Apache Flink – A New Feather Cap for Big Data Analytics

The Hadoop ecosystem has many tools for big data analysis that cover almost all niches. Apache Flink is a new generation in big data processing frameworks. Apache Flink big-data framework has many innovations and will soon be the standard for batch and streaming data processing in big data analytics.
Apache Flink is a tool that Hadoop uses to continuously evaluate new rows. Hadoop developers have been working under Spark until now. (Read our articles on Spark and Why it’s so fast to learn more).
Apache Flink was created and we can now see a silent battle between Spark and Apache Flink. Let’s first discuss how Apache Flink aids in stream processing real-time.
Apache Flink and its Streaming Method
Apache Flink is an open-source platform that is used to process large amounts of distributed and batch data. Flink can be integrated with other open-source tools as well as big data processing tools to achieve big data analytics purposes such as data input, output and deployment. Flink engine can create streaming applications with multiple APIs for real-time data use, including static data, SQL data and unlimited streaming data.
Flink big data streaming process is based on two key specialties: –
High performance
Low latency
Flink supports batch data processing. It integrates real-time streaming and batch data into a single system. It also has a single runtime that supports streaming and batch operations. Flink’s DataStream API also supports data stream transformations. It also supports flexible windows and user-definable data states.
Image source: https://flink.apache.org/features.html#streamingMoreover, Flink has a measure of fault tolerance. Flink draws periodic highlights from streaming data that could be used to recover the data. Flink, on the other hand captures the sequences of these transformations for batch data processing. It can therefore restart failed jobs without causing data loss.
How does Flink and Hadoop work together in Hadoop Ecosystem
Flink in Hadoop ecosystem can be integrated with other data processing software to facilitate streaming big data analytics. Flink can be run on YARN. It can also be used with HDFS (Hadoop’s distributed file system). It can fetch stream data from Kafka. It can also connect to many storage systems and execute program code on Hadoop.
Image source: https://flink.apache.org/features.html#streamingTo repeat, Flink has its own runtime and does not depend on MapReduce for data processing. It can replace Hadoop’s MapReduce. It can also work independently within the Hadoop ecosystem. Flink can also access Hadoop’s File System to read or write data.
Flink and Spark Operative Models: Similarities & Distinctions
Apache Spark and Apache Flink both offer streaming services with the same guarantee of processing every record only once. It eliminates duplicate records that may be available. Flink and Spark, the two frameworks, provide high throughput. It also provides better fault tolerance.
Spark and Flink both use in-memory databases that do not store their data. They store data in memory and only serve streaming. This makes big data analytics more efficient for programmers. Both can take data in any format and help in the calculation. Spark and Flink both can be used for predictive analysis. They can also plug data into machine learning algorithms to find patterns.
It doesn’t matter if it’s data from financial transactions, GPS signals or signals generated by telephony. It all comes down to data! It is a continuous flow or, to put it another way: it is data. Stream processing is the most important thing. Stream processing can be a challenge when data consistency and fault tolerance are required. This is where you will need to answer complex questions, and more importantly in the form of windows. Performance is achieved by low latency and high throughput.
Spark streaming and Flink streaming are different in the way they compute. Flink streams in continuous flow streaming while Spark processes in micro-batch models. Flink is able to process in continuous flow streaming models. Flink, on the other hand, supports custom or record-based window criteria. Spark, however, follows time-dependent window criteria.
Spark vs Flink: Which one is the most urgent?
You should have a good idea of Apache Flink and the advantages it has over Spark. Does this mean Spark will become obsolete? Or will you switch to Flink for the next project? Spark vs Flink: Which one is better?