2016 was a landmark year for big data with more and more organizations switching to big data for storing, processing, and extracting value from data. In 2017, systems that support large volumes of both structured and unstructured data will continue to rise.
1.Big Data becomes Faster and Easier.
With all the buzz that has been surrounding Big Data and Hadoop technology over the major advantages they have which includes performing sentiment analysis and machine learning with the support of AI, Hadoop still had certain shortcoming which was the ability to support interactive SQL. SQL has of course been the means through which business users access Hadoop faster for exploratory analysis. This need for SQL fueled the adoption of faster databases like MemSQL and Cassandra, Hadoop-based stores like Kudu. With these boosters, Hadoop will now become as powerful as traditional warehouses with respect to use of SQL and advanced analytics
2.Big Data will grow more than just Hadoop in 2017.
With growing need of organizations to access data from various sources ranging from cloud warehouses, to structured and unstructured data sources, Big data will no more remain just with Hadoop. Businesses that rely only on Hadoop will have to use a variety of tools and infrastructures to perform advanced analysis and find answers to some critical questions. They will need data preparation tools, data cleansing tools, predictive analysis and various other analytical algorithms and so on so forth.
The Apache Spark, although it is in its primitive stages it has by far evolved to be a complete package to meet all these requirements.Apache Spark has a unique in-memory capability that supports a wide variety of data processing workloads.This in-memory storage enables applications low latency computation and to implement efficient iterative algorithms. In 2017 Customers will demand analytics and insights of all sizes and types of data. And so only those platforms which are capable of evolving to fulfill these needs will rise and continue to grow with Big Data.
3. Hadoop will no longer be just a batch-processing platform for Data Science.
Hadoop has now become a multi-purpose engine to perform ad hoc reporting. Hadoop can also perform operational reporting on day-to-day workloads which were earlier looked after by traditionally data warehouses. In the years to come, Hadoop will conquer all its shortcomings and be equal in power to probably be able to replace the existing and age-old techniques of reporting with an extremely easy user interface and self-service BI capabilities. Hadoop will create new opportunities for self-service analytics. In the years to come everything will become sensor controlled and everything will have its convergence from IoT which generates massive amount of structured and unstructured data which will be deployed and stored over cloud, as a result of which Big Data and Hadoop tools will have to fasten their speed to meet the growing demands for analytical tools that seamlessly connect to and combine a wide variety of cloud-hosted data sources.
4. Deep Learning Algorithms will Add Value to Big Data
Big Data can get even more valuable in the years to come if Deep Learning Algorithms continue to grow the way they are right now. Already Deep Learning Algorithms have the capabilities to recognize patterns in the video, audio, speech, image and other non-textual data. Well having said this there is a lot more that Deep Learning Algorithms can do! It will be no wonder if one day along with recognizing the image or objects these algorithms will be able to understand what is happening in the image or video and probably be able to have a human-like brain to analyze and take actions according to the situations.
5. Last but not the Least “The BlockChain Technology promises”
Have you all wondered where does Big Data come from? And Can this data be trusted? And how do we ensure that this Big Data is not a Big Bad Data? With the amount of data that is being generated from heterogeneous sources and the rate at which it is being generated the probability of this data being erroneous has also gone up to great extent. So what can we do to ensure that this powerful capability? We have to aggregate terabytes of data is, in fact, producing correct big data? It would be so nice if we never had bad data entering our databases. All these possibilities can come true if BlockChain Technologies stands by its promises.
Today, as a matter of fact, we all know that the Internet is a global repository of information but the need of the hour is that we need a global and a secure ledger of truth. A ledger that is not corruptible by any human fraud or is not subjected to any manipulation by any group, corporation, company, or even government for that matter. And this ledger is BlockChain Technology.
Blockchain Technology will basically help all industry where digital transactions are involved, this ranges from the financial industry to the legal industry to the real estate to the notaries, to gambling or even to publishing to data storages. In 2017 there will be a wider adoption of the blockchain technologies as most of the banks have already started investing and experimenting blockchain technology to ensure secure transactions.
Well, we hope this snippet of some predictions of Big Data Trends in 2017. It help your organization to invest in right things at the right time and be up front in this race of emerging trends and technologies. Stay tuned for more insights on Big Data and its related ecosystem at BistaSolutions.com.You can also get in touch with us through email@example.com and write to us at firstname.lastname@example.org.