Apache Flink: Key Features and Uses

Apache Flink has an extensive feature set that enables organizations to develop and run various applications. According to Apache Flink’s website, “Flink has been proven to scale to thousands of cores and terabytes of application state, delivers high throughput and low latency, and powers some of the world’s most demanding stream processing applications” [1]. Expand your understanding of Apache Flink’s key features and uses and discover more about its advantages and disadvantages. 

What is Apache Flink?

Apache Flink is a processing engine that works with unbounded data streams as well as data streams with a fixed endpoint. It performs computations at in-memory speed at any scale and is capable of layering APIs and implementing flexible deployments with high levels of scalability and performance.

Three key applications of Apache Flink

Three common components of Apache Flink include event-driven, data analytics, and data pipeline applications. 

1. Event-driven applications intake data from one or more event streams and respond to events by triggering computations, performing updates, or implementing external actions. This application processes and accesses data locally, which enables better latency and performance. Examples of event-driven applications include fraud detection, outlier detection, business process monitoring, and more. 

2. Data analytics applications derive insights and information from raw data. Their simpler architecture makes these applications easier to work with and enhances the reliability of failure recovery. Examples of data analytics applications include quality monitoring, product updates analysis, ad-hoc analysis, experiment evaluation, and more. 

3. Data pipeline applications transfer data from one storage system to another, streaming the data as it’s transferred. This application ensures lower latency in moving data to its destination and has versatility since it continuously intakes and emits data. Examples of data pipeline applications include real-time search index building and continuous extract-transform-load (ETL). 

Who uses Apache Flink?

Various organizations implement Apache Flink into their workflows, including: 

• Netflix: This streaming platform runs Flink on AWS within the cloud to manage massive amounts of data and uses real-time streaming processes for fraud detection, personalization improvement, and operational insights implementation.

• Lyft: This driving resource utilizes Flink to stream and distribute data between applications and implement real-time data processing.

• Poshmark: This social marketplace uses real-time data enrichment and batch processing with Flink to enhance personalization.

• National Hockey League (NHL): The NHL is a popular sports league that performs predictive analytics with Flink.

• Norton Lifelock: This cybersecurity organization enforces real-time usage limits with Flink to ensure proper use of its resources.

Various industries utilize Apache Flink to process and monitor large sets of data, detect abnormalities and fraudulent events, track changes within datasets, and more. Since Flink can process unbounded data streams, it enables real-time data processing, which is great for organizations that require real-time analytics and monitoring. 

Apache Flink vs. Kafka

Kafka is a streaming library like Apache Flink, but it’s used for smaller sets of data. Users generally turn to Apache Flink when they require mass amounts of data processing and they’re working with a framework that’s not stored on a Kafka cluster. However, since Flink is complex, it incurs higher costs for organizations, so they will often utilize Kafka or other simpler processing technologies to save money and time. 

Apache Flink vs. Spark

Apache Spark is another popular data processing framework like Apache Flink. While these processing systems share various similarities, they have a few key differences. Flink provides better performance, latency, and scalability, but Spark offers cloud solutions to implement faster development, which is a feature Flink lacks. Spark is more widely used since it has easier accessibility, but Flink is more useful for complex event processing. 

Advantages of using Apache Flink

Powerful data processing and reliable performance are two benefits of using Apache Flink. Its advantages include: 

• Scalability: Flink can process and run applications at any scale and process mass amounts of data since it maintains several terabytes across various machines. 

• Data processing: Flink can process unbounded and bounded data sets. Unbounded data is an endless, infinite stream of data, while bounded data is a finite batch. 

• Integration: Flink seamlessly integrates with various platforms, including Kafka and Spark and programming languages such as Java, Scala, and Python. 

• Reliability: Flink implements fault tolerance, which ensures data security and reliability of data safety. 

Disadvantages of using Apache Flink

Accessibility issues can pose a potential drawback, as can other disadvantages. Three primary challenges that come along with utilizing Apache Flink include: 

• Complex architecture: Flink can be challenging to learn, understand, and debug. Developers may struggle with complexities around serialization, custom watermarks, and type evolution. 

• Deployment issues: Since Flink has such a complex architecture, it requires complex hardware that may be difficult to set up and could delay deployment in some cases. 

• Accessibility: Flink requires experienced teams with extensive knowledge of streaming processes, so it’s generally only accessible to large organizations with the proper resources. 

Getting started with Apache Flink 

You can learn how to download and install Apache Flink, browse its project directory, start and stop a local cluster, and implement job execution. Its website provides a detailed, step-by-step tutorial that guides you through these processes. Apache Flink also provides an in-depth training overview. This overview includes valuable insights into stream processing implementation, covering things like event-driven applications and their architecture. It also offers information on how to integrate fault tolerance into your applications, ensuring reliability and scalability. The tutorials and information available on its website can help you gain a strong understanding of Apache Flink’s capabilities and help you learn how to leverage these skills in your own projects. 

Build relevant Apache Flink skills and knowledge on Coursera

Apache Flink is a widely used streaming and processing engine with various benefits across several industries. Expand your knowledge of data processing and API interactions on Coursera with the University of Michigan’s Data Collection and Processing with Python. If you’re working with a smaller data set, learn how to utilize Apache Spark with IBM’s Machine Learning with Apache Spark.