The Importance of Apache Flink in Processing Streaming Data

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Apache Flink is a data processing tool that can handle both batch data and streaming data, providing flexibility and versatility for users.

Streaming data processing is an emerging area. It means processing the data almost instantly (with very low latency) when it is generated. Until now, most data processing was based on batch systems, where processing, analysis and decision making were a delayed process. Now, as the new technologies and platforms are evolving, organizations are gradually shifting towards a stream-based approach rather than the old batch-based systems. Apache Flink is an open-source project for streaming data processing. It helps organizations to do real-time analysis and make timely decisions.

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What Is Apache Flink?

Apache Flink can be defined as an open-source platform capable of doing distributed stream and batch data processing. The core of Apache Flink is a streaming dataflow engine, which supports communication, distribution and fault tolerance for distributed stream data processing. Apache Flink is the only hybrid platform for supporting both batch and stream processing. It supports different use cases based on real-time processing, machine learning projects, batch processing, graph analysis and others.

Flink consists of the following components for creating real-life applications as well as supporting machine learning and graph processing capabilities:

  • DataSet API — Helps static data embedded in Python, Scala and Java
  • DataStream API — Helps unbounded streams in Python, Java and Scala
  • Table API — A SQL-like language, which can be used in Scala and Java

Apache Flink’s Basic Principles

Let us have a look at the basic principles on which Apache Flink is built:

  1. Consider everything as streams, including batches. So the stream is always there as the underlying concept and execution is done based on that.
  2. Write the application as the programming language and then do the execution as a database.
  3. Focus on the user-friendly features, like removal of manual tuning, removal of physical execution concepts, etc.
  4. Allow minimum configuration to implement the solution.
  5. Support different file systems and deployments.
  6. Integrate with legacy big data applications.
  7. Native support of batch, real-time stream, machine learning, graph processing, etc.

Why Apache Flink Is Different

Apache Flink is an open-source platform for stream and batch data processing. It has the following features which make it different compared to other similar platforms:

  • High performance and low latency — The runtime environment of Apache Flink provides high throughput and very low latency. This can be achieved by doing minimum configuration changes.
  • Custom state maintenance — Stream processing systems always maintain the state of its computation. Flink has a very efficient check pointing mechanism to enforce the state during computation.
  • Flow control — Flow control is an integral part of any stream processing system. Flink has a natural flow control system built in. It helps in efficient flow control with long-running operators.
  • Fault tolerance — Flink has an efficient fault tolerance mechanism based on distributed snapshots. This mechanism is very lightweight with strong consistency and high throughput.
  • Single runtime — Apache Flink provides a single runtime environment for both stream and batch processing. So the same implementation of the runtime system can cover all types of applications.
  • Efficient memory management — Apache Flink has its own memory management system inside JVM. So the application scalability is handled easily beyond main memory with less overhead.
  • Iterative computation — Flink provides built-in dedicated support for iterative computations like graph processing and machine learning.
  • Program optimization — Flink has a built-in optimizer which can automatically optimize complex operations.

Apache Flink also has two domain-specific libraries:

  • FlinkML — This is used for machine learning projects.
  • Gelly — This is used for graph processing projects.

Is Apache Flink Hadoop Dependent?

Real-time data analytics is done based on streaming data (which flows continuously as it generates). Apache Flink is a data processing system which is also an alternative to Hadoop’s MapReduce component. It has its own runtime and it can work independently of the Hadoop ecosystem. Flink can run without Hadoop installation, but it is capable of processing data stored in the Hadoop Distributed File System (HDFS). Flink has its built-in support libraries for HDFS, so most Hadoop users can use Flink along with HDFS. Flink can also access Hadoop’s next-generation resource manager, YARN (Yet Another Resource Negotiator). Flink also bundles Hadoop-supporting libraries by default. (To learn more about YARN, see What are the Advantages of the Hadoop 2.0 (YARN) Framework?)

So Apache Flink is a separate system altogether along with its own runtime, but it can also be integrated with Hadoop for data storage and stream processing.

An Alternative to Hadoop MapReduce

Apache Flink is considered an alternative to Hadoop MapReduce. Flink offers cyclic data, a flow which is missing in MapReduce. Flink offers APIs, which are easier to implement compared to MapReduce APIs. It supports in-memory processing, which is much faster. Flink is also capable of working with other file systems along with HDFS. Flink can analyze real-time stream data along with graph processing and using machine learning algorithms. It also extends the MapReduce model with new operators like join, cross and union. Flink offers lower latency, exactly one processing guarantee, and higher throughput. Flink is also considered as an alternative to Spark and Storm. (To learn more about Spark, see How Apache Spark Helps Rapid Application Development.)

What Are Apache Flink’s Tools?

Apache Flink has the following useful tools:

  • Command Line Interface (CLI) — This is a command line interface for operating Flink’s utilities directly from a command prompt.
  • Job Manager — This is a management interface to track jobs, status, failure, etc.
  • Job Client — This is basically a client interface to submit, execute, debug and inspect jobs.
  • Zeppelin — This is an interactive web-based computational platform along with visualization tools and analytics.
  • Interactive Scala Shell/REPL — This is used for interactive queries.

Fourth-Generation Big Data Analytics Platform

Apache Flink is known as a fourth-generation big data analytics framework. The first-generation analytics engine deals with the batch and MapReduce tasks. The second-generation engine manages batch and interactive processing. The third is a bit more advanced, as it deals with the existing processing along with near-real-time and iterative processing. Now comes the latest one, the fourth-generation framework, and it deals with real-time streaming and native iterative processing along with the existing processes.


Apache Flink is a new entrant in the stream processing analytics world. It is still an emerging platform and improving with new features. It will surely become even more efficient in coming years. Although it is compared with different functionalities of Hadoop and MapReduce models, it is actually a parallel platform for stream data processing with improved features. In time, it is sure to gain more acceptance in the analytics world and give better insights to the organizations using it.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…