Hadoop vs Apache Spark

Things you need to know about Hadoop vs Apache Spark

By: rkspark

If you are into big data technologies then you definitely heard about Hadoop and Apache Spark, they’re hard to miss.

They are the biggest competitors in the industry. This is strange because they do not even serve the same purposes and you can use one without the other.


Some people refer to it as Apache Hadoop. Doug Cutting and Mike Cafarella created it in 2006, in order to support distribution for the Nutch search engine project. It works as a Data processing and data storage software and dominated the market until recently.

Apache Spark 

It is an Open source cluster computing framework that carries out data processing in little or no time. It can process very large data but lacks the capacity for storage. It cannot work alone. It was originally created to work with Hadoop but then came the Hadoop vs Spark competition. It was donated to the Apache Software Foundation and has remained there since then.

Spark vs Hadoop Comparison 

Here are a few things you should know about both:

  1. They serve diverse purposes

Hadoop is an Open source program platform, which handles the processing, storage and distribution of data in big data applications that run in clustered systems, in addition, it contains data processing software called MapReduce. In other words, it is the backbone of big data operations and can easily be modified by anyone. If you have Hadoop, it is needless to purchase and maintain an expensive custom hardware as it does all that the custom hardware should do. On the other hand, Apache Spark does not manage distributed storage; it is chiefly processing software. Spark acts on the distributed data collections.

  1. Spark vs Hadoop are individually sufficient 

You can use Hadoop without using Spark and vice versa. Hadoop uses software, MapReduce to carry out data processing, so if you have Hadoop, Spark could become irrelevant. In addition, even though Spark is ultimately for data processing and used alone, cannot perform data storage, it can be attached to another data storage software like HDFS or any other cloud-based data platform. 

  1. One is faster than the other

Spark is faster than MapReduce because MapReduce uses a systematic approach to data processing while Spark carries out a wholesome processing of the data and completes it in near time. Even though one is slower than the other is, when you have used one to process data, you do not need the other. However, while Spark is just 10 times faster than MapReduce when used for data processing, it is as much as 100 times faster when it comes to in-memory analytics. I guess you made a choice already.

  1. They can work together 

As stated earlier, Spark cannot perform data storage. Therefore, most companies usually use it together with Hadoop. In addition, because Spark is way faster than Hadoop, companies still purchase Spark after purchasing Spark especially when very large and/or multiple data is involved. So, while Spark vs Hadoop are competitors for market space, they can work together.

Irrespective of the Hadoop vs Spark competition, they can work independently of each other and they can work together too. In fact, most users recommend that they work together as they bring out the best in each other

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