Confluent Announces the General Availability of Tableflow

Rhea-AI Summary

Confluent (NASDAQ:CFLT) has announced major updates to Tableflow, their solution for accessing operational data from data lakes and warehouses. The platform now offers general availability of Apache Iceberg™ support and launched an early access program for Delta Lake through an expanded Databricks partnership.

Key enhancements include:

• Bring Your Own Storage flexibility for storing Iceberg or Delta tables
• Direct integrations with AWS Glue Data Catalog and Snowflake Open Catalog
• Seamless catalog management for analytical engines like Amazon Athena, EMR, and RedShift

Tableflow addresses a critical market need, as IDC predicts that by 2027, 70% of IT teams will refocus on AI-ready data infrastructure platforms after experiencing AI project failures. The platform aims to solve the challenge of disconnected data silos that impede effective AI model development by providing real-time business context to analytical systems.

Positive

• General availability of Apache Iceberg support expands enterprise-ready capabilities
• Strategic partnership with Databricks for Delta Lake integration
• Enhanced flexibility through Bring Your Own Storage feature
• Expanded ecosystem integrations with major analytical platforms

Negative

• Delta Lake support still in early access program, not yet generally available

Insights

Data Infrastructure Specialist

Confluent's general availability of Tableflow represents a significant strategic advancement in bridging the operational-analytical divide that plagues enterprise data systems. By enabling Kafka topics to be represented as Apache Iceberg tables (now GA) and Delta Lake tables (early access), Confluent is solving a fundamental architecture challenge that has real-time AI applications.

The technical significance here shouldn't be understated - most enterprises struggle with disconnected data pipelines where operational data exists in isolated systems from analytical environments. This creates significant latency and consistency problems. Tableflow eliminates expensive and error-prone table maintenance tasks like compaction while providing continuous synchronization between operational and analytical systems.

The expanded partnership with Databricks (powering Delta Lake integration) and catalog provider integrations with AWS Glue and Snowflake Open Catalog strategically positions Confluent as a central hub in the modern data stack. By handling governance and processing upstream as data is generated, Tableflow addresses the "garbage in, garbage out" problem that undermines many AI initiatives.

This product evolution significantly strengthens Confluent's competitive moat, extending beyond Kafka message queuing to becoming essential infrastructure for real-time AI applications. The customer example from Busie demonstrates the real-world application for seamless consumption of operational data in analytical formats without preprocessing overhead.

Enterprise AI Strategist

This announcement directly addresses what's becoming known as the "AI reality gap" - the disconnect between ambitious AI projects and the infrastructure needed to support them. IDC's prediction that 70% of IT teams will refocus on AI-ready data infrastructure after multiple AI project failures highlights the market opportunity Confluent is targeting.

Tableflow solves a critical AI implementation challenge: ensuring models have access to current business context rather than stale data. Consider inventory management AI - without real-time data synchronization between operational systems and analytical environments, AI recommendations become inaccurate and potentially costly.

The technical approach is compelling - rather than building yet another data pipeline that copies information between systems (creating more silos), Confluent is leveraging its streaming architecture to maintain synchronized views across environments. This significantly reduces complexity for data engineering teams while improving data freshness.

The integration ecosystem strategy is particularly noteworthy. By supporting both Apache Iceberg and Delta Lake formats, plus integrations with AWS and Snowflake catalog services, Confluent is positioning itself as vendor-agnostic infrastructure that works with customers' existing investments. The "Bring Your Own Storage" capability addresses enterprise concerns about data residency and compliance requirements.

This product evolution represents Confluent's strategic shift toward becoming essential infrastructure for AI-driven enterprises rather than just a messaging system, substantially expanding their addressable market.

Tableflow brings real-time business context to analytical systems to make AI and next-generation applications enterprise-ready

BENGALURU, India--(BUSINESS WIRE)-- Confluent, Inc. (NASDAQ:CFLT), the data streaming pioneer, today announced significant advancements in Tableflow, the easiest way to access operational data from data lakes and warehouses. With Tableflow, all streaming data in Confluent Cloud can be accessed in popular open table formats, unlocking limitless possibilities for advanced analytics, real-time artificial intelligence (AI), and next-generation applications. Support for Apache Iceberg™ is now generally available (GA). And as a result of an expanded partnership with Databricks, a new early access program for Delta Lake is now open. Additionally, Tableflow now offers enhanced data storage flexibility and seamless integrations with leading catalog providers, including AWS Glue Data Catalog and Snowflake’s managed service for Apache Polaris™, Snowflake Open Catalog.

“At Confluent, we’re all about making your data work for you, whenever you need it and in whatever format is required,” said Shaun Clowes, Chief Product Officer at Confluent. “With Tableflow, we’re bringing our expertise of connecting operational data to the analytical world. Now, data scientists and data engineers have access to a single, real-time source of truth across the enterprise, making it possible to build and scale the next generation of AI-driven applications.”

The Downfall of AI Projects: Disconnected Data

“By 2027, after suffering multiple AI project failures, 70% of IT teams will return to basics and focus on AI-ready data infrastructure platforms,” claimed IDC in the IDC FutureScape: Worldwide Digital Infrastructure 2025 Predictions. The report added, “Many IT organizations rely on scores of data silos and a dozen or more different copies of data. These silos and redundant data stores can be a major impediment to effective AI model development.”

AI projects are failing because old development methods cannot keep pace with new consumer expectations. These applications are expected to know the current status of a business and its customers and take action automatically. For example, an AI agent for inventory management should be able to identify if a particular item is trending, immediately notify manufacturers of the increased demand, and provide an accurate delivery estimate for customers. This level of hands-free efficiency is simply not possible if business data is not getting to the analytics and AI systems in real time. The old ways of batch processing lead to inaccurate results, and manually copying over data is unstable, not scalable, and proliferates the silo problem.

Bridging the Data Gap for Enterprise-Ready AI

“Transportation companies love us because we make sales, operations, and dispatching super simple, but that’s only possible with real-time data analytics,” said Brady Perry, Co-founder at Busie. “Tableflow offers a promising path for our analytics engine to seamlessly consume operational data in Kafka in real time as Apache Iceberg tables, eliminating the need for additional preprocessing. By integrating Tableflow with Snowflake, we could prevent raw, unclean data from being ingested while reducing complexity and storage costs. This approach would simplify workflows and accelerate time to insight while ensuring a more efficient and cost-effective data architecture.”

Tableflow simplifies the integration between operational data and analytical systems. It continuously updates tables used for analytics and AI with the exact same data from business applications connected to Confluent Cloud. Within Confluent, processing and governance happen as data is generated, shifting these tasks upstream to ensure that only high-quality, consistent data is used to feed data lakes and warehouses. This is a breakthrough for AI, as it’s only as powerful as the data that shapes it.

Today, Confluent announces significant updates to Tableflow:

• Support for Apache Iceberg^TM is ready for production workloads. Teams can now instantly represent Apache Kafka^® topics as Iceberg tables to feed any data warehouse, data lake, or analytics engine for real-time or batch processing use cases. Expensive and error-prone table maintenance tasks, such as compaction, are automatically handled by Tableflow, giving time back to data engineers to deliver more business value. It also provides a single source of truth for one of the most widely adopted open-format storage options, enabling data scientists and data engineers to scale AI innovation and next-generation applications.
• New Early Access Program for Delta Lake is now open. This open-format storage layer, pioneered by Databricks, processes more than 10 exabytes of data daily and is used alongside many popular AI engines and tools. With this integration, customers will have a consistent view of real-time data across operational and analytic applications, enabling faster, smarter AI-driven decision-making. Apply for the Tableflow Early Access Program here.
• Increase flexibility through Bring Your Own Storage. Store fresh, up-to-date Iceberg or Delta tables once and reuse them many times with the freedom to choose a storage bucket. Customers now have full control over storage and compliance to meet their unique data ownership needs.
• Enhance data accessibility and governance with partners. Direct integrations with Amazon SageMaker Lakehouse via AWS Glue Data Catalog (GA) and Snowflake Open Catalog (GA) enable seamless catalog management for Tableflow’s Iceberg tables. They also streamline access for analytical engines such as Amazon Athena, Amazon EMR, and Amazon RedShift, and leading data lake and warehouse solutions including Snowflake, Dremio, Imply, Onehouse, and Starburst. Additionally, Confluent has strengthened enterprise adoption for Tableflow with support from global and regional system integrators, including GoodLabs Studio, Onibex, Psyncopate, and Tata Consultancy Services (TCS).

These announcements will be spotlighted on the main stage of Current Bengaluru on March 19 at 10:00 a.m. Indian Standard Time. Register here to attend virtually.

Additional Resources

• Read the Tableflow GA product blog for a full rundown of what’s new.
• Start your free trial of Confluent Cloud. No credit card required.
• Ask our Professional Services experts for faster adoption.

About Confluent

Confluent is the data streaming platform that is pioneering a fundamentally new category of data infrastructure that sets data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion—designed to be the intelligent connective tissue enabling real-time data from multiple sources to constantly stream across an organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven back-end operations. To learn more, please visit www.confluent.io.

As our road map may change in the future, the features referred to here may change, may not be delivered on time, or may not be delivered at all. This information is not a commitment to deliver any functionality, and customers should make their purchasing decisions based on features that are currently available.

Confluent® and associated marks are trademarks or registered trademarks of Confluent, Inc.

Apache^®, Apache Kafka^®, Kafka^®, Apache Flink^®, Flink^®, Apache Iceberg™, Iceberg™, Apache Polaris™, and Polaris™ are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by the Apache Software Foundation is implied by the use of these marks. All other trademarks are the property of their respective owners.

View source version on businesswire.com: https://www.businesswire.com/news/home/20250318342911/en/

Media Contact:

Lyn Eyad

pr@confluent.io

Source: Confluent, Inc.

FAQ

What are the key features of Confluent's Tableflow general availability release?

Tableflow now offers GA support for Apache Iceberg, Delta Lake early access, Bring Your Own Storage flexibility, and integrations with AWS Glue Data Catalog and Snowflake Open Catalog.

How does Tableflow address the AI data infrastructure challenge for CFLT?

Tableflow bridges operational and analytical data by continuously updating tables with real-time business data, ensuring high-quality, consistent data for AI applications.

What storage options does Confluent's Tableflow support?

Tableflow supports Apache Iceberg tables (GA) and Delta Lake (early access), with flexible storage options through Bring Your Own Storage functionality.

Which major analytical platforms are compatible with Confluent's Tableflow?

Tableflow integrates with Amazon Athena, EMR, RedShift, Snowflake, Dremio, Imply, Onehouse, and Starburst.