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Big Data Trends for Analytics

Published on 25 December 18

The disciplines of big data and analytics are quickly evolving that put the businesses on the risk of being left behind. In recent years, many technologies have emerged that drive solution in a matter of weeks or months. One of the notch one among it is the Big Data Analytics. Every day we deal with a massive amount of data that gets generated by a structured or unstructured format like spreadsheets, XML, audio, video etc. Clearly, following some of the big data trends can help shape your business and keep it going forward.

Analytics in Cloud

Initially, to work on clusters of physical machine Hadoop was designed as a framework and set of tools for processing extra-large sets of data. As time passed, the focus was shifted on the cloud systems considering it as the future state of big data for hybrid solutions. Amazon Redshift is one such example for a cloud-based data warehouse that helps to associate with the sales and customer demographic data, real-time behavioral data and much more. Cloud systems are appraised by all as it is very much cheaper to buy the virtual machines rather than physical for use.

Hadoop and SQL

To distribute resources that are gradually turning into the general-purpose data operating system, the analytics framework namely Map Reduce has evolved. It is similar to the SQL which is an in-memory stream processing, graph analytics and different types of workloads that are able to run on Hadoop with efficient performance to attract more businesses to utilize it as an enterprise data hub.

Hadoop is the best option for the language structure format when combined with the SQL. Apache Hive offers SQL like query language for Hadoop and also few commercial alternatives like Cloud era, Pivotal Software and IBM are getting faster all the time. SQL on Hadoop will not replace the warehouses but it definitely provides alternatives to more costly software appliances for analytics.

Deep Learning and In-Memory Analytics

A set of machine learning techniques based on neural networking is defined as Deep Learning which recognizes items of interest in large quantities of unstructured and binary data and to deduce relationships without a need of specific model or programming instructions. Using in-memory databases to speed up the analytic process is popularly increasing and beneficial for the businesses that are leveraging on Hybrid Transaction/Analytical Processing (HTAP) that allows transactions and analytic process to reside in the same in-memory database.

Regardless of any trends or hot technologies, the businesses are always looking forward to finding better, less expensive and affordable solutions. Any business can benefit from Big Data considering that the data is a most crucial asset to understand the market appetite for advanced and improved decisions of the future.

These are just the emerging trends which impact the big data and analytics services and trends for businesses irrespective of types. So start capitalizing on these opportunities what are you waiting for?

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