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E-MapReduce:Overview

Last Updated:Oct 11, 2023

Flink (VVR) is a commercial version developed based on Apache Flink (referred to as Flink in this topic). Ververica Runtime (VVR) is fully compatible with Flink and provides high value-added features such as GeminiStateBackend to improve job performance and stability.

Background information

The core of Flink is a streaming execution engine. The engine provides features such as data distribution, data communication, and fault tolerance for distributed streaming computing. Based on the streaming execution engine, Flink provides APIs of a higher abstraction level to allow you to compile distributed jobs.

Flink (VVR) is fully compatible with Flink. For more information, see the following documentation:

Scenarios

Flink is widely used for real-time big data computing. This section describes the scenarios of Flink from the perspectives of technologies and enterprise applications.

  • Technologies

    From the perspective of technologies, Flink is suitable for the following scenarios:

    • Real-time extract, transform, and load (ETL) and data streams

      Data is delivered from Point A to Point B by using the real-time ETL process and data streams. During data delivery, data cleansing and integration may be required, such as real-time indexing in the search system and the ETL process in real-time data warehousing. Real-time ETL and data streams

    • Real-time data analysis

      Real-time data analysis is a process of extracting and integrating required information from raw data to achieve your business objectives. For example, you can view the top 10 products sold per day, the average turn-around time in the warehouse, the average document click rate, and the open rate for push notifications. Real-time data analysis allows you to view real-time reports or dashboards. Real-time data analysis

    • Event-driven applications

      An event-driven application is a system that processes or reacts to subscription events. Event-driven applications depend on internal states and respond to suspicious events detected during fraud detection or in the risk control system or O&M exception detection system. When your behavior triggers a risk control point, the system captures the event and analyzes the current and previous behavior to determine whether to perform risk control over your behavior. Event-driven

  • Enterprise applications

    From the perspective of enterprise applications, Flink is suitable for the following scenarios:

    • Business department: real-time risk control, real-time recommendation, and real-time indexing of search engines

    • Data department: real-time data warehousing, real-time reports, and real-time dashboards

    • O&M department: real-time monitoring, real-time exception detection and alerting, and end-to-end debugging