SQL Server, Oracle and Db2 for IBM i as they happen. Orchestration covers schedules as frequent as every minute, alerts by email, Slack or webhook, and an Activity dashboard of every run. Maia, the company's AI data team, builds, manages and orchestrates pipelines inside the Data Productivity Cloud. The platform suits data engineers and analytics teams who want pipelines built visually but still editable as code. Individual developers use it for early exploration and light workloads. Larger organizations come to it for Hybrid SaaS deployment, where the runner sits in their own AWS, Azure or Google Cloud account, along with data lineage and custom SSO. Teams that work under change control keep each project in Git branches, in the built-in repository or their own GitHub repo, and promote it through environments. Companies that must install the whole platform on their own servers are served by Matillion ETL instead. A typical project loads SaaS applications and databases into Snowflake or Databricks, then runs transformation pipelines on the landed tables before the next load arrives. Kafka and Cloud Pub/Sub topics feed the same pipelines when event data has to sit beside batch data, and SAP systems connect through the SAP ODP component. A data quality framework filters bad rows into an audit table before they reach reporting tables. Lineage leaves through an OpenLineage events API, and the platform works with catalogs such as Collibra, Alation and Unity Catalog and with dbt. The result is ingestion, SQL transformation and scheduling for a warehouse team held in one Git-versioned project.