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In this video on Hadoop vs Spark you will understand about the top Big Data solutions used in the IT industry, and which one should you use for better performance. So in this Hadoop MapReduce vs Spark comparison some important parameters have been taken into consideration to tell you the difference between Hadoop and Spark also which one is preferred over the other in certain aspects in detail.
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📝Following topics are covered in this Hadoop vs Spark comparison tutorial:
01:25 - What is Hadoop?
03:40 - Hadoop Ecosystem
07:15 - What is Spark?
08:25 - Spark Ecosystem
09:27 - Apache Spark components
12:15 - Hadoop vs Spark
19:04 - Job trends and Salaries
20:27 - Which is better to choose?
23:33 - Quiz

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What do you think which one of them is better among Hadoop vs Spark according to you? Tell us in the comment section below.
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Why Hadoop is important

Big data hadoop is one of the best technological advances that is finding increased applications for big data and in a lot of industry domains. Data is being generated hugely in each and every industry domain and to process and distribute effectively hadoop is being deployed everywhere and in every industry.

Why Spark is important

Today there is a widespread deployment of Big Data. With each passing day the requirements of enterprises increases and therefore there is a need for a faster and more efficient form of processing data. Most of the data is in unstructured format and it is coming in thick and fast as streaming data.Apache Spark is seeing widespread demand with enterprises finding it increasingly difficult to hire the right professionals to take on increasingly challenging roles in real world scenarios. It is a fact that today the Apache Spark community is one of the fastest Big Data communities with over 750 contributors from over 200 companies worldwide.

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