It has been 10 years since Hadoop first disrupted the Big Data world, but many are still unaware of how much this technology has changed the data analysis scene. The tiny toy elephant in the big data room has become the most popular big data solution across the globe. It is critical that you understand, what Hadoop is, what it does and how does Hadoop work before you decide to steer your career in that direction.
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Hadoop is used in big data applications that gather data from disparate data sources in different formats. HDFS is flexible in storing diverse data types, irrespective of the fact that your data contains audio or video files (unstructured), or contain record level data just as in an ERP system (structured), log file or XML files (semi-structured). Hadoop is used in big data applications that have to merge and join data – click stream data, social media data, transaction data or any other data format. Hadoop can be used to build an enterprise data hub for the future.
Why use Hadoop?
Hadoop is used where there is a large amount of data generated and your business requires insights from that data. The power of Hadoop lies in its framework, as virtually most of the software can be plugged into it and can be used for data visualization. It can be extended from one system to thousands of systems in a cluster and these systems could be low end commodity systems.
The cost savings with Hadoop are dramatic when compared to the legacy systems. It has a robust community support that is evolving over time with novel advancements.
What is Hadoop used for?
Hadoop has become the go-to big data technology because of its power for processing large amounts of semi-structured and unstructured data. Hadoop is not popular for its processing speed in dealing with small data sets.
Hadoop has also given birth to countless other innovations in the big data space. Apache Spark has been the most talked about technology, that was born out of Hadoop. Hadoop and Spark is the most talked about affair in the big data world.
Hadoop offers a scalable, flexible and reliable distributed computing big data framework for a cluster of systems with storage capacity and local computing power by leveraging commodity hardware. Hadoop follows a Master Slave architecture for the transformation and analysis of large datasets using Hadoop MapReduce paradigm.