Emerging Big Data Trends for 2017

Emerging Big Data Trends for 2017

Big Data Trends for 2017

"Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass.” said the McKinsey Global Institute (MGI) in its executive overview of last month's report: "The Age of Analytics: Competing in a Data-Driven World."

2016 was an exciting year for big data with organizations developing real-world solutions with big data analytics making a major impact on their bottom line. 2017 will see a continuation of these big data trends as technology becomes smarter with the implementation of deep learning and AI by many organizations. Growing adoption of Artificial Intelligence, growth of IoT applications and increased adoption of machine learning will be the key to success for data-driven organizations in 2017. Here’s a sneak-peak into what big data leaders and CIO’s predict on the emerging big data trends for 2017.

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Top 8 Big Data Trends for 2017

1) Big Data become Fast and Approachable with multiple options to speed up Hadoop

Organizations can perform sentiment analysis and machine learning on Hadoop but the foremost question that people ask is how fast the interactive SQL is because business users who want to use Hadoop for faster data access and exploratory analysis – SQL is the channel.  The need for speed to use Hadoop for sentiment analysis and machine learning has fuelled the growth of hadoop based data stores like Kudu and adoption of faster databases like MemSQL and Exasol. With the use of various SQL-on-Hadoop tools like Hive, Impala, Phoenix, Presto and Drill, query accelerators are bridging the gap between traditional data warehouse systems and the world of big data.

2) Big Data is no longer just Hadoop

A common misconception is that Big Data and Hadoop are synonymous. Often, many people in their data journey think that big data only means “Hadoop”. Big Data solutions are playing an integral part in plethora of mobile apps, connected cars, wearables like FitBit, and Smart Meters. However, this does not mean just Hadoop but Hadoop along with other big data technologies like in-memory frameworks, data marts, discovery tools ,data warehouses and others that are required to deliver the data to the right place at right time. Organizations today are looking to glean insights from a host of multiple sources ranging from systems of record to cloud warehouses and structured and unstructured data from both non-hadoop and hadoop sources. In 2017, big data platforms that are just built only for hadoop will fail to continue and the ones that are data and source agnostic will survive.

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3) Usable Data Lakes to drive Business Value

"With existing big data projects recognising the need for a reliable data foundation, and new projects being combined into a holistic data management strategy, data lakes may finally fulfil their promise in 2017."- said Ramon Chen, CMO of data management specialists Reltio

Data lakes hold value in all organizations whether it is large or small. Organizations are embarking on data lake strategy for applications that are centralized and for applications coming together on a single central platform. Organizations have realized that they have lots of data for profitable business decision making and they can derive value from it through data lakes. Data lakes allow enterprise to centralize all sorts of information and gain competitive edge in the market. Organizations now see data lakes as an important way of transforming business and that explains why 2017 will be the year of focus around data lakes as companies invest in analytics platforms and use data lakes to drive business innovation.

4) Big Data Grows Up : Data Governance and Security add to Enterprise Standards

Data is the new oil but oil leaks can be a dangerous threat to the people surrounding it. Enterprises building big data solutions on top of hadoop will focus on data govern menace and security front in 2017 thereby eliminating barriers to the enterprise adoption of big data technologies like Hadoop. Hadoop security is non-optional as hadoop deployments become business-critical for organizations. Organizations focus on security of the centralized hadoop based data lakes by replacing the practice of dumping raw log files containing sensitive information with encryption of all long term data storage and systematic data classification procedures. Some of the latest data governance and security components surrounding enterprise systems include -

  • Apache Atlas developed as a part of data governance strategy allows organizations to apply consistent data classification procedures across the entire data ecosystem.
  • Apache Sentry enforces role based authorization to the metadata and data stored in a Hadoop cluster.
  • Apache Ranger renders centralized security administration for hadoop clusters.

 Data governance and security gained steam in 2016 and the momentum will carry over in 2017 as hadoop becomes the core part of the IT landscape with enterprises hashing out all obstacles preventing them from capitalizing on data.

5) Growth of Cloud Based Analytics

With maturing hadoop architecture and markets, cloud based hadoop deployments will rule the big data space in 2017. Demand for hybrid and public cloud services will increase as investors claim their stake. Cloud based hadoop deployments will become more convincing for organization who still want to maintain historical data for reporting because of their economical storage cost, higher accessibility and availability. AtScale, popular Hadoop BI vendor in its recent survey found that more than 50% of the respondents to its survey had big data solutions deployed in the cloud increasing to 75 % in 2017. With many businesses moving to the cloud, organizations realize the potential of analytics in the cloud and cloud data warehouses like Amazon RedShift continue to be the data destination heroes.

Another important reason for the growth of cloud based analytics in 2017 is the shortage of requisite talent to run in-house hadoop clusters. Opting for a cloud services providers provides organizations with the big data processing platform along with the relevant expertise.

6)  Machine Learning Automation

Most of the organizations are making the best use of Hadoop’s scalability to build super-sized data warehouses for the execution of familiar SQL queries for BI reporting. Still not many hadoop users consider hadoop as a platform for the execution of machine learning algorithms. One cannot deny the fact that hadoop appeals as a platform for machine learning. Big data trend is all about gleaning meaningful insights from huge amounts of varied data and finding out a way on how to act on the insights in a predictive manner to get ahead of the competition, then in that case the practice of training and scoring machine learning models needs to be considered as a trendsetter for many hadoop deployments. With the continuous growth of data and shortage of data scientists in, many organizations in 2017 will consider machine learning automation to scale up their analytics efforts.

7) Apache Spark and Machine Learning to Ignite the Big Data Space

Apache Spark is no more just a component of the hadoop ecosystem but has become the big data platform of choice for several organizations. A survey of architects by Syncsort found that 70% of the BI analysts and IT managers favoured spark over hadoop mapreduce because of its real-time in-memory fast processing speed. Apache Spark is lightning up big data as it is much more natural, mathematical and convenient for programmers. Spark’s big computing big data capabilities have enhanced the platforms featuring graph algorithms, artificial intelligence and machine learning. However, one important thing to note here is that Apache Spark is meant to enhance the big data computing capabilities of Hadoop and not replace it. To gain greater value from big data, organizations consider using Hadoop and Spark together.

8) Transition from Internet of Things (IoT) and Internet of People (IoP)

Big data experts predict that by end of 2020 there will be 26 billion to 100 billion connected devices. 2017 will witness a transition from IoT to IoP as predictive analytics mainly focuses on human interactions, human behaviour which will infiltrate through different industry verticals. Big data is already being used to predict various health trends, forestall any major disease outbreaks and cure illness. In 2017, it will become an integral part in the detection and prevention of diseases at an early stage. For instance, hospitals will deploy machine learning models to predict the probability of relapse of a disease so that they can work out on when a patients is likely to be readmitted during his discharge.

Increasingly sophisticated big data demands means the gravity to innovate will remain high in 2017. This will be the year with major changes to the big data ecosystem as organizations continue to embrace data realizing that the only way to become a data-drive organization is to provide value to stakeholders. We are looking forward to what 2017 will bring on to the big data table.





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