Data has become the new currency and the technologies now revolve around the concept of putting the big data into work and increase ROI through enhanced productivity and minimal risk. The year 2017 continues to witness massive growth of data both in terms of volume and variety. And with this kind of growth, we will see a simultaneous rise of systems catering to processing data in more real-time.
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We organize Big Data trends into two categories: technology trends and business trends.
- 1 Big Data Business Trends
- 2 Big Data Technology Trends
Big Data Business Trends
Using Big Data to enhance CX
This year will focus on enhancing CX with the help of big data, by moving its way from legacy to the vendor systems with hard core system upgrades. It aims at doing so by analyzing data with self-service flexibility and deriving insights about the ongoing trends. People would be using the big data analysis to interpret the behavior of the customers and thereby enhancing customer experience and increasing the revenue by reducing the churn.
Deep learning revolves around machine learning based on neural networking and imbibes great potential in solving business problems. It aids the computer to identify the items of interest in unstructured data of massive volumes to deduce relationships without specific programming instructions.
The algorithms cater to the domain of artificial intelligence mostly, which has the ability to observe the patterns and gauge and make decisions for complex problems. So deep learning is mostly helpful for learning from massive volumes of structured as well as unstructured data and extracting meaning from them and patterns from big data. So businesses and organizations are bound to pay more attention to unsupervised training algos to take care of the heavy influx of the data.
Data-as-a-Service (Self-service analytics platform)
Self-service data prep will be mainstream in the upcoming year. The end users play a major role in shaping the big data. The biggest challenge facing the world of big data is to make the Hadoop data accessible to the business users. However, a step towards achieving this goal has already been taken in the form of self-service analytics platform. Business users though want to minimize the time and the complexity of the process of preparing the data. Agile self -service data preparation tools not only helps in data prep at the source but at the same time makes the data accessible in the form of snapshots for quick and lucid exploration. Theses tools are minimizing the barrier for late Hadoop entry and will gain traction in 2017.
We can expect a lot of government interference in the way data is going to be handled in the coming year. Government scrutiny will be done on each and every data that are being used by the companies and various government departments. With the ever increasing variety and volume of data there has been a constant rise in the rate of cyber attacks, so governments will have a hand in this big data concept in 2017, now all that we are waiting for is how this will be done and how it will impact us in the coming years.
Big Data Technology Trends
Speed up Hadoop
This year will witness a surge of organizations who will be willing to adopt big data stuff, Hadoop, and myriad Hadoop Solutions. With Hadoop, organizations of any size will be able to process large volume and variety of data using advanced analytics to dig valuable information and use the same to make profitable decisions.
However, speed has become an integral part of everything nowadays so adoption of faster databases like MemSQL, Exasol and other Hadoop-based stores Kudu had become imperative. Implementing OLAP on Hadoop technologies like AtScale, Jethro Data and SQL on Hadoop engines like Apache Impala, phoenix, drill accelerates queries and keeps to the pace.
Convergence of IoT, Cloud and Big Data
We can expect that every little thing in the year 2017 will have a sensor as an integral part that would send back the information to the mothership. The internet of things or as we say IoT generate large volume and variety of data and a huge portion of this data is deployed on the cloud. The data will reside in myriad relational and nonrelational systems that include Hadoop clusters to NoSQL databases. So capturing data and analyzing the same from innumerable sources itself will be a pretty tough challenge and demand for analytical tools that have the capacity to seamlessly connect and combine a variety of cloud-hosted data sources will definitely increase. And such tools will aid the businesses to explore and figure out the hidden opportunities in the data gathered.
Data Warehouse is heating up in the cloud
The death of data warehouse has been quite the talk in the Big data world for some time now! The pace obviously has declined but we have been witnessing a major shift in patterns in this technology where Amazon is now leading with the concept of on-demand cloud data warehouse. According to analysts, 90% of companies who have already adopted Hadoop will be sticking on to their data warehouses and with the new upcoming the customers can scale the computing resources and storage accordingly in data warehouse compared to the huge volume of information stored in the Hadoop data lake.
Rise of Metadata Catalog
Many a times companies tend to discard a lot of collected data as it increases the pressure on processing such huge volume of data. Hadoop can process data but it does not simplify the process of finding data. The concept of Metadata catalog aids the users to discover and explore the relevance of data. They make use of tags to understand the relationships between data assets and also provides query suggestions thereby minimizing the time to get hold of the accurate data.
This year will witness a strong magnetism towards data virtualization. Data virtualization unlocks the hidden concepts and conclusions from a large set of data. Graphical data virtualization allows the enterprises and organizations to retrieve and manipulate data on the go no matter where the data is residing and in which format.
Hadoop is just no more a batch processing platform but has upgraded itself to be a multi-purpose engine for ad-hoc analysis. It has started to been used to for operational reporting on day to day basis similar to the way done by traditional data warehouses. In the coming year, enterprises will cater to these hybrid needs by following use case specific architecture design. It involves a lot of digging of a host like a user persona, questions, the speed of data, etc prior committing to a particular data strategy. The modern architecture would be mostly needs to be driven, and they will look forward to combining the data-prep tools like Hadoop Core and end user analytics platforms so that they can be configured and reconfigured as per the evolution of the needs.
I hope you have found value in this article and have learned the foundation of Big Data Frameworks. If you like this article. Please share it! Thanks!
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