
Explore real-time data streaming with Apache Kafka and Swim. Learn about stateful business logic, stream processing, and creating dynamic UIs for actionable insights from distributed data sources.

Explore Apache Kafka's evolution, community impact, and future developments. Gain insights from industry leaders on Kafka's applications and upcoming features like KRaft.

Explore building a real-time AI data platform using Apache Kafka, focusing on migrating from batch to stream processing without stateful components. Learn about Forecasty.ai's journey and innovative solutions.

Discover essential recommendations for optimizing Apache Kafka in production environments, covering operational aspects, serialization, architecture, and advanced features for efficient data streaming.

Explore event-driven architecture with Galapagos, an open-source tool for Apache Kafka. Learn how to decouple teams, improve data management, and enhance business communication through event-driven thinking.

Explore tiered storage in Apache Kafka for improved scalability and elasticity. Learn implementation, benefits, and impact on performance from a Confluent engineer.

Learn to build a successful data engineering team and culture, empowering engineers and partners for efficient data systems and fruitful projects. Explore critical roles and strategies for data-driven decision-making success.

Explore Netflix's innovative approach to automating asset management workflows using rule-based systems, enhancing efficiency and scalability.

Explore OpenMessaging Benchmark to measure and compare performance of streaming systems, enhancing data infrastructure decisions for real-time applications.

Implement Event-Driven Architecture effectively with a Center of Excellence. Learn from real-life experiences in large industries to set up strong foundations, define designs, and guide development teams.

Explore digital signal processing with Kafka Streams, transforming time-domain signals to frequency domain using FFT. Learn to build a precise frequency alert system and visualize results in real-time.

Explore scaling strategies for Kafka, including horizontal and vertical scaling, replication techniques, and routing events between clusters. Learn to overcome bottlenecks and manage large-scale data processing effectively.

Optimize Kafka Connect clusters for scalability. Learn best practices to configure and manage Kafka Connect effectively, ensuring successful integration of external systems with Apache Kafka at scale.

Learn to create powerful data pipelines by combining Apache Kafka and InfluxDB for real-time processing, analysis, and visualization of large-scale data, especially in IoT applications.

Explore Lyft's real-time optimization platform evolution, focusing on streaming infrastructure for marketplace balance, scalability, and decision-making using Kafka and data science tools.

Explore a novel system for microservices that handles state, communication, and consistency. Learn how it complements event-processing systems for complex control flow and coordination in distributed applications.

Explore NASA's use of Kafka for real-time astronomy data sharing, including architectural choices and OAuth2 implementation for public access in the General Coordinates Network.

Explore innovative incremental snapshot techniques for Debezium CDC connectors. Learn to pause, resume, and parallelize snapshots, and customize snapshot contents for efficient data pipeline management.

Discover an automated system for detecting and remediating degraded storage in Kafka, improving cluster performance and availability. Learn about monitoring, algorithm formulation, and mitigation strategies.

Explore multidimensional time-state analytics for complex, large-scale metrics tracking. Learn about architectural and algorithmic solutions to improve performance, reduce costs, and simplify development.

Explore challenges and improvements in Exactly-Once Semantics for distributed transactions between Apache Flink and Kafka. Learn about proposed protocol changes and enhanced integration for robust data processing.