7 Predictions Made To Harness The Power Of Big Data
2016 was a benchmark year for Big Data with an ever increasing number of organizations storing, processing and extracting value from data of all forms and sizes. While 2017 brings a big opportunity for retailers to win their customers.
There is no doubt in the fact that Internet of Things (IoT), Big Data and artificial intelligence (AI) are the three biggest buzzwords of the IT industry. The term big data is more than 20 years old, coined by O’Reilly Media’s Roger Magoulas in 2005. And all that was “hype” related to these terms years ago became a justified norm this year.
Big Data: Beyond the hype
In the year 2017, systems that support large volumes of both structured and unstructured data will continue to rise. Soon the market will demand platforms that help data custodians govern and secure big data while empowering end users to analyze that data.
Besides, big data has become big enough which cannot be ignored anymore! So with a lot of digging. Here down below are some of the most possible trends to be found in big data this year and beyond.
#1 Big Data Becomes Faster And Approachable
The upcoming year will surely allow you to perform machine learning and conduct sentiment analysis on Hadoop. With the adoption of faster databases like Exasol and MemSQL, the need for speed has fueled for Hadoop-based stores like Kudu, and technologies that enable faster queries. In addition to this, SQL-on-Hadoop engines like Apache Impala, Hive LLAP, Presto, Phoenix, and Drill and OLAP-on-Hadoop technologies (AtScale, Jethro Data, and Kyvos Insights) are the query accelerators which will end up further blurring the lines between traditional warehouses and the world of big data.
#2 Big Data Is No Longer Just A Hadoop
In the Previous years, we saw a rise in several technologies with the big-data wave. And this fulfilled the need for analytics on Hadoop. However, many enterprises with complex, heterogeneous environments no longer wished to adopt a siloed BI access point just for one data source/Hadoop. In 2017, customers will demand accurate analytics on all data. And soon the platforms that are data- and source-agnostic will thrive while those that are purpose-built for Hadoop and fail to deploy across use will fall by the wayside. The exit of Platfora turns out to be an early indicator of this trend.
#3 Organizations leverage man-made reservoir (data lakes)
At First you dam the end (build a cluster), then you let It fill up with water, i.e. data. Once the data lake is established, you start using the water (data) for numerous purposes like generating electricity, drinking and recreating, i.e. predictive analysis, ML, cyber security, etc. Till now, hydrating the lake has been an end in itself. The year 2017 will bring a major change as the business justification for Hadoop tightens. Organizations will soon start demanding repeatable and agile use of the lake for quick answers. Moreover, they will carefully consider business outcomes before investing in personnel, data, and infrastructure. Which will eventually foster a strong partnership between the business and IT.
Last but certainly not the least, several self-service platforms will gain deeper recognition as the tool for harnessing big data assets.
#4 Hadoop matures to reject one-size fits all frameworks
Presently, Hadoop is no longer just a batch processing platform for data science use cases. It has become a multi-purpose engine and is even being used for operational reporting on day-to-day workloads (traditionally handled by data warehouses)
In the upcoming years, with the use of case- specific architecture design more and more organizations will start responding to these hybrids. Several factors will be taken into consideration such as user personas, questions, volumes, frequency of access, speed of data and level of aggregation before committing to a data strategy. These modern-reference architectures will be need-driven and will incorporate the best self-service data-prep tools, Hadoop Core and end-user analytics platforms in such ways that can be reconfigured as those needs evolve. The flexibility of these architectures will ultimately drive technology choices.
#5 Variety drives big data investments
According to Gartner, big data is defined as the three Vs namely:
- High volume
- High Velocity
- High variety information assets
All three of them are growing exponentially, variety is becoming the single biggest driver of Big-data investments. The trend will continue to grow as firms try to integrate more sources in order to focus on the “Long tail” of big data. Starting from schema-free JSON to nested types in other databases like relational and NoSQL to non-flat data (Avro, Parquet, XML), data formats are multiplying and connectors are becoming crucial day in day out.
It may quite interest you to know that in the upcoming years, big data analytics platforms will be evaluated based on their ability to provide live direct connectivity to these disparate sources.
#6 Machine learning technology to light up big-data
Apache Spark, a component of the Hadoop ecosystem now becomes the big-data platform of choice for enterprises. A recent survey reveals that around 70% of the respondents favored Spark over incumbent MapReduce as it is batch –oriented and doesn’t lend itself to interactive applications or real-time stream processing.
These big-compute-on-big-data-capabilities have raised platforms including computation-intensive machine learning, AI and graph algorithms. Microsoft Azure ML has taken off and all thanks to its beginner- friendliness and easy integration with existing Microsoft platforms. Opening up to ML to the masses will soon lead to the creation of more models and applications generating petabytes of data. As machine learn and systems grow smart, all eyes will be set on self-service software providers to see how they make this data approachable to the end user.
#7 Everything will have a sensor that sends information back to the mothership
There is no denying the fact that IoT is generating massive volumes of structured and unstructured data. Which means an increasing share of this data is being deployed on cloud services. The data is often heterogeneous and lives over multiple social and non-social frameworks like Hadoop clusters to NoSQL databases. As the innovations in storage and managed services have sped up the capture process, accessing and understanding the data itself still pose a significant last-mile challenge.
As a result, demand for big-data analytical tools that connect to and combine a wide variety of cloud-hosted data sources is growing. Such tools enable businesses to explore and visualize any type of data stored anywhere, helping them discover hidden opportunity in their IoT investment.
As big data continues to bring down its way of growth & development, there is no doubt that these innovative approaches will allow companies to reach full potential with data. According to Gartner” revenue generated from IoT products and services alone will exceed $300 billion in 2020, and that probably is just the tip of the iceberg.”