The Apache Flink community introduced the Hybrid Shuffle Mode[1] in 1.16, which combines traditional Batch Shuffle with Pipelined Shuffle from stream processing to give Flink batch processing more powerful capabilities. The core idea of Hybrid Shuffle is to break scheduling constraints and decide whether downstream tasks need to be scheduled based on the availability of resources, while supporting in-memory data exchange without spilling to disk when conditions permit.
Stream processing is a programming paradigm which views data streams, or sequences of events in time, as the central input and output objects of computation. This enables organizations to harness the value of data immediately, making it a valuable tool for time-sensitive applications and scenarios requiring up-to-the-minute insights. Stream processing systems excel at handling high-velocity, unbounded data streams, such as click streams, log streams, live sensor data, social media feeds
PyFlink serves as a Python API for Apache Flink, providing users with a medium to develop Flink programs in Python and deploy them on a Flink cluster. In this post, we will introduce PyFlink from the following aspects: The structure of a fundamental PyFlink job and some basic knowledge surrounding it The operational mechanisms of PyFlink jobs, the high-level architecture, and its internal workings Essential performance optimization strategies for PyFlink Future projections for PyFlink
Imagine a photo without its vibrant colors; intriguing but lacking depth. Stream enrichment works similarly for data. It infuses raw data streams with added context, transforming them from grayscale to full color. Going beyond the simple transmission of information, stream enrichment breathes life into data, augmenting it with additional context and details. By embedding supplementary data into an existing data stream, businesses and organizations can paint a clearer picture, driving enhanced
Batch processingand stream processing are two very different models for processing data. Both have their strengths but suit different use cases. In this post we cover the differences, provide examples of use cases, and look at the ways the two models can work together.
A common requirement in the area of data engineering is to first process existing historical data before processing continuously live data. Processing existing data first is also referred to as bootstrapping the system. How to easily achieve this with Apache Flink? In this blog-post we will look at Flink's HybridSource which is specifically designed for such a task. If you want to clone the repository with the code from this blog post, use
Since its inception, Apache Flink has undergone significant evolution. Today, it not only serves as a unified engine for both batch and streaming data processing but also paves the way toward a new era of streaming data warehouses. Apache Flink has the concept of Dynamic Tables, which bear resemblance to materialized views in databases. However, unlike materialized views, Dynamic Tables are not directly queryable. Recognizing the need to support querying of intermediate tables
Every year, Apache Flink® sets new records in its development journey. Standing as a testament to its growing popularity, Flink now boosts over 1.6k contributors, 21k GitHub stars, and 1.4M downloads. In operational environments, Flink clusters are reaching impressive scales, with some individual clusters surpassing 2000 nodes. The largest known Flink infrastructure in production boasts over 4 million CPU cores, processing a staggering 4.1B events per second. If scalability is a concern
In October, at Flink Forward 2023, Streamhouse was officially introduced by Jing Ge, Head of Engineering at Ververica. In his keynote, Jing highlighted the need for Streamhouse, including how it sits as a layer between real-time stream processing and Lakehouse architectures, and discussed the business value it provides.
In this blog post, you will learn how to build a real-time data view on top of your Streamhouse using Apache Paimon table format. If you are coming from the Data Management world, you might know that Data engineers are generally concerned about implementing a data analytics pipeline, minimizing compute-infrastructure cost, and achieving the smallest end-to-end latency for the target users.
