Confluent Unveils Tableflow to Unite Analytics and Operations with Data Streaming

Confluent, Inc. (NASDAQ: CFLT), the data streaming pioneer, announced exciting new Confluent Cloud capabilities making it easier for customers to stream, connect, govern, and process data for more seamless experiences and timely insights while keeping their data safe. Confluent Tableflow easily transforms Apache Kafka topics and the associated schemas to Apache Iceberg tables with a single click to better supply data lakes and data warehouses. Confluent’s fully managed connectors have been enhanced with new secure networking paths and up to 50 percent lower throughput costs to enable more complete, safe, and cost-effective integrations. Stream Governance is now enabled by default across all regions with an improved SLA available for Schema Registry, making it easier to safely adjust and share data streams wherever they’re being used.

For companies to make decisions that optimize costs, boost revenue, and drive innovation, it requires connecting the operational and analytical estates of data, which are traditionally siloed in organizations. The operational estate includes the SaaS applications, custom apps, and databases that power businesses such as Oracle, Salesforce, and ServiceNow. The analytical estate includes data warehouses, data lakes, and analytics engines that power analytics and decision-making and use data streams and historical tables to run queries and different analytical functions.

"The critical problem for modern companies is that operational and analytical estates must be highly connected, but are often built on point-to-point connections across dozens of tools,” said Shaun Clowes, Chief Product Officer at Confluent. "Businesses are left with a spaghetti mess of data that is painful to navigate and starves the business of real-time insights."

Many organizations turn to Kafka as the standard for data streaming in the operational estate, and to Iceberg as the standard open table format for data sets in the analytical estate. Using Iceberg, companies can share data across teams and platforms while keeping tables updated as the data itself evolves.

Companies using Kafka want to utilize Iceberg to meet the rising demand for both streaming and batch-based analytics. As a result, many companies must execute complex migrations which can be resource-intensive, resulting in stale and untrustworthy data and increased costs.

“Open standards such as Apache Kafka and Apache Iceberg are popular choices for streaming data and managing data in tables for analytics engines," said Stewart Bond, Vice President of Data Intelligence and Integration Software at IDC. "However, there are still challenges for integrating real-time data across operational databases and analytics engines. Organizations should look for a solution that unifies the operational and analytical divide and manages the complexity of migrations, data formats, and schemas."