10 Big Data Possibilities for 2017 Based on Oracle's Predictions

2017 will see a host of informed predictions, lower costs, and even business-centric gains, courtesy of the global adoption of Big Data and associated technologies.

2017 is already upon us, and Big Data seems to be growing in leaps and bounds. Be it the exteriors of IoT or the more intricate aspects of cloud computing, enterprise technologies are on the way up, facilitating dramatic transformations.

Many companies are embracing Big Data as the newest fad, mainly as an advantage in this competitive era. In this post, we will be talking about some of the predictions made by Oracle concerning Big Data and its future in 2017.

1. Embracing the Era of Machine Learning

Machine learning was previously restricted to data scientists, but 2017 will bring it out into the open. Be it Google’s newest ranking algorithm or electronic gadgets par excellence, machine learning will find a foothold to work with. Big Data was pretty big in 2016 and is expected to grow bigger in the existing year, with machine learning at the hindsight.

Be it an array of tools for business analysts or back-end benefits, machine learning will be making a few inroads in an otherwise monotonous domain of Big Data. This will change the way governments and enterprises handle data sets across physical and virtual servers. Prospective areas of change will include healthcare automation and energy.

2. Cloud-Data Cohesion

Big Data has always been known to respond well to cloud-based servers, but 2017 will amplify its reach. Be it privacy issues concerning cloud adoption or data sovereignty, things are expected to improve. With bigger data sets in the picture, most enterprises might shift to virtual servers because of the ambiguities associated with relocations.

Bringing cloud to data is what looks like a prospective change in 2017 as compared to shifting data to the cloud. Cloud strategies specific to data requirements will be of paramount importance.

3. Data-Driven Applications

Big Data technologies were previously known for their impact in the field of Information Technology. However, recent trends have guaranteed a higher adoption rate for a host of analytic and even entrepreneurial applications. Be it a wide-array of AI-powered applications or streaming clients like Megabox, every enterprise will soon be making that Big Data shift — along with their futuristic applications.

4. IoT and Its Integration

Internet of Things received a lot of criticism owing to the barrage of absurdly designed gadgets. As much as we second the lack of innovation in IoT, Big Data might just revive the same, courtesy of high-end intuition. Be it mobile-centric applications or household gadgets, pairing IoT with Big Data is expected to be a revolutionary step in 2017.

IoT application development will be a lot simpler and the impacts (or rather, ripples) will be felt even at a distance. We are looking at smart cities and even smarter nation-wide projects.

5. Data Virtualization: A Reality

When it comes to entrepreneurial charades, the proliferation of data silos is common. Be it working with the likes of NoSQL, Spark or even Hadoop, databases will surely get a boost in 2017. It must be known that dark data sets are often hard to access as organizations fail to identify the perfect repositories for the same. Unified access, an elusive entity, will get a boost in 2017 courtesy the emergence of data virtualization.

This approach will render steadfastness to analytics and Big Data adoption, as data movement is no longer necessary.

6. Working With Kafka

Big Data predictions feel incomplete with the mention of Kafka, a technology put forth by Apache. While Kafka is already growing in leaps and bounds, it might just peak by the third quarter of 2017. To be exact, Kafka is expected to be the much-awaited runway for the Big Data technology.

Otherwise a bus-styled technology, in terms of architecture, Kafka can easily handle data structures and even myriad data sets — focusing largely on the data lake and its proliferation and facilitating subscriber access.

7. Boom in Cloud Data Systems (Prepackaged and Integrated)

Building a conventional data lab is difficult and that too from the scratch. However, organizations are increasingly becoming reliant on Big Data, facilitating the growth of integrated cloud data systems. These are pre-packaged entities including data science, analytics, data wrangling, and even the complexities of data integration.

2017 will witness a steady growth in the adoption of pre-packaged cloud systems dedicated to Big Data reservoirs.

8. An Alternate to the Hadoop HDFS

Hadoop’s HDFS has long been the most sought-after data accommodation platform, but object stores are expected to trump the same in 2017. The reasons for the same are better data replication, availability, and backup.

Moreover, feasibility is a bonus when Object Stores are concerned. These are repositories to Big Data based on the same data-tier technology as the HDFS.

9. Deep Learning Even at the Cloud Level

As mentioned, data virtualization will now be easier sans added layers. This approach will, therefore, boost a host of acceleration technologies including NVMe and even GPUs. In 2017, we will also get to see deep learning joining hands with Big Data metrics. Visible results will include nonblocking, high-capacity, improved I/O, and even better network performances.

10. Hadoop Turns Vital

Users and companies looking to leverage Big Data were using Hadoop sparingly but in 2017 we might see multi-level deployment in every possible, Data-centric project. Hadoop security will come across as a non-optional entity and would require possible applications— in every field.

Bottom Line

Big Data is on a rampage and the growth scale is absolutely second to none. However, with the emergence of IoT and even social media, snappier Big Data applications have received overwhelming responses.

In 2017, we will surely be seeing a host of informed predictions, lower costs, and even business-centric gains, courtesy of the global adoption of Big Data and associated technologies.