Confluent, the data streaming pioneer, announced the General Availability (GA) of Delta Lake and Databricks Unity Catalog integrations in Confluent Tableflow, along with Early Access (EA) availability on Microsoft OneLake. These advancements make Tableflow a fully managed, end-to-end solution that connects operational, analytical, and artificial intelligence (AI) systems across hybrid and multicloud environments. Confluent now gets Apache Kafka® topics directly into Delta Lake or Apache IcebergTM tables with automated quality controls, catalog synchronization, and enterprise-grade security.
Since its debut, Tableflow has transformed how organizations make streaming data analytics-ready. It eliminates brittle ETL jobs and manual Lakehouse integrations that slow teams down. With Delta Lake and Unity Catalog integrations now in GA and Tableflow support for OneLake, Confluent is expanding its multicloud footprint. These updates deliver a unified solution that connects real-time and analytical data with enterprise governance. Now, it’s easier to build real-time AI and analytics that propel businesses ahead of their competition.
“Customers want to do more with their real-time data, but the friction between streaming and analytics has always slowed them down,” said Mr. Shaun Clowes, Chief Product Officer at Confluent. “With Tableflow, we’re closing that gap and making it easy to connect Kafka directly to governed lakehouses. That means high-quality data ready for analytics and AI the moment it’s created.”
Production-Ready for Enterprise Scale
The GA release introduces new enterprise-grade features that make Tableflow one of the most complete, reliable, and secure stream-to-table solutions available today, enabling organizations to:
- Simplify analytics. Delta Lake support (GA) converts Kafka topics directly into Delta Lake tables stored in cloud object storage (Amazon S3 or Azure Data Lake Storage). Delta Lake and Iceberg formats can now be enabled simultaneously per topic for flexible, cross-format analytics.
- Unify governance. Unity Catalog support (GA) automatically synchronizes metadata, schema, and access policies between Tableflow and Databricks Unity Catalog for centralized governance and consistent data management.
- Improve reliability. Dead Letter Queue captures and isolates malformed records without interrupting data flow. This schema-backed error handling provides transparency, faster recovery, and continuous data quality.
- Save time and cut complexity. Upsert functionality automatically updates and inserts records as data changes, keeping Delta Lake and Iceberg tables consistent, deduplicated, and always analytics-ready without manual maintenance.
- Strengthen security. Bring Your Own Keyextends customer-managed encryption keys to Tableflow for full control of data at rest. This enables compliance for regulated industries like financial services, healthcare, and the public sector.
Building on existing capabilities such as schema evolution, compaction, and automated table maintenance and integrations with Apache Iceberg, AWS Glue, and Snowflake Open Catalog, Tableflow now offers an end-to-end foundation for teams that need their streaming data to be instantly analytics-ready, governed, and resilient.
“At Attune, delivering real-time insights from smart building Internet of Things (IoT) data is central to our mission,” said Mr. David Kinney, Principal Solutions Architect at Attune. “With just a few clicks, Confluent Tableflow lets us materialize key Kafka topics into trusted, analytics-ready tables, giving us accurate visibility into customer engagement and device behavior. These high-quality datasets now power analytics, machine learning (ML) models, and generative AI applications, all built on a reliable data foundation. Tableflow has simplified our data architecture while opening new possibilities for how we leverage data.”
Now Available on Microsoft OneLake
Tableflow is also now available in EA on Azure integrated with OneLake, expanding its footprint and giving customers greater flexibility across multicloud deployments. This is especially impactful for organizations using Azure Databricks and Microsoft Fabric, where Delta Lake and Unity Catalog integrations are now fully supported. Together, they offer a seamless, governed analytics experience from real-time streams to cloud lakehouses. Now customers can:
- Accelerate time to insight. Instantly materialize Kafka topics as open tables in Microsoft OneLake and query them from Microsoft Fabric or your choice of tool using the OneLake Table APIs—no manual ETL or schema wrangling required.
- Eliminate complexity and operational costs. Automate schema mapping, type conversion, and table maintenance for your streaming data, enabling governance and reliability in Azure-native analytics workflows.
- Enable Azure AI and analytics services. Seamlessly integrate with Azure’s analytics and AI services using Microsoft OneLake Table APIs to power real-time insights and AI use cases and manage deployments easily via Confluent Cloud UI, CLI, or Terraform.
This EA release marks a major milestone in extending Tableflow’s multicloud footprint and strengthening Confluent’s partnership with Microsoft and Databricks.
“Access to real-time data is critical for customers to make fast and accurate decisions,” said Ms. Dipti Borkar, Vice President and GM of Microsoft OneLake and ISV Ecosystem. “With Confluent’s Tableflow now available on Microsoft Azure, customers can stream Kafka events to OneLake as Apache Iceberg or Delta Lake tables and query them instantly via Microsoft Fabric and popular 3rd party engines using OneLake Table APIs, cutting complexity and speeding up decisions.”
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