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Apache Kafka
Data Products, Data Contracts, and Change Data Capture
Change data capture is a popular method to connect database tables to data streams, but it comes with drawbacks. The next evolution of the CDC pattern, first-class data products, provide resilient pipelines that support both real-time and batch processing while isolating upstream systems...
Unlock Cost Savings with Freight Clusters–Now in General Availability
Confluent Cloud Freight clusters are now Generally Available on AWS. In this blog, learn how Freight clusters can save you up to 90% at GBps+ scale.
Unleash Real-Time Agentic AI: Introducing Streaming Agents on Confluent Cloud
Build event-driven agents on Apache Flink® with Streaming Agents on Confluent Cloud—fresh context, MCP tool calling, real-time embeddings, and enterprise governance.
Scaling Kafka Streams Applications: Strategies for High-Volume Traffic
Learn how to scale Kafka Streams applications to handle massive throughput with partitioning, scaling strategies, tuning, and monitoring.
Cross-Data-Center Apache Kafka® Replication: Decision Framework & Readiness Playbook
Learn how to choose the right Apache Kafka® multi-cluster replication pattern and run an audit-ready disaster recovery and high availability program with lag SLOs, drills, and drift control.
How to Transform Data and Manage Schemas in Kafka Connect Pipelines
Learn how to handle data transformation, schema evolution, and security in Kafka Connect with best practices for consistency, enrichment, and format conversions.
Best Practices for Validating Apache Kafka® Disaster Recovery and High Availability
Learn best practices for validating your Apache Kafka® disaster recovery and high availability strategies, using techniques like chaos testing, monitoring, and documented recovery playbooks.
How to Scale and Secure Kafka Connect in Production Environments
Learn best practices for running Kafka Connect in production—covering scaling, security, error handling, and monitoring to build resilient data integration pipelines.
Introducing Apache Kafka® 4.1.0: What’s New and How to Upgrade
Announcing the release of Apache Kafka 4.1
Solving ETL Challenges with Apache Kafka®, Confluent Tableflow, and Zero ETL
Confluent Tableflow unifies operational and analytical data by integrating Kafka with zero ETL, leveraging open table formats such as Iceberg and Delta Lake. It offers advantages over traditional zero ETL by enabling data reuse, schema decoupling, and better scalability, streamlining data sharing.
Introducing KIP-848: The Next Generation of the Consumer Rebalance Protocol
Big news! KIP-848, the next-gen Consumer Rebalance Protocol, is now available in Confluent Cloud! This is a major upgrade for your Kafka clusters, offering faster rebalances and improved stability. Our new blog post dives deep into how KIP-848 functions, making it easy to understand the benefits.
How to Query Apache Kafka® Topics With Natural Language
The users who need access to data stored in Apache Kafka® topics aren’t always experts in technologies like Apache Flink® SQL. This blog shows how users can use natural language processing to have their plain-language questions translated into Flink queries with Confluent Cloud.
The Future of AI Agents is Event-Driven
For AI agents to transform enterprises with autonomous problem-solving, adaptive workflows, and scalability, they need event-driven architecture (EDA) powered by streaming data.
Building Streaming Data Pipelines, Part 1: Data Exploration With Tableflow
This blog post demonstrates using Tableflow to easily transform Kafka topics into queryable Iceberg tables. It uses UK Environment Agency sensor data as a data source, and shows how to use Tableflow with standard SQL to explore and understand the data.
Guide to Consumer Offsets: Manual Control, Challenges, and the Innovations of KIP-1094
The guide covers Kafka consumer offsets, the challenges with manual control, and the improvements introduced by KIP-1094. Key enhancements include tracking the next offset and leader epoch accurately. This ensures consistent data processing, better reliability, and performance.
Powering AI Agents with Real-Time Data Using Anthropic’s MCP and Confluent
Model Context Protocol (MCP), introduced by Anthropic, is a new standard that simplifies AI integrations by providing a secure and consistent way to connect AI agents with external tools and data sources…
