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Businesses need data experts who can build secure systems to process real-time information from multiple sources. Apache Flink powers this infrastructure. Learn Flink with online courses delivered through edX.

What is Flink?

Apache Flink is an open-source framework for large-scale stream processing that can write, read, and consume data from processing systems. All data can be seen as a stream of events, such as clicks, transactions, or logs.

Unbounded streams have no defined end and must be continuously processed. Flink provides a framework to process this kind of data and run applications at any scale. It can also handle bounded streams, which have a defined end point and are processed in batch streaming.1

Apache Flink also features two relational APIs: the Table API and SQL. With the Table API, you can compose data queries for both data analytics and data pipelining, as well as extract, transform, and load processes. With Flink SQL, you can implement a standard, structured query language for storing and processing information in a relational database, whether you’re using a streaming or batch query. There are a number of other APIs via Apache Flink that can help you build scalable workloads in data analysis and machine learning. For instance, PyFlink Table makes it simpler to write powerful relational queries. Also, Flink Python’s DataStream API lets you control Flink building blocks, like state and time, to build more complex stream processing cases.2

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Apache Flink tutorial curriculum

While an Apache Fink tutorial can be a focused way of learning, a standalone Apache Fink course may be difficult to find. However, instruction on stream processing with Apache Flink can be part of a larger “big data” or data engineering curriculum.

In these kinds of courses, learners may spend time on some of the following topics:

  • Understanding challenges businesses face given the upsurge of data
  • Using ETL (extract, transform, load) tools
  • Managing data pipelines
  • Processing large-scale data streams
  • Querying data streams
  • Creating machine learning models with streaming frameworks

It could be helpful to have a basic understanding of Python or Java programming before getting started with Apache Flink. Other skill areas that could make learning the stream processing service easier include, basic statistics, SQL, and business intelligence analytics.

Jobs that use Apache Flink

With Apache Flink processing skills in your wheelhouse, you could be qualified for a variety of different jobs, such as:

  • Data scientist: Builds machine learning and automation systems to leverage data.3
  • Software engineer: Develops back-end solutions to improve user experience.4
  • Database administrator or data engineer: Migrates and converts cross-platform data.5

For learners interested in advancing their knowledge of the field, edX offers a variety of educational pathways, including specialized boot camps, full programs that enable you to earn a bachelor's degree or pursue a master’s degree. Find the right course for you.

How to conduct stream processing with Apache Flink

Today’s data scientists are handling real-time data from web activity, internet of things (IoT) devices, financial service transactions, and location-based tracking feeds. Stream processors like Apache Flink provide an open-source framework for creating stateful, fault-tolerant stream processing at scale, and providing immediate insights.

Once you install Apache Flink, you can begin using Flink’s DataStream API to stream and filter data inputs. You will need to select a deployment mode: Local, Cluster, or Cloud. From there, learners could implement basic operations on streaming data, or start incorporating multiple stream sources.6

If you are interested in adding new skills to your resume, explore how online learning opportunities such as a data analytics boot camp or even a bachelor’s degree in data science can help you build the expertise and experience you need to pursue roles in this field.

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    Frequently Asked Questions

    What are Apache Flink use cases?

    Boasting an extensive set of features, Apache Flink is an excellent tool for developers. One Apache Flink example that showcases its use: You might build event-driven applications that detect fraud, security anomalies, or other rules-based triggers, allowing for immediate response. You can also create a consistent state savepoint to update, A/B test, or adapt the scale of an application.

    What language is Flink written in?

    Apache Flink is written in Java and Scala and has language support for Java, Scala, Python, and SQL. If you’re wondering which language you should write scripts or big data logic in, a good choice is likely Java or Scala, as these well-supported building block languages offer built-in functionality.

    What is the difference between Flink and Spark?

    Both Apache Flink and Apache Spark offer single platforms where you can batch, stream, and process interactive data. Flink was built as a streaming product, whereas Spark added this feature later. Spark uses micro-batching; streams are batched and processed together at small intervals. Meanwhile, Flink uses native streaming, where every incoming data record is processed immediately without batching.

    What is the difference between Flink and Kafka?

    Both Apache Flink and Kafka Streams can be used for stream processing, from user-facing applications to back-end data analytics systems. The main difference is that the framework resides in a cluster with Apache Flink and inside microservices with Kafka Streams. You might choose Kafka if you’re dealing with microservice or IoT applications, as you can use the API to build an app for easy real-time decision making.

    What is the difference between Flink and Storm?

    Apache Storm is another example of a stream processing service. Their architecture differs — Storm uses Workers and Executors for tasks, while Fink uses Task Managers. Additionally, Flink uses “windows” to achieve its “exactly-once” results. This means that every incoming event is processed to affect the final results once and is not duplicated.

    What is the difference between Flink and Beam?

    Apache Beam is a unified API for batch and streaming scenarios, with built-in support for Python and Go, along with all their libraries. Together, Apache Flink’s “exactly-once” semantics, memory management, and robust security and Beam’s flexible cross-language support and side inputs are powerful. You can translate Beam pipelines into Flink jobs and configure metrics reporting, create checkpoints, and achieve “write-once, execute-anywhere” development.

    1. What Is Apache Flink? — Architecture. Apache Flink. Retrieved March 9, 2023

    2. Table API & SQL. (2023). Apache Flink. Retrieved January 23, 2023

    3. What Data Scientists Do. (2022). U.S. Bureau of Labor Statistics. Retrieved March 9, 2023

    4. What Software Developers, Quality Assurance Analysts, and Testers Do. (2023). U.S. Bureau of Labor Statistics. Retrieved March 9, 2023

    5. What Database Administrators and Architects Do. (2022). U.S. Bureau of Labor Statistics. Retrieved March 9, 2023

    6. Intro to the DataStream API. Apache Flink. Retrieved March 9, 2023