With more businesses making the switch to Big Data for storing, processing, and extracting value from data sets already this year than the same time last year, we look at the current top 5 trends of 2017
1.Big Data becoming faster and accessible
Hadoop has historically faced certain shortcomings in as much as it was not able to fully support interactive SQL. SQL has been the traditional means through which organisations access Hadoop. The need for SQL has necessitated the take up of faster databases like MemSQL, Cassandra, and Kudu. With these boosters, Hadoop is becoming as beneficial as the more embedded warehouses.
2. Within Data Science, Hadoop is now more than a batch-processing platform
Hadoop has now established itself as a multi-use engine that can undertake ad hoc reporting in addition to the daily operational reporting that has conventionally been the domain of data warehouses.
Hadoop is beginning to overcome its limitations by introducing user friendly interfaces and business intelligence capabilities. The Internet of Things will mean that there will be much more data (both structured and unstructured) which will be stored and transferred over the cloud. Big Data and Hadoop will need to increase their speed to meet this challenge.
3. Big Data is outgrowing Hadoop
Due to changing and expanding needs of business to access data from a variety of sources, Big Data will no longer stay solely with Hadoop. Organisations that do rely solely on Hadoop will need to access different tools and infrastructures to undertake any advanced analytics.
An example of an emerging technology is Apache Spark. It has become a complete one-stop-shop. Although in its infancy, the Apache Spark has a distinctive in-memory facility to support a range of data processing tasks. This allows for greater analytics and insights.
4. Deep Learning Algorithms continue to add value.
Deep Learning Algorithms continue to advance at a significant pace. This is increasing the value of Big Data. The existing capability allows it to identify patterns in visual and audio data. But great advancement is being made to allow it to understand to a greater degree as to what is going on in, for example, a video. The follow on from this is that the technology will allow it to take corrective action.
5. Reliability of BlockChain technology.
BlockChain technology is needed to mitigate the challenges posed by Big Data, namely trust around data integrity. Moreover, it also must be non-corruptible (most notably by human actions). And this is where BlockChain technology come in.
Blockchain technology adoption is increasing at a significant rate as business invest in security. This is only likely to continue.