5 top trends driving big data analytics

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With the advent of digitization, big data analytics has become an established part of doing business across the globe.

Most organizations now look to big data as a mainstream practice to evolve new technological advancements. They are constantly looking for upcoming tools and deployment models to overcome the big data challenges.

Big data analytics is one of the key pillars of enabling digital transformation and devising solutions to solve business issues across various industries globally. But big data management and analytics are evolving at a rapid pace. Organizations need to quickly adapt to the evolving and ever-changing information management environment or they face the risk of being left behind the competition.

Here are five top trends that will continue to drive big data analytics into 2019.

Predictive Analytics

With big data, business analysts not only have a humongous amount of data to handle but also the need to manage the huge bulk of records comprising of a variety of attributes. Analysts can explore new behavioral data by combining big data and computational power.

Traditional machine learning tends to use statistical analysis on the basis of a total dataset sample. By bringing in reasonable computational power to a problem, you can analyze a huge number of records consisting of an enormous number of attributes per record and this considerably increases predictability too.

Deep Learning

Big data uses advanced analytic techniques such as deep learning methods to manage diverse and unstructured text. Deep learning includes of a set of machine learning methods which are based on neural networks. It has great potential to solve business problems and empowers computers to manage large quantities of unorganized and binary data.

In-memory Database

To speed up analytical processing, the use of in-memory databases is increasing exponentially and this proves beneficial to many businesses. Various enterprises are encouraging the use of Hybrid Transaction/Analytical Processing (HTAP) by allowing analytic processing and transactions to stay in the same existing in-memory database.

As HTAP is extremely popular, some organizations are using it repetitively on a massive scale by putting all transactions together from different systems onto one database only. By doing this they are able to manage, protect, and assess how to integrate diverse datasets.

Use of Hadoop

Enterprise data operating systems and distributed big data analytics frameworks such as MapReduce are used to perform various analytical operations and data manipulations and store the files into Hadoop.

As a practice, organizations use Hadoop as the distributed file storage system for holding in-memory data processing functions and other workloads. One key benefit of using Hadoop is that it is low -cost and is used as a general purpose tool for storing and processing huge datasets and is treated as an enterprise data hub by many organizations.

Data Lakes

A storage repository which holds an enormous amount of raw data in its native format is known as a data lake. There are hierarchical data warehouses which store data in files or folders, but a data lake tends to use a flat architecture to store data.

A data lake is like an enterprise data hub. It not only stores a vital amount of data but also includes high-definition datasets which are incremental for building a large-scale database. In general, big data lakes are being used only by highly-skilled professionals for analysis purposes only.

With the emergence of big data analytics trends globally, organizations are trying to create flexible conditions for business analysts and scientists to work. This has helped the skilled workforce to stay one step ahead and evaluate, experiment and integrate the best technologies and frameworks into the business and benefit the entire fraternity as a whole.

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