Distributed Storage Cluster and Hadoop

How big giant MNC's like Google, Facebook, Instagram, etc stores, manages, and manipulates thousands of Terabytes of data with High Speed and High Efficiency.

The story starts with the buzzword Big data, which refers to a large volume of data that is large, fast, or complex and with different variety, variability, veracity – both structured and unstructured. The importance of big data doesn’t revolve around how much data you have, but what you do with it.

Sources of big data: Streaming data(Youtube, Twitch, Netflix), Social media(Twitter, Facebook), Publicly available data(US government and other agencies data), others come from data lakes, cloud data sources, suppliers, and customers.

How to Access, Manage, and Store Big Data:

Modern computing systems like Distributed Storage Cluster and Hadoop provide the speed, power, and flexibility needed to quickly
manages and manipulate Thousands of Terabytes of data with High Speed and High-Efficiency. Along with reliable access, companies also need methods for integrating the data, ensuring data quality, providing data governance and storage, and preparing the data for analytics. Some data is stored on local premises in a traditional data warehouse and data center – but there are several flexible, low-cost options for storing and handling Big Data via Cloud solutions, Data Lakes, and Hadoop.

Big Data analytics is the technique with which companies gain value and insights from data. With the coming of AI, Augmented Analytics, and Augmented Data Management the problem can be solvable and be managed easily.

Tip:
follow https://twitter.com/SubhashTyler for creating Hadoop master-slave architecture or Distributed Storage Cluster on your system in the next blog.