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  • Professor Andrea M. Armani, University of Southern California
  • Ruti Ben-Shlomi, Ph.D., LightSolver
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  • Professor Xuchen Wang, Harbin Engineering University
  • Professor Stefan Witte, Delft University of Technology

Confluent and Databricks Partner to Usher In New Age of Real-Time AI

New partnership eliminates data silos for enterprises with Apache Kafka and Delta Lake

Confluent, Inc. (NASDAQ: CFLT), the data streaming pioneer, and Databricks, the Data and AI company, today announced a major expansion in their partnership that brings together Confluent’s complete Data Streaming Platform and Databricks’ Data Intelligence Platform to empower enterprises with real-time data for AI-driven decision-making. New integrations between Confluent's Tableflow and Databricks Unity Catalog will seamlessly govern data across operational and analytical systems, allowing businesses to build AI applications more efficiently.

Addressing the AI Data Challenge

Enterprises are rapidly building AI applications that require reliable, real-time data for better decisions and customer experiences. Yet, only 22 percent of enterprises are confident their current IT infrastructure is able to support these new AI applications. One of the major remaining hurdles they need to overcome is the divide between operational systems where data is generated and analytical systems where that data is turned into insights. Because these tools exist in separate silos, different teams, tools, and processes are applied between the two. The result is that teams are unable to marry real-time data with other systems in a meaningful way, and AI innovation on advanced use cases becomes impossible.

“For companies to maximize returns on their AI investments, they need their data, AI, analytics and governance all in one place,” said Ali Ghodsi, co-founder and CEO, Databricks. “As we help more organizations build data intelligence, trusted enterprise data sits at the center. We are excited that Confluent has embraced Unity Catalog and Delta Lake as its open governance and storage solutions of choice, and we look forward to working together to deliver long-term value for our customers.”

“Real-time data is the fuel for AI,” said Jay Kreps, co-founder and CEO, Confluent. “But too often, enterprises are held back by disconnected systems that fail to deliver the data they need, in the format they need, at the moment they need it. Together with Databricks, we’re ensuring businesses can harness the power of real-time data to build sophisticated AI-driven applications for their most critical use cases.”

Real-time Data, Ready for AI

To bridge the divide, Confluent and Databricks are announcing new integrations to ensure real-time interoperability and empower teams across the business to collaborate successfully. A bidirectional integration between Confluent’s Tableflow with Delta Lake and Databricks’ Unity Catalog, a unified and open governance solution for data and AI, will provide consistent, real-time data across operational and analytical systems that is discoverable, secure, and trustworthy.

Delta Lake, an open-format storage layer pioneered by Databricks, was originally developed for streaming use cases with fast writes. It has become the most adopted lakehouse format, proven out at a massive scale - processing over 10 exabytes of data daily. Now, Tableflow with Delta Lake makes operational data available immediately to Delta Lake’s rich ecosystem. Confluent and Databricks customers will be able to bring any engine or AI tool such as Apache Spark, Trino, Polars, DuckDB and Daft to their data in Unity Catalog.

Further, custom integrations between Tableflow and Databricks’ Unity Catalog will ensure metadata - a critical enabler for AI applications - is automatically applied to data exchanged between platforms. This makes operational data discoverable and actionable for data scientists and analysts working in Databricks while ensuring analytical data is equally accessible and useful for application developers and streaming engineers in Confluent. Additionally, Confluent’s Stream Governance suite will provide upstream governance and metadata to enhance fine-grained governance, end-to-end stream lineage, and automated data quality monitoring in Unity Catalog.

"Leveraging proximity to generation sources is a key factor not just in the energy sector, but also in the field of data,” said Dr. Dora Simroth, Head of Data and AI Engineering, E.ON Digital Technology. “Confluent and Databricks are already essential technologies in our Data and AI stack. These integrations will allow our practitioners to work on a single source of well-defined and timely data for both our operational and analytical plane. By partnering, Confluent and Databricks open up a faster path for us to build data and model-centric digital solutions.”

With these new capabilities, operational data from Confluent becomes a first-class citizen in Databricks, and Databricks data is easily accessible by any processor in the enterprise. The streaming data topics that AI applications consume and the tables that data analysts use will now have consistent views of the same real-time data, enabling faster, smarter AI-driven decision-making across the organization. This seamless integration between enterprise applications, analytics, and governance is critical for AI innovation at scale.

To learn more about the expanded partnership, read the blog from Databricks CEO Ali Ghodsi and Confluent CEO Jay Kreps: www.confluent.io/blog/confluent-and-databricks/

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 the 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.

About Databricks

Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on X, LinkedIn and Facebook.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. All statements other than statements of historical fact could be deemed forward-looking, including without limitation statements regarding expectations for the planned partnership between Databricks and Confluent, including anticipated benefits, features, and use cases for customers and AI applications. These forward-looking statements are subject to known and unknown risks, uncertainties, assumptions, and other factors that may cause actual results or outcomes to be materially different from any future results or outcomes expressed or implied by the forward-looking statements, including those described from time to time in Confluent’s filings with the Securities and Exchange Commission. Except as required by law, Confluent undertakes no obligation, and does not intend, to update these forward-looking statements.

Any unreleased services or features referenced in this announcement are not currently available and may not be made generally available on time or at all, as may be determined in Confluent’s and Databricks’ sole discretion. Any such referenced services or features do not represent promises to deliver, commitments, or obligations of either Confluent or Databricks and may not be incorporated into any contract. Customers should make their purchase decisions based upon services and features that are currently generally available.

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