Technical articles on stream processing, Apache Flink, architecture, and the internals that power real-time data platforms.
How a one-line connector swap silently poisoned a savepoint, why raising heap memory only bought time, and what it took to cut the stale state out.
From Kappa to Lakehouse and now Streamhouse, explore how each help address modern data challenges and unlock unified batch and stream processing systems.
Ververica[19]Explore the comparison of Apache Paimon, Iceberg, and Hudi in the context of the Streaming Lakehouse architecture. Learn about their features, use cases, and performance in data ingestion scenarios.
Learn about Streamhouse, a data processing pattern that combines real-time stream processing with the structured nature of data warehouses.
Discover Apache Paimon, the powerful streaming lakehouse that combines the flexibility of data lakes and the optimization of data warehouses.
Learn how to easily bootstrap a data pipeline using Apache Flink's HybridSource. This blog post provides a step-by-step guide and code examples.
Ververica[19]Batch processing and stream processing are two different models for processing data. This blog post explores their differences, provides use case examples
Ververica[19]Stream enrichment in Apache Flink breathes life into data, transforming it from grayscale to full color. Discover the three ways to access reference data.
Ververica[19]If you find yourself needing real-time computing solutions and you're comfortable with the Python or want to use some handy Python libraries in the process
Ververica[19]Discover the challenges and solutions in developing stream processing systems and how Ververica's Platform can simplify the process.
Learn how to optimize the performance of Hybrid Shuffle Mode with our comprehensive analysis and tuning guides.
Ververica[19]Learn how to handle data skews in stream joining for aggregation-related cases with Flink SQL. Discover potential solutions and how to implement them.
