Apache Spark Use Cases for DataOps in 2021



October 27, 2021

Apache Spark Use Cases for DataOps in 2021

Apache Spark is one of the best and the most powerful data processing solutions. Its use cases are limitless and in recent years it has become a core platform for big data architectures. 

Today we will discuss a few Spark use cases and why the need for Apache Kafka is growing everyday.

Use cases of Spark and rooted in big data

Today, fast data processing is a necessity for organizations that create and sell products. Today, people are dealing with a massive amount of data being created everyday. It is tough to keep pace with big data. Well, you don’t necessarily need millions of users to leverage Spark, all you need is to work with large datasets. Organizations are using  Spark to deliver more accurate data faster. 

When DataOps isn’t handling Big Data, they can’t justify using specialized tools like Spark. The added complexities can leave more room for error. In this situation, the data which is extracted, passed to a cluster, combined and stored.

How does Apache Spark work?

When the data started growing, the amount of data being created outpaced the data that could be processed. This was seen as a bottleneck and to counter that, Hadoop was created. Hadoop worked well for quite some time but with the increasing volume of data and the demand for greater processing speed, the requirement for a new solution grew.

Apache Spark followed the same principle of distributed processing but with different goals. Spark jobs distributes partitions on RAM instead of HDFS drives. This means that it doesn’t require reading and writing partitions everytime to a disk. Thus, Apache Spark is 100x faster than Hadoop and closer to real-time processing.

Lets go deeper and explore Apache Spark use cases for data products.

Apache Spark use cases for DataOps

Apache Spark can do a lot of things. Hare, we will discuss two use cases:

  • Productizing machine learning models
  • Decentralized data organizations and data mashing

Productizing ML models

Apache Spark was created to fill the gap between a successful implementation and wasted investments. Although it has not removed all the barriers, it has allowed for the proliferation of ML data. 

MLib, which is Spark’s general machine learning library, has algorithms for Supervised and Unsupervised Learning. One of the biggest advantages of using Machine Learning is its end-to-end capabilities when building an ML pipeline. Data scientists can use MLib to apply ML algorithms and distribute workload.

This helps engineers to solve Machine Learning models faster.

Data meshing to democratize your data organization 

Organizations are becoming more data-driven these days. To create scalable data products, it is important that every data practitioner is able to collaborate with each other to access the raw data. Data mesh is a data platform architecture that operates on a domain-oriented, self-service design. 

There are two objectives of implementing data mesh models, one is the need of domain and the other is the duplication of efforts. Apache Spark can easily solve these problems. Also, Spark is great in helping these self-service teams build their own pipeline. 

A data mesh needs a system for conducting scalable observability, some of these capabilities are-

  • Data product versioning
  • Data product schema
  • Unified data logging
  • Data product lineage
  • Data product monitoring/logging
  • Data product quality metrics

Unchecked distribution can create problems for your data health

It doesn’t matter if you are distributing ownership of a pipeline across your organization or distributing a workload, Sparks ensures an efficient process.  

Spark is dividing the computation task, sending partitions to clusters, computing each micro-batch, combining outcomes and sending it to the next phase of the lifecycle of the pipeline.  That’s a complicated process and creates opportunities for error and complications. So, along with Spark, it is essential to have a system of observability set up to manage data health.


As we have discussed use cases of Spark for DataOps, we can say that Spark is popular and versatile because of the capabilities it has. If you are looking to implement Spark in your organization, Ksolves can help you. We provide customized end-to-end Apache Spark services so you don’t have to worry about it. If you want to reduce the risk of spark job filing or looking for a platform that is fast, scalable, and can do everything, we must connect.

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Read related articles:

Feeding Data To Apache Spark Streaming

Is Apache Spark enough to help you make great decisions?

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Vaishali Bhatt

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