Important Things About Hadoop and Apache Spark

In the big data space, they are seen as competitors but the main feeling is that they are better together with growing consensus. If you go through an reads about big data you will get to know about the presence of Apache Spark and Hadoop. Here are their brief overlook and comparison.

1) There are lots of things they do:

The two big data frameworks are Hadoop and Apache Spark but there is no same purpose that is actually served. Across various nodes, it shares massive data collections inside a cluster of commodity servers that you need not buy and handle commodity servers and it means you don’t need to buy or maintain expensive custom hardware. A data processing tool in spark, on the other hand, works on distributed data collections and it doesn’t do shared storage.

2) They both are independent:

There is not only just a storage component in Hadoop called Hadoop Distributed File System as you can also find MapReduce a processing component and there is no need of a spark to get it done. It is possible to use Spark without the need for Hadoop. There is no own file management system in Spark and it needs to be combined with one apart from that if HDFS is of no use then you can find another cloud-based data platform and the Spark was designed for Hadoop, however, there are lots of people who agree that they work better together.

3) Spark is faster:

MapReduce is generally slower than Spark because the latter’s way of processing the data. The operation of MapReduce is done in steps throughout the data in one fell swoop. This is how the MapReduce workflow looks like, “ the cluster reads the data work an operation and the clusters are written with results and the cluster reads the updated data and the next operation is performed, produce next result to the cluster etc. In memory and in near real-time the Spark completes the full data analytics and the data from the cluster is read for working all requisite analytic workings. Thus Spark is 10 times faster than MapReduce and 100 times faster than in-memory analytics.

4) Spark’s speed is not required for you:

If your data operations and reporting requirements are mostly static and you can stay for batch mode processing then your MapReduce processing would be just fine. On streaming data, if you need to do analytics like from sensors on a factory floor or possess applications needing multiple operations, then you need to go with Spark. For instance, there are lots of operations required and common applications for Spark are a real time marketing campaign, along with online product recommendations, analytics, machine log monitoring etc.

Thus join DBA Course to know more about Hadoop and Apache Spark.

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