How to be successful with Apache Spark in 2021

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The arrival of Big Data in the technology world gave birth to big data platforms like Hadoop, Apache Spark, Flink, etc. Although each platform is unique with stand-out features, Apache Spark is the one leading the race. Ever since it was launched in the mainstream market, Apache Spark is helping businesses deal with the enormous data smoothly. It loads big data, does computation in a distributed manner and is thus known as Spark distributed computing.

What is Apache Spark

Apache Spark was designed at AMPLab of UC Berkeley as an alternative to Hadoop’s MapReduce. Spark was later given to the Apache software foundation. But, unlike Hadoop, it is easy, fast, and supports real-time streaming. Apache organizations claim Spark as a lightning-fast cluster computing technology. Even though Spark was built as a processing engine for Hadoop data, it is not dependent on Hadoop due to its very own cluster management.

Apache Spark in-memory processing is a kind of computing where data is kept in RAM instead of slow disk drives. This enables users to store and process huge amounts of data at a low cost. This increases the performance of spark by 100x as compared to hadoop mapreduce.

Spark comes with multiple libraries for ML like Spark MLib and graph algorithms such as spark GraphX. It is not only a versatile platform but also supports programming languages like Java, Scala, Python, and R.

All these vast features of Apache Spark make it the first choice for companies like Apple, Facebook, and Microsoft.

Why is Spark Popular?

Apache Spark comes with a wide range of features that inspire companies to adopt the platform. Some of them are as follows:

Apache Spark’s native parallelism helps in the fast processing of large data sets that can range from GB to PB scale.
Spark is designed for real-time data streaming and helps you in recovering the lost data if any.
Spark is a platform that can run on both cluster mode and Hadoop YARN. This feature makes Spark flexible and independent.
Spark has connectors for all data storage and the clusters can be deployed on any cloud. This makes Spark extremely versatile.
Spark is a fast and general purpose cluster computing system and using Spark on top of Cassandra can solve major complex issues.

Challenges while using Apache Spark

Apache Spark is an excellent Big Data Framework but is not immune to challenges. There are certain challenges that Apache Spark developers face. Let us look at some of the challenges that create hurdle in the way of smooth spark processing:

Certain configurations in Spark are complex for a beginner as a result; most new developers go for default settings that could hurt the performance.
It gets really hard for a beginner to understand the interpretation and distribution of code. This makes debugging a challenging task.
Deployment is tricky with Apache Spark. The cluster size is the biggest matter of concern as oversizing the clusters the application will suffer from low utilization while under-sizing could result in unsustainable workloads.

Overcoming the above-mentioned issues will help in improving the performance of Spark. In this article, we are going to shed light on some ways you should practice to achieve success with Apache Spark.

Speed the Spark Deployment with Docker

Dockerizing is the process of running an application using Docker containers. The dependencies need to be built only once and later can be run anywhere.

The biggest advantage of using Docker is that it makes Spark more stable and efficient. It also speeds up the deployment as it takes only 30 seconds to do any change in the code and deploy it.

Automatic Computing

Autopilot mode is designed to reduce the burden of managing and optimizing clusters. Ksolves provide Spark platforms that automatically adjust configurations like cluster size, disc type, memory management, etc.

This improves the efficiency of any Spark platform by 2X.

Serialization

Serialization is very important for any application that distributes data. Make sure your program can send objects and serialize them real quick. Slow serialization will also slow down the computing of Spark.

Overcoming Degradation of RDD

When there is not enough memory in storage, Spark RDD starts degrading. The storage can be minimized using a disc that provides the same performance.

While Deploying Spark increases the size of the RAM for better efficiency.

Closure

We hope we have covered major aspects of how your enterprise can achieve great success with Apache Spark by overcoming the shortcomings.

If you are looking for an Apache Spark consulting company and wondering where to go then, Ksolves is a perfect choice. Ksolves is a one-stop solution and has the right expertise to help you with the smooth installation of the Apache Spark platform. Do write to us for any assistance.

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