Top 5 Up-And-Coming Big Data Trends for 2018

Top 5 Up-And-Coming Big Data Trends for 2018

The big data market is constantly growing and evolving. It is predicted that by 2020 there will be over 400,000 big data jobs in the US alone, but only around 300,000 skilled professionals in the field. The constant evolution of the big data industry makes it quite difficult to predict trends. However, below are some of the trends that are likely to take shape in 2018.

Open source frameworks:

Open source frameworks like Hadoop and Spark are dominating the big data realm for quite some time now and this trend will continue in 2018. The use of Hadoop is increasing by 32.9% every year- according to Forrester forecast reports. Experts say that 2018 will see an increase in the usage of Hadoop and Spark frameworks for better data processing by organizations. As per TDWI Best Practices report, 60% of enterprises aim to have Hadoop clusters functioning in production by end of 2018.

As Hadoop frameworks are becoming more popular, companies are looking for professionals skilled in Hadoop and similar techs so that they can draw valuable insights from real-time data. Owing to these reasons, more and more candidates interested to make a career in this field are going for big data Hadoop training.

Visualization Models:

A survey was conducted with 2800 BI experts in 2017 where they highlighted the importance of data discovery and data visualization. Data discovery isn’t just about understanding, analyzing and discovering patterns in the data, but also about presenting the analysis in a manner that easily conveys the core business insights. Humans find it simpler to process visual patterns. Hence, one of the significant trends of 2018 is development of compelling visualization models for processing big data.


Streaming success:

Every organization is looking to master streaming analytics- a process where data sets are analyzed while they are still in the path of creation. This removes the problem of having to replicate datasets and provides insights that are up-to-the-second. Some of the limitations of streaming analytics are restricted sizes of datasets and having to deal with delays. However, organizations are working to overcome these limitations by end of 2018.

Dark data challenge

Dark data refers to any kind of data that is yet to be utilized and mainly includes non-digital data recording formats such as paper files, historical records, etc. the volume of data that we generate everyday may be increasing, but most of these data records are in analog form or un-digitized form and aren’t exploited through analytics. However, 2018 will see this dark data enter cloud. Enterprises are coming up with big data solutions that enable the transfer of data from dark environments like mainframes into Hadoop.

Enhanced efficiency of AI and ML:

Artificial intelligence and machine learning technologies are rapidly developing and businesses are gaining from this growth through use cases like fraud detection, pattern recognition, real-time ads and voice recognition. In 2018, machine learning algorithms will go beyond traditional rule-based algorithms. They will become speedier and more precise and enterprises will use these to make more accurate predictions.

These are some of the top big data trends predicted by industry experts. However, owing to the constantly evolving nature of big data, we should brace ourselves for a few surprises too!

Big data is shoving the tech space towards a smarter future and an increasing number of organizations are making big data their top priority. Take advantage of this data-driven age and enroll for big data Hadoop courses in Gurgaon. At DexLab Analytics, industry-experts patiently teach students all the theoretical fundamentals and give them hands-on training. Their guidance ensures that students become aptly skilled to step into the world of work. Interested students can now avail flat 10% discount on big data courses by enrolling for DexLab’s new admission drive #BigDataIngestion.




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July 19, 2018 11:12 am Published by , , , , , , , , ,

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