Making Best Use Of Big Data Using Analytic Tools

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Big data refers to data sets that are high in volume, velocity and variety. The data is so enormous that traditional software that we are used to are not capable of collating, processing, storing and managing.

Big data has a group of interwoven components that allow companies to collect, analyze, interpret data and make decisions practically. These components are referred to as the tools and technologies from which different problems (business etc.) are proffered solutions to.

The arrangement of the data can be unstructured, structured, semi-structured and more. Plus, the data sizes are often large. Therefore, technologies are developed to handle, process and help IT personnel to make reasonable evaluation and for companies to make quality decisions on the data.

The tools we’d be considering are the backbone of big data and why big data analytics has been of great relevance to our world.

Spark

Also called Apache Spark. It has affinity with the Hadoop ecosystem tool. Spark is one of the open source technology frameworks that can be deployed in several ways. The mono-framework of Apache Spark functions as engine o big data processing within Hadoop.

Apache Spark supports other tools such as stream data, machine learning, graph processing and also SQL computing. It nativity is good as it supports binding for R programming, Anaconda Python distro, Scala and Java.

Hadoop

Hadoop ecosystem is popularly known with big data. In fact, to learn big data, one has to incorporate Hadoop first before progressing to other tools. Hadoop has mono framework for distribution and scaling.

Apache Hadoop library enables distribution of large data processes across series of computer clusters by using simple programing. It can scale up from single server to multiples with each providing local computation and storage.

Hadoop comes with some project modules for big data analytics – such as Hadoop Common, Hadoop Distribute File System, Hadoop YARN, Hadoop MapReduce, all for fantastic big data analytics.

Data lakes

With data lakes, big data content are stored. Data lakes hold enormous volume of raw data in the format they are so that analysts can make use of them when looking to make decisions.

Data lakes as storage repositories are developed to make large amount of data available for users whenever the need arises. Its development is the initiatives and growth of Internet of Things (IoT) and digital transformation.

IMDB

In-memory Database as the name implies is a database management system. IMDB relies primarily on main memory for data storage. It doesn’t store data using optimized disk, rather it picks on the main memory for storage and the main memory storage is key to big data analytics usage, data marts, and data warehousing.

NoSQL

This is a database that permits storage of data as well. NoSQL is advancement on conventional SQL which are made for ad-hoc queries and transaction. The conventional SQL databases have limitations that make them unsuitable for some applications.

These limitations are what NoSQL databases have overcome. Another limitation faced by the conventional SQL database is the capacity to scale across multiple servers at the same time. But, NoSQL is fortified allowing horizontal scaling across thousands of servers. Compared to SQL databases, NoSQL databases can manage and store data in greater speed and flexibility.

As long as these tools are being developed from time to time, big data development will always have a backbone to fall back on, enabling us to meet up with large increases in data and to have more standard information on big data analytics.

Kevin Roger is the innovator of TecArticles. He is a web engineer and graphic designer. Also he loves Open Source. Apart from Technology, he loves travelling and photography.

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