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Industries Facing Big Data Challenges And Their Solutions

Big Data Challenges

Solutions to Big Data Challenges

Clustering Technology

The relentlessly growing big data is stored on a large number of small inexpensive machines. Thus, there is an inevitable challenge of machine failure at some point or another. Hence would entail a loss of data stored on it. The data is stored on multiple machines, which would guarantee the availability of at-least one-copy. The Google using Hadoop Distributed File System, a well-known clustering technology for Big Data store billion of pages and sort them to answer user search queries.

Stream Processing Engine

The chances are that torrential streams of data may be received. To handle an unlimited number of channels to create a queuing system. They will hold data from where customer applications can request and process at their own pace. The system would also send data in batch processing and stream processing directions. With ApacheSpark streaming applications, a stream processing does it work simultaneously with batch processing.

NoSQL Database System

Storing a variety of data in a format which can give fast and easy access, also various functions can work on it. Hence NoSQL database systems used to store big data. Various format variants available for NoSQL like key-pair, document format, Pig and Hive.

Parallel Processing

The massive amount of data would move from clustered machines to the processing machines. This chokes network, and hence to avoid these tasks are distributed to machines which work in parallel and finally consolidated result is delivered. Google uses MapReduce for parallel processing for distributed big data. Resource manager YARN monitors the resource usage and balance load to the clustered machines.

Sectors Facing Big Data Challenges

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