What are the trends for Big data in this year?


What are the trends for Big data in this year? 

This year we will see a democratization of data throughout the enterprise. It is considered that data will become a commodity that is not just kept in only one department alone. It will also be used purely by senior company leaders. Big data requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex, and of a massive scale. Here we have mentioned and explained the trends of Big data in this year. 

No Data Scientists

As we are becoming more and more data driven, one of the main aspects that has become clear is that finding the necessary talent has become difficult. It means that companies are often reliant on either too few staff or outsourced consultants. Therefore, this year is likely to see more automated platforms that can enable employees, who may not have as much skill with data as others, to collect, analyze and make decisions based on this data. With new technology allowing metrics to be tracked across more areas and wearables creating even more possible trackable actions, deeper customer understanding is inevitable. Business users wish to reduce the time and complexity of preparing data for analysis, something that is especially important in the world of big data when dealing with a variety of data types and formats. Nevertheless, self-service data preparation tools are also increasing.

Sensor Driven Data

Big data has seen a huge leap forward in the past years regarding how it has been used across companies. The IOT is evolving, since more companies have opted to use this. This would be sensor-to-sensor data being collected, collated and analyzed through purely sensor based collection. This can be done in multiple ways from the way that objects are interacting with another object, to the settings that people are using on particular devices. Sensor based data is still to see a marked increase. It is likely that we may see more device-to-device data being created and collected in the year.

NoSQL takeover

The NoSQL companies like MongoDB, DataStax, Redis Labs and MarkLogic, are set to outnumber the traditional database vendors in Gartner’s Leaders quadrant of the report. But over the year, NoSQL technologies, commonly associated with unstructured data, have seen significant adoption. The shift to NoSQL databases as a leading piece of the enterprise IT landscape becomes clear as the benefits of schema-less database concepts become more pronounced. Gartner’s Magic Quadrant for Operational Database Management Systems shows, in the past it was dominated by Microsoft Oracle, IBM, and SAP.

Hadoop and MPP data warehouse growth

With Hadoop gaining more traction in the enterprise, there will be a growing demand from end users for the same fast data exploration capabilities they’ve come to expect from traditional data warehouses. Experts mention ninety percent of companies who have adopted Hadoop will also keep their data warehouses and with these new cloud offerings, those customers can dynamically scale up or down the amount of storage and compute resources in the data warehouse relative to the larger amounts of information stored in their Hadoop data lake. The trend of Hadoop becoming a core part of the enterprise IT landscape, investment will grow in the components surrounding enterprise systems such as security.

But there is a major shift in the application of this technology to the cloud where Amazon led the way with an on-demand cloud data warehouse in AWS’s fastest growing service Redshift. Now Redshift has competition from Google with BigQuery, offerings from long time data warehouse power players such as Microsoft (with Azure SQL Data Warehouse) and Teradata, along with new start-ups such as Snowflake, winner of Strata + Hadoop World 2015 Startup Showcase, also gaining adoption in this space. To meet that end-user demand, adoption of technologies such as Cloudera Impala, AtScale, Actian Vector and Jethro Data that enable the business user’s old friend, the OLAP cube, for Hadoop will grow – further blurring the lines behind the “traditional” BI concepts and the world of big data.

Apache Spark lights up Big data

According to Matei Zaharia, Spark originator and co-founder of Databricks, Spark provides dramatically increased data processing speed compared to Hadoop and is now the largest big data open-source project. From a being a component of the Hadoop ecosystem, Apache Spark has moved to the big data platform of choice for a number of enterprises.

In-Memory Databases

As the use of data has increased in the past years, the speed at which results are needed has grown with it. When this hasn’t been the case, people want to be more informed than before or have the ability to make decisions in real time, rather than through the use of reports reporting on historical data. In-Memory databases allow companies the freedom to access, analyze and take actions based on data much quicker than regular databases. This in turn means that either decisions can be made quicker as data can be analyzed faster or more informed as more data can be analyzed in the same amount of time.

Big data, IoT and Cloud will come together

It is considered that the data from devices in the Internet of Things (IoT) will become one of the “killer apps” for the cloud and a driver of petabyte scale data explosion. For this reason, leading cloud and data companies like Google, Amazon Web Services and Microsoft will bring IoT services.

 ( Courtesy : Information-Age and  Theinnovationenterprise)

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