Tools and Technologies Required for Big Data

CIO Review Europe | Tuesday, November 23, 2021

Fremont, CA: Big data analytics is the process of analyzing different types of big data sets to extract actionable information. Big data analytics are used to uncover hidden patterns, market trends, and consumer preferences to make organizational decisions. There are several steps and techniques involved in big data analytics.

Non-Relational Databases

Even the databases that store these vast data sets have changed how and where data is stored. Currently, the preferred protocol for storing big data is JavaScript or JSON object notation. JSON allows you to write actions at the application level and improves cross-platform functionality. This gives you the flexibility to develop scalable and flexible data processing solutions for developers. Many companies use XML as a replacement for structured data transfer between servers and web applications.

Hybrid Data Storage and Processing Systems—Apache Hadoop

Apache Hadoop is a hybrid storage and data processing system that provides scalability and speed for small businesses at an affordable cost. It uses the Hadoop Distributed File System (HDFS) to store large files on multiple machines known as cluster nodes. Hadoop has a replication mechanism that ensures smooth operation even in the event of individual node failure. Hadoop uses Google MapReduce parallel programming at its core. The name comes from big data processing algorithms’ “mapping” and “reducing” functional programming language. MapReduce works by increasing the processing power of individual nodes instead of increasing the number of functional nodes. In addition, Hadoop has grown significantly in development and popularity as it can run using readily available hardware.

By considering these two aspects of big data, businesses stand to unlock a greater value from big data analytics. And it would surely catalyze a new era of big data analytics.

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