5 Trends to Watch in Big Data Analytics

Big data and analytics is a fast evolving field. New technologies are emerging at a rapid pace. In fact, if enterprises and businesses do not keep tab on the latest technological developments, they may risk being left behind. So, as the world tries to keep pace with the evolving technologies and trends, we bring to you the emerging technologies that must be on your watch list.

Mentioned here are the 5 trends in Big Data analytics that you must keep a close watch on:

Big Data and Cloud Analytics

Technologies today are focused on finding ways to process data in the cloud. A number of technologies have already achieved success in this arena and various evolving technologies are also concentrating on this aspect. Among the technologies that are available for processing data in the cloud are IBM’s Bluemix cloud platform, Amazon’s Redshift hosted BI data warehouse and Google’s BigQuery data analytics service.

Though the initial tools for Big Data were designed for working on groups of physical machines, that concept has undergone changes. Now tools are being designed that will be suitable for both, on-premises and cloud. As businesses move towards adopting cloud analytics in a bigger manner, the need is to find ways to make cloud analytics cost effective enough to move all data to cloud.

Hadoop as a Data Operating System

Apache Hadoop

Hadoop, the core platform for structuring Big Data, is taking up the role of a data operating system. As distributed analytic frameworks are growing into distributed resource managers, they are converting Hadoop’s role as well. The fact is that various works of data analytics can be carried out by attaching these analytic frameworks to Hadoop.

The point to note is that as more and more workloads, such as stream processing and graph analytics acquires the ability to run on Hadoop without compromising on their performance, Hadoop will continue to gain importance as enterprise data hub.

Enterprise Data Lake

In a typical database theory you are required to design the data set before any data can be entered into it. However, in case of an enterprise data lake, also known as Big Data Lake, the strategy adopted is the exact opposite of this one. As per the enterprise data lake model, data sources need to be simply dumped into a Hadoop repository without having to design the data set first.

Enterprise Data Lake provides the tools through which data can be analyzed. It also allows a user to know what data is present in the lake. However, there is still a long way to go as far as enterprise Data Lake is concerned. It must possess qualities that are similar to traditional databases, such as monitoring access control and providing security to the data.

Predictive Analytics

Big Data brings along with it technologies that can handle a huge volume of data and process such data with varied attributes. This enhances the predictability ratio. The use of Big Data in combination with computational power increases the scope for analytics and decreases the dependency on speed and memory.

The challenge in this scenario is to generate real-time analysis as well as predictive analysis out of one single Hadoop core. Hadoop is bogged down by issues of speed, but there are several other data processing engines and query tools such as SQL query and Spark SQL that can be used to achieve better performance.

NoSQL Database

NoSQL or ‘Not Only SQL’ is a database that is being used as an alternative solution against conventional SQL- based databases. As the popularity of NoSQL continues to increase, data analysts are using it in various kinds of analytic applications to achieve the desired results or to derive insights.

Though open-source SQL databases are still there, the need for different types of analysis is making several data analysts prefer NoSQL. Moreover, along with the ability for high-performance, NoSQL is also lightweight. As such, this unconventional database is becoming a preferred choice for many people who deal with Big Data and analytics.

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