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Avantika Shergil
Avantika Shergil

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Hadoop vs Spark: Which is a better framework to select for processing Big Data?

Big Data Analytics has brought a paradigm shift in the business realm. New-age companies understand the need for gaining invaluable insights about their business through the application of Big Data. And this is why Hadoop and Spark have emerged as reliable solutions for processing Big Data. There are a number of supporters for both and the expert Big Data Analytics Companies decide amongst the two based on the various factors and after knowing the requirements from the businesses looking for a solution.

What is Hadoop?

Hadoop is a collection of open-source Java programs written to perform various operations on Big Data. The framework is famous for handling a broad set of simultaneous tasks as it provides enormous processing power and massive storage capacity to the developers.

This is one of the most commonly heard names in the field of big data and advanced data analytics. The top Hadoop Consulting and Development Companies will have better expertise to manage and decide based on your requirements.

What is Spark?

Spark is a Big Data Analytics technology designed for fast computing. Spark is based on MapReduce and builds upon the MapReduce model to include more range of computations, including stream processing and interactive queries.

The differentiating feature about Spark is the in-memory cluster computing feature that tremendously increases the processing speed of the application.

Spark is considered to be one of the most used frameworks in big data analytics, the Spark Development Service Providers believe that the framework has its own set of benefits and importance.

Differences between Hadoop and Spark


Earlier, Hadoop MapReduce was the undisputed leader in terms of processing speed. But the scenario has changed now as Spark proving to be 100 times faster with the in-memory processing operations and is ten times faster for services that are disc-based.

Spark does not face input-output issues at every single step granting a better performance for applications running on Spark. Spark also provides cyclical connections between various processing steps improving the analysis by leaps and bounds.

Although Spark is much faster, for batch processing, the performance of Hadoop is more stable as it can handle large amounts of data. It is because Spark is much less efficient while processing large datasets. It can lead to RAM overhead memory leaks.

Ease of Use

Hadoop is a complex framework, and beginners find it challenging to use as every operation requires coding by hand. This makes it challenging to use Hadoop for large scale projects which require thousands of lines of code.

Spark, on the other hand, is a very user-friendly framework that even allows the users to get an immediate response to their queries.


Although both Hadoop and Spark are open-source projects and are virtually free, you need to take into account other expenses as well. These costs include the cost of hardware, software, maintenance, and the cost of hiring a team that knows how to implement cluster administration.

Usually, Hadoop requires more memory, and Spark Requires more RAM. Thus, for on-premise clusters, if you choose Spark, then the costs will be more. Also, since Spark is a relatively newer system, there is a shortage of professionals who are well versed in the order. It makes it more expensive as compared to Hadoop.


Developers consider Hadoop as more secure since it contains more confident and adequate controls for its Distributed File System. Hadoop also enjoys a particular project dedicated to its security, called Apache Sentry.

Spark has a much-less robust security model as compared to Hadoop.

In Hadoop, the data gets replicated across many codes, and each file is stored on numerous machines. It increases the safety and security of the data as one can rebuild the data quickly in-case a computer goes down.

Spark has security authentication enabled via a shared secret, and it prevents data corruption by using RDDs (Resilient Distributed Datasets). The main benefit of RDDs is that they can reference the external storage systems, and you can rebuild them if the need arises. This feature makes the data in Spark resilient to attacks and gives peace of mind to the user.

Machine Learning

Hadoop uses the power of Mahaout for data processing. Batch-based collaborative filtering, clustering, and classification are the powers that Mahaout provides Hadoop.

Hadoop has now evolved, and it is using Samsara now, which is a DSL language that lends the power of in-memory computing to the users of Hadoop. The Hadoop users can also write their unique algorithms using Samsara.

Spark, on the other hand, uses MLLib, which is a machine learning library used in iterative in-memory machine learning applications. Available in Java, Python, R, and Scala, the MLLib also includes regression and classification.

Thus, we can conclude that both Hadoop and Spark have high machine learning capabilities.

Hadoop is good for

Processing massive data sets

Hadoop is excellent for processing massive data as it has a parallel processing feature. The MapReduce function of Hadoop breaks the data into small chunks so that Hadoop can handle data sets separately across separate data nodes. Once Hadoop gathers the results, it can then compile the results from multiple nodes into a single result.

If the Dataset is larger than the RAM available, then Hadoop will outperform Spark.

Where cost is a significant factor

Hadoop can prove to be the right solution where the processing speed is not that important. If you do not want immediate results, then Hadoop proves to be a cost-effective solution.

Spark is good for

Lightning-fast data processing

The feature of in-memory computing makes Spark fast as compared to Hadoop. Spark has proven to be 100 times faster than Hadoop for data that is stored in RAM and ten times faster for data that is stored in the storage. Thus, if a company needs to process data on an immediate basis, then Spark and its in-memory processing is the best option.

As Spark can process data faster, it can create logical combinations amongst data sets pretty quickly. Hence, Spark is suitable for applications where a lot of shuffling and sorting of data is required.

Repetitive data processing

Spark is good at tasks where you need to process data iteratively. RDDs (Resilient Distributed Datasets) of Spark helps in enabling multiple map operations in memory. It is significantly better than Hadoop, where you need to write interim results to a disk.

The computational model of Spark is great in Graph processing as it is an operation that requires iterative processing. GraphX is a perfect API that Spark provides for graph data processing.

The “Big” Takeaway

Hadoop is mainly used for operations that are disk-heavy, while Spark is a flexible system that can process less-heavy data processing applications. With Hadoop MapReduce, you can cost-effectively process massive amounts of data. Hence, wherever cost is more important than the speed, you can choose Hadoop over Spark.

While Spark can provide instant results but is costlier than Hadoop, hence wherever speed is more important, you should choose Spark.

Thus at the end of the day, the choice between Hadoop and Spark is solely dependent on the requirements of your business. This should have provided a good overview of the difference between the two and but if you are still unsure or need assistance with the execution then, these top big data service providers can help you end-to-end right from selecting a better framework to executing it for your business requirement.

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