Apache Spark vs. Apache Flink: A Comparison 

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Big Data processing has become an irreplaceable part of IT Services in the modern era. Scientific research, multinational corporations, industries, markets, and all sorts of other fields produce a large pool of information. This data stream had been previously underutilized but now proves to be a disruptive force in their sectors. Processing all this information can give many relevant results, providing insights into each and every part of an industry’s complex dynamics. 

Apache Hadoop, Apache Spark, and Apache Flink are the three frontrunners in the fields of Big Data Analytics and processing. Each of these platforms has its own strengths as well as weaknesses. Spark and Flink are exceptional cases in this regard, as they are both considered the evolved forms of Hadoop. Being so, they also offer native Hadoop support capabilities on these platforms. Apache Spark training has become indispensable for data scientists due to this very fact. 

Being the more recent avatars of the sector, Spark and Flink are often compared to each other. This is a run-down on both platforms individually, as well as compared to each other.

Apache Spark

Apache Spark is an open-source Data Processing platform that handles information in bulk. Being the next iteration of Hadoop, Spark has native support on Hadoop and offers all its services, albeit on a bigger scale and better. It is a hybrid service that bridges the gap between Data handling in bulk volume as well as in real-time, being able to perform both these functions to an extent.

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Apache Flink

Apache Flink is also an open-source Big Data Analytics framework. The platform is hailed as a dedicated stream processing framework, which means that the data can be processed in real-time without dividing it into batches. Flink is lightning fast in its operation and has some significant advantages over its competitors, including Spark. It is considered a specialized platform for live stream processing of data and is streamlined to produce faster, more accurate results on this front. Due to these features, it is regarded as the next generation (4G) of Data processing.

Flink vs. Spark: Points of Contest

There are quite a few distinguishing features between Apache Flink and Spark in terms of their working, speed, application, and other salient features. Some of these fundamental differences are as follows.

Data Processing and Streaming: 

Apache Spark, like its predecessor Hadoop, is a Batch Processing system at its core. The Data Processing is done using datasets known as micro-batches, and this makes it the ideal tool for handling bulk volumes of input at a time very efficiently. All data processing functions, be it batch processing or the streaming functionality, is done via micro-batches. While this is ideal for handling volumes of data, it does lead to restrictions while processing live streams.

Flink, on the other hand, is optimized for streaming a lot more than it is for Batch processing. It offers similar runtimes for both. Unlike Spark, which uses micro batches, Flink is a real live-streaming tool. This allows it to process and analyze data inputs in real-time. It also supports batch processing, where the batch is a finite set of stream data.

Process Optimization and Latency: 

Apache Spark requires manual optimization and has a higher latency. This means that work takes longer on Spark, and this mainly affects its performance during real-time processing.

In contrast, Flink has inbuilt optimization capabilities that are independent of the programming interface that it runs on. The processes are streamlined automatically, regardless of the interface, and this gives it an edge over Spark. It also has a much lower runtime for live stream data processing and offers lower latency. Simply put, it outperforms Spark in the live stream Data processing department.

Performance and Community rating: 

Apache Spark is well established as a data handling platform and has a long-standing relationship with members of the Data Science community. It is considered the go-to tool for Batch processing of large datasets, and this is a reason why Apache Spark training is an integral course for any and every budding data scientist.

Apache Flink also has a high rating as a data processing system, especially when it comes to real-time data processing. It is considered a specialized tool in this regard, and its benefits and consistent performance in this category make it a favorite for live stream data processing.

Iterative Processing: 

This is also one of the functionalities built into Flink, whereas Spark has to go about iterative processing via coding of external loops. Native support of iterative processing gives Flink an edge over Spark, especially in terms of speed.

Speed: 

From most points discussed above, it is a reasonably obvious conclusion that Flink tends to be faster than Spark.

Commonalities: 

Being related systems, they have many common features as well. Some of these are as follows;

  1. Both Spark and Flink offer Automatic Memory management. While older versions of Spark offered customization in this regard, it has been changed since Spark 1.6.
  2. They both also have features of Duplicate Elimination by processing each record exactly once. 

Conclusion

While Apache Spark and Apache Flink are both improved versions of Hadoop, they are both aimed at different functions and applications. While Spark is a significant improvement over Hadoop and its bulk handling capabilities, Flink moves in a different direction, focusing on live stream data handling. These two platforms have their own strengths, weaknesses, and applications where they both excel. They are both indispensable in data analytics.

To sum it up, Apache Spark is the old faithful for many data scientists. It is tried and tested and a true jack of all trades. It has a passionate community of users dedicated to making it better, and all data scientists will have to join this family and learn the ways of the Spark. On the other hand, Flink is regarded as the next-gen tool and laser-focused on live stream processing. While it does have many capabilities of its older brother, it shines in a different department. Apache Spark training gets you equipped with a reliable tool, while Flink helps you grasp a brave new world.

LisaLisa

Welcome to the Night Helper Blog. The Night Helper Blog was created in 2008. Since then we have been blessed to partner with many well-known Brands like Best Buy, Fisher Price, Toys "R" US., Hasbro, Disney, Teleflora, ClearCorrect, Radio Shack, VTech, KIA Motor, MAZDA and many other great brands. We have three awesome children, plus four adorable very active grandkids. From time to time they too are contributors to the Night Helper Blog. We enjoy reading, listening to music, entertaining, travel, movies, and of course blogging.

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