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Big data is about data mining as much as it is about data analytics. Hadoop is an open-source framework that data scientists invariably depend on. Using simple programming models, it can store and processes big data in a distributed environment, all while scaling up from single servers to thousands of machines with local computational and storage functionalities. Hadoop professionals are in great demand given its wide range of applications in the data science domain. Here are the top 10 Hadoop tools for big data analytics to know in 2022.
A unified analytics engine can perform data processing functions hundred times faster. It uses the MapReduce model for operations like interactive queries, stream processing, etc. Its unique in-memory data processing feature for batch, real-time, and advanced analytics.
Map Reduce is highly scalable, the reason why it is used for applications that process huge datasets across thousands of nodes on a Hadoop cluster. Using Hadoop, a MapReduce task can be segmented into different sub-tasks.
A highly secure platform integrated with Hadoop works as a native analytic database for Apache Hadoop. With Impala it is easy to retrieve data stored from HDFC or HBase in real-time. Analytics gets much easy with BI and Hadoop integration.
A data warehousing tool developed by Facebook to analyze and process large data. Hive Query Language is used here to process big data, bypassing MapReduce jobs. A command line tool called Beeline shell and JDBC driver is all required to interact with Apache Hive.
An open-source framework works with Hadoop infrastructure at its background to process huge volumes of data. It is generally used for implementing scalable machine learning algorithms using the Hadoop library to scale in the cloud.
Pig's uniqueness lies in its extensibility to perform specific purpose processing. Developed by Yahoo to lessen the burden on MapReduce, is used for the pig framework to run on Pig runtime.
HBase is an open-source NoSQL that comes with scalable storage, is fault-tolerant, and supports real-time search on sparse data strewn across billions of rows and columns. Designed along the lines of Google's big table, it is used primarily to pull information from large data sets.
It is a command-line interface, mostly used to move data between Hadoop and structured data stores or mainframes. It imports data from RDBMS and stores it in HDFS, transformed it into MapReduce, and is sent back to RDBMS. It comes with a data export tool and a primitive execution shell.
A real-time data processing tool is similar to Hadoop when it comes to real-time processing. Its uniqueness lies in its ability to take continuous message streams and generate output in real-time.
It is a distributed system used for streamlining data processing operations over a flexible architecture that is easy to use and works on data streams. Because of its failover and recovery mechanism, it highly faults tolerant.
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