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Apache Flink is an open-source distributed processing engine for large-scale data streaming, batch processing, and event-driven applications. It was designed to process large amounts of data with low latency and high throughput, making it ideal for real-time data processing and analysis. https://hubs.ly/Q02nbT4v0

Apache Flink is an open-source distributed processing engine for large-scale data streaming, batch processing, and event-driven applications. It was designed to process large amounts of data with low latency and high throughput, making it ideal for real-time data processing and analysis. https://hubs.ly/Q02vwk-x0

Apache Flink is an open-source distributed processing engine for large-scale data streaming, batch processing, and event-driven applications. It was designed to process large amounts of data with low latency and high throughput, making it ideal for real-time data processing and analysis. https://hubs.ly/Q02sgbpC0

Apache Flink is an open-source distributed processing engine for large-scale data streaming, batch processing, and event-driven applications. It was designed to process large amounts of data with low latency and high throughput, making it ideal for real-time data processing and analysis. https://hubs.ly/Q02nbTjp0

Apache Flink is an open-source distributed processing engine for large-scale data streaming, batch processing, and event-driven applications. It was designed to process large amounts of data with low latency and high throughput, making it ideal for real-time data processing and analysis. https://hubs.ly/Q02vw4Mc0

Apache Flink is an open-source distributed processing engine for large-scale data streaming, batch processing, and event-driven applications. It was designed to process large amounts of data with low latency and high throughput, making it ideal for real-time data processing and analysis. https://hubs.ly/Q02yQZ9q0

🏁 Day - 91 Micro-batch versus true streaming: A long-running battle has been ongoing between micro-batch and true streaming approaches. Fundamentally, it’s important to understand your use case, the performance requirements, and the performance capabilities of the framework in question. Micro-batching is a way to take a batch-oriented framework and apply it in a streaming situation. A micro-batch might run anywhere from every two minutes to every second. Some micro-batch frameworks (e.g., Apache Spark Streaming) are designed for this use case and will perform well with appropriately allocated resources at a high batch frequency. True streaming systems (e.g., Beam and Flink) are designed to process one event at a time. However, this comes with significant overhead. Also, it’s important to note that even in these true streaming systems, many processes will still occur in batches. A basic enrichment process that adds data to individual events can deliver one event at a time with low latency. However, a triggered metric on windows may run every few seconds, every few minutes, etc. When you’re using windows and triggers, what’s the window frequency? What’s the acceptable latency? If you are collecting Black Friday sales metrics published every few minutes, micro-batches are probably just fine as long as you set an appropriate micro-batch frequency. On the other hand, if your ops team is computing metrics every second to detect DDoS attacks, true streaming may be in order. When should you use one over the other? Frankly, there is no universal answer. The term micro-batch has often been used to dismiss competing technologies, but it may work just fine for your use case and can be superior in many respects depending on your needs. If your team already has expertise in Spark, you will be able to spin up a Spark (micro-batch) streaming solution extremely fast. How it started: 👇 https://lnkd.in/gFtwbqkV #dataengineering #dataengineer #dataanalytics #datascience #datanerd

How can you improve the memory management of your JVM when doing stateful streaming? Look no further—RocksDB is here to the rescue. RocksDB is a high-performance, log-structured database engine written entirely in C++ and optimized for fast, low-latency storage. It helps in effective state management, reducing the number of out-of-memory exceptions. Please note that it is available only in Spark 3.2 and above. For more details, refer to the article below. https://lnkd.in/ggsPYEZj #dataengineer

Internally, by default, Spark Structured Streaming queries are processed using a micro-batch processing engine, which processes data streams as a series of small batch jobs thereby achieving end-to-end latencies as low as 100 milliseconds and exactly-once fault-tolerance guarantees.

Francine Anestis writes a series of articles about various Messaging and Streaming technologies. She examines the Key Features, Architecture, Use Cases, Strengths & Weaknesses, Cost and Maturity Level of each technology. In this second article she explores Apache Pulsar. https://hubs.ly/Q02CMz2m0