Apache Spark and Apache Flink are two of the most popular open-source frameworks for large-scale data processing. While both are designed to handle big data workloads, they have distinct architectures, processing models, and use cases. Here’s a comprehensive comparison to help you understand their differences: 1. Processing Paradigm 2. Latency and Throughput 3. Fault Tolerance
I’ve been working with Apache Flink for some time now, and I often find myself deciding between using the map or the flatMap operators. Recently, I encountered a scenario where choosing the right transformation became crucial for my data pipeline. Here’s a quick comparison I rely on when making that decision: • map - Purpose: Applies
Flink’s Kafka integration can be an excellent choice for building near real-time data pipelines. Offsets are at the center of these pipelines, governing exactly where Flink should begin reading data and how it responds to any missing or invalid positions. By combining Flink’s offset initializers with Kafka’s own offset resets, you can create robust and
Apache Flink is a powerful tool for processing data streams, but setting it up locally can sometimes feel like an uphill task. As someone who appreciates a smooth development workflow, I’ve found that using Docker simplifies the process immensely. In this post, I’ll walk you through setting up Flink locally using Docker Compose, troubleshooting potential