Apache Flink in Big Data Analytics

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    Apache Flink in Big Data Analytics

    Hadoop ecosystem has introduced a number of tools for big data analytics that cover up almost all niches of this field. At present, a new generation of big data processing framework is in the picture – Apache Flink. With a lot of innovations under its belt, Apache Flink big data framework is soon going to become the de-facto tool for batch and streaming data processing for big data analytics.

    Obviously, with Apache Flink, Hadoop is continually evaluating in a new row. Till now, Hadoop developers were under the umbrella of Spark with its impressive features (To know more about Spark, go through our previous articles on <u>Apache Spark</u> and <u>Why it is so fast</u><u>)</u>.

    However, with the invention of Apache Flink, we can observe a silent battle between Apache Flink vs Spark. But before discussing it, let’s get an overview of how Apache Flink helps in real-time stream processing.

    Apache Flink and its Streaming Process

    Apache Flink is another platform which is open source and mainly used to process the large set of distributed and batch data. You can integrate Flink with other open source tools, as well as with big data processing tools for big data analytics purpose such as data input, output, and deployment. Flink engine with the help of multiple APIs creates streaming applications on real-time use for different types of data like static data, SQL data, unlimited streaming data, etc.

    The two critical specialties of Flink big data streaming process are –

    • High performance
    • Low latency

    As a result, Flink also supports batch data processing. It is a continuous streaming pattern which integrates batch and real-time streaming into a single system. Moreover, it is a single runtime for both the batches and streaming. In addition to that, Flink’s DataStream API supports transformations on data streams. Also, it supports the user-defined data state and flexible windows.

    Moreover, Flink has a measure of fault tolerance. It draws periodical highlights of the streaming datasets which could be used for future recovery. On the other hand, for batch data processing Flink captures what the sequences of those transformations are. Hence, it can restart failed jobs without data loss.

    How does Flink & Hadoop Work Together in Hadoop Ecosystem?

    Flink in Hadoop ecosystem is integrated with other data processing tools to ease the streaming big data analytics. Flink can run on YARN. It also works with HDFS (Hadoop’s distributed file system) and fetches stream data from Kafka. It can execute program code on Hadoop, and also connects many other storage systems.

    To repeat, Flink has its own runtime and does not depend on MapReduce for data processing. Hence, it works as a replacement to Hadoop’s MapReduce. So, it can work independently in the Hadoop ecosystem. Along with that, Flink can also access Hadoop’s File System to read and write the data.

    Flink and Spark Operative Model: Similarities & Differences

    Both Apache Spark and Apache Flink provide streaming service with the similar guarantee of processing every record once. Thereby it eliminates any duplicate record that might be available. Hence, Flink and Spark, both the framework provide very high throughput. Besides that, it ensures better fault tolerance.

    Flink and Spark both are in-memory databases and do not persist their data to storage. They serve the sole purpose of streaming by storing data in memory. This makes big data analytics faster for the programmer. Both of them can take data in whatever format it is and helps in the calculation. Flink and Spark both are helpful for predictive analysis as they can plug data into machine learning algorithm to discover patterns.

    Whether it is the data coming from financial transactions, GPS signals, signals generated from telephony, or sensor data, ultimately, it’s all about data! To put it another way, it is a continuous flow of data. Most importantly, the need of the hour is real-time stream processing. However, stream processing is a real challenge when it needs to maintain data consistency with fault tolerance. Here you need to answer complex queries, moreover in the form of windows. Low latency with high throughput is the answer to the performance.

    The main difference between Spark and Flink streaming is in their computation style. Spark processes in micro-batch model whereas Flink supports a continuous flow streaming model. On the other hand, Spark follows time-dependent window criteria, whereas Flink supports record-based or custom window criteria based on user definition.

    Spark vs Flink – Which One is the Need of the Hour?

    Till now, you must have gained a fair idea about Apache Flink and its advantage over Spark. But does that mean Spark will be obsolete, or you will switch to Flink for your next project? Well, Spark vs Flink, there is no specific justification for it. However, below are some areas where we can compare Spark and Flink regarding memory management, data processing, CLI (Command line interface), data flow and of course for the support for other streaming products.

    Data Processing

    Flink processes data without latency and almost in real time. But Spark processes batch data which is known as RDD. Hence, there is always minimum data latency with Spark data processing.

    Memory Management

    Spark data partitioning and caching is a manual process. Hence, data processing becomes slow. In addition to that, if Spark runs out of memory, it crashes that node. These problems are not there in Flink. It does not only optimize all data sets but also performs paging when it is out of memory.

    Data Flow

    Spark follows procedural programming approach. Hence, you can retrieve intermediate result during Spark data processing. On the other hand, Flink follows distributed data flow and can deliver intermediate result through its broadcast variable.

    More Enhanced Memory Management

    Flink doesn’t follow JVM’s traditional garbage collection process to reduce the memory load. Instead, it uses custom memory manager for this purpose which ultimately helps in better memory management as compared to Spark.

    Hence, Flink is ideal for complex data streaming otherwise, go for Apache Spark. Not only it is a matured project with a more prominent user base, but also with more third-party libraries. As Flink is getting developed, Spark is also adding features for better performance. Hence, Apache Flink vs Spark, the winner is not yet decided.

    Bottom Line

    If you want to grow as a big data professional, you must get acquainted with latest tools and technologies in big data analytics. However, Hadoop or Spark is the base of all upcoming technologies in this niche.

     

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