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5 Reasons Why ETL Professionals Should Learn Hadoop

ProjectPro

While the initial era of ETL ignited enough sparks and got everyone to sit up, take notice and applaud its capabilities, its usability in the era of Big Data is increasingly coming under the scanner as the CIOs start taking note of its limitations.

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Difference between Pig and Hive-The Two Key Components of Hadoop Ecosystem

ProjectPro

Generally data to be stored in the database is categorized into 3 types namely Structured Data, Semi Structured Data and Unstructured Data. We generally refer to Unstructured Data as “Big Data” and the framework that is used for processing Big Data is popularly known as Hadoop.

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5 reasons why Business Intelligence Professionals Should Learn Hadoop

ProjectPro

The toughest challenges in business intelligence today can be addressed by Hadoop through multi-structured data and advanced big data analytics. Big data technologies like Hadoop have become a complement to various conventional BI products and services. Big data, multi-structured data, and advanced analytics.

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What are the Pre-requisites to learn Hadoop?

ProjectPro

There have been several headlines about various big data jobs recently- Best Salary Boost in 8 years awaits US professionals in 2016, STLToday Geeks Wanted! Learning Hadoop will ensure that you can build a secure career in Big Data. Big Data is not going to go away. The US will soon be flooded with 1.9

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Top 6 Big Data and Business Analytics Companies to Work For in 2023

ProjectPro

To answer this question at hand, this blog presents you 6 of the top big data companies to consider in the big data world. According to NASSCOM, the global big data analytics market is anticipated to reach $121 billion by 2016. intelligence community after which it extended the analytics solutions into corporate work.

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Hadoop Ecosystem Components and Its Architecture

ProjectPro

In our earlier articles, we have defined “What is Apache Hadoop” To recap, Apache Hadoop is a distributed computing open source framework for storing and processing huge unstructured datasets distributed across different clusters. It can also be used for exporting data from Hadoop o other external structured data stores.

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