Apache Flink Course

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What is Apache Flink?

The Apache Software Foundation developed Apache Flink, an open-source, unified stream-processing and batch-processing framework. The core of Apache Flink is a Java and Scala-based distributed streaming dataflow engine. Flink ran any dataflow program in a data-parallel and pipelined (thus task-parallel) fashion. Flink's pipelined runtime technology allows bulk/batch and stream processing programs to be executed. Additionally, Flink's runtime natively allows the execution of iterative algorithms.

Flink is a streaming engine with high throughput and low latency, as well as support for event-time processing and state management. In the event of a machine failure, Flink programs are fault-tolerant and support exactly-once semantics. Dataflow programs are automatically compiled and optimized from programs written in Java, Scala, Python, and SQL and run in a cluster or cloud environment. Flink does not have its own data storage system, but it does provide data-source and sink connectors for Amazon Kinesis, Apache Kafka, HDFS, Apache Cassandra, and ElasticSearch.

Apache Flink is a framework for doing computations on any data stream. It's a distributed framework engine that's open-source. It can run in any environment, with computations performed in any memory and at any size. The processing is frequently done at a high rate with little latency. Furthermore, the data is generated at a high rate. Dataflow diagrams can be run in a parallel or pipeline mode. Java and Scala are used to create the framework. It is faster than Spark because it only processes the data that has changed.

What is Apache?

The Apache HTTP Server is a free and open-source cross-platform web server distributed under the Apache License 2.0. Apache is produced and maintained by an open community of developers under the umbrella of the Apache Software Foundation. Although Linux is used by the vast majority of Apache HTTP Server instances, recent versions also run on Microsoft Windows, OpenVMS, and a number of Unix-like systems. Previous versions supported NetWare, OS/2, and other operating systems, as well as mainframe ports.

After work on the NCSA code stagnated, the development of Apache began in early 1995, based on the NCSA HTTPd server. Apache was essential in the early development of the World Wide Web, swiftly displacing NCSA HTTPd as the most popular HTTP server. It was the first web server software to serve over 100 million websites in 2009. According to Netcraft, Apache served 24.63 percent of the million busiest websites in January 2021, while Nginx served 23.21 percent and Microsoft was in third place at 6.85 percent (for some of Netcraft's other stats, Nginx is ahead of Apache), while Apache is ranked first at 35.0 percent, Nginx second at 33.0 percent, and Cloudflare Server third at 17.3 percent according to W3Techs.

Benefits of using Apache Flink?

Data can be processed as unbounded or bounded streams.

Unbounded streams have a beginning but no end. They don't stop working and continue to give data as it is generated. Unbounded streams must be continually processed, which means that events must be handled as soon as they are consumed. Because the input is unbounded and will never be complete at any point in time, it is impossible to wait for all of the data to come. To be able to reason about result completeness when processing unbounded data, events must often be consumed in a specified order, such as the order in which they occurred.

Bounded streams have a beginning and an endpoint. Before conducting any computations, bounded streams can be treated by consuming all data. Because a bounded data collection may always be sorted, ordered ingestion is not necessary to process bounded streams. Batch processing refers to the processing of bounded streams.

Anywhere Application Deployment

To run applications, Apache Flink is a distributed system that requires computational resources. Flink works with a variety of cluster resource managers, including Hadoop YARN, Apache Mesos, and Kubernetes, but it may also be used as a standalone cluster.

Flink is built to work with all of the resource managers mentioned above. This is done via resource-manager-specific deployment modes, which allow Flink to communicate with each resource manager in their own language. When deploying a Flink application, Flink automatically finds the required resources depending on the application's chosen parallelism and requests them from the resource management, Flink requests new resources to replace the failed container. REST calls are used for all communication related to submitting or controlling an application. This makes it easier to integrate Flink into a variety of contexts.

Flink is a stateful streaming application framework that can run at any scale. Applications are parallelized into thousands of jobs that are distributed and run in a cluster at the same time. As a result, a program can use an almost infinite number of CPUs, main memory, disc, and network IO. Furthermore, Flink maintains a vast amount of application states with ease.

Why should we use it?

It comes with a long list of features. It can be utilized in a variety of situations, including real-time data processing and iterative processing. It is quite simple to implement in a new context. It gives you a more powerful foundation for dealing with streaming data. It has a more efficient and powerful data-playing algorithm. It's the next step in the big data evolution. It outperforms all other big data processing engines.


Below are some of the areas where Apache Flink can be used:

Fraud Detection

Anomaly Detection

Social network

Rule-based alerting

Continuous ETL

Quality Monitoring

Ad-hoc analysis of live data

Large scale graph analysis

Real-time search index building


Why do we need Apache Flink?

We have Apache Spark for massive data processing up until now. However, it is a better version of Apache Spark. A distributed Stream data processor is at the heart of Apache Flink, doubling the performance of real-time stream data processing. Apache Flink also makes graph analysis simple. It's also free and open source. As a result, it is the next-generation big data tool.

Who is the right audience for learning Apache Flink?

Until now, we've relied on Apache Spark for large-scale data processing. It is, however, a more advanced version of Apache Spark. Apache Flink is built around a distributed Stream data processor that doubles the throughput of real-time stream data processing. Graph analysis is also straightforward using Apache Flink. It's also open-source and free. As a result, it has been dubbed the "next-generation big data tool."

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Course Syllabus

Getting Started 11 lectures 17 mins
Introduction Preview 01:42
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