Big Data Hadoop & Spark

10 Big Data Trends for 2017

At the start of every year, experts gaze through the crystal orb in the hopes of predicting the new year trends. This time too, our experts at AcadGild sat down and predicted the Big Data trends for 2017. Here are a few of those trends.

Big Data Trends to Watch Out For in 2017

1. The Need For Speed

Doesn’t matter if you can perform sentiment analysis on Hadoop, the speed of the interactive SOL is the takeaway. This need for speed has propelled the adoption of faster databases. Databases like Exasol, Hadoop-based stores like Kudu that enable faster queries. Query accelerators are fast further blurring the lines between traditional warehouses and the world of Big Data by using SQL-on-Hadoop engines and OLAP-on-Hadoop technologies. SQL-on-Hadoop engines are Apache Impala, Hive LLAP, Presto, Phoenix, and Drill). OLAP-on-Hadoop technologies, on the other hand, are AtScale, Jethro Data, and Kyvos Insights),

2. The Changing Face of Data Lakes

A storage repository that holds a vast amount of raw data, a Data Lake holds data in its native format until it is needed. Up until now, keeping this lake well hydrated was the end game. But in 2017, we expect to see organizations to demand the repeatable and agile use of the lake for quicker answers. They are bound to carefully consider business outcomes before they invest in data, personnel and infrastructure. This is in turn will lead to a stronger partnership between business and IT.

3. The Dominant V

Volume, Velocity and Variety are the 3 V’s of Big Data. But 2017 will have us know the dominant V of them all; Variety. With organizations integrating a wide assortment of data sources, this trend will continue to grow. From schema-free JSON to nested types in other databases (relational and NoSQL), to non-flat data (Avro, Parquet, XML), data formats are multiplying and connectors are becoming crucial. Our experts believe that 2017 will be the year where various analytics platforms will be graded based in their efficaciousness to provide live and direct connectivity to this variety of data sources.

4. The Spark

It is no surprise that the majority favours Spark over MapReduce. The in-memory caching abstraction feature of Spark makes it ideal for workloads where multiple operations access the same input data. Spark is simpler and usually much faster than MapReduce for the usual Machine learning and Data Analytics applications. Therefore, Spark will provide an exceptional platform form computation intensive machine learning, AI and graph algorithms. For instance, Microsoft Azure ML has taken off beautifully thanks to its beginner-friendliness and easy integration with existing Microsoft platforms. With Machine Learning more accessible to the public, we can soon expect to see more models and applications generating petabytes of data.

5. IoT, Cloud and Big Data

The Internet of things is generating massive volumes of data as we speak. This data can be both, structured and unstructured. A large part of this data is being deployed on Cloud services. This data is often heterogeneous and is scattered across multiple relational and non-relational systems. For instance, from Hadoop clusters to No SQL databases. This upsurge in data has led to an expansion in the demand for analytical tools. Analytical tools that can seamlessly connect to and combine a wide variety of cloud- hosted data sources. These analytical tools are necessary to enable organizations to sift through and envisage any and every type of data that is stored. Why? So as to discover those opportunities that are hidden in their IoT investments.

6. Analysis-worthy Data

As mentioned in the point above, there is a humongous amount of data that s being channelled into the data lakes. And figuring out those data that is worth analyzing is a painful task. Although there are Metadata catalogues that helps users to discover and understand relevant data that is worth analyzing using self-service tools, it isn’t enough. Companies like Alation and Waterline are now using Machine Learning to automate the work of finding data I Hadoop. They catalogue files using tags, uncover relationships between data assets, and even provide query suggestions via searchable UIs. This year, we expect to see n upscale in the demand for self-service discovery, which will grow as a natural extension of self-service analytics
big-data-analytics-solutions

7. Dark Data

Nothing sinister we assure you. According to Gartner, dark data is “information assets that organizations collect, process and store in the course of their regular business activity, but generally, fail to use for other purposes. In other words, Dark Data is operational Data that is not being used. 2017 will see organizations increasingly sifting through their wealth of dark data. This dark data is contained in paper-based documents, photos, videos and other corporate assets. This information has so far been lying dormant in storage closets and file baskets but now, they will all be brought out in the open.
Why a sudden interest in dark data? Because these assets can give organizations a more comprehensive view of historical performance trends and product cycles that can be useful for planning. The data can also provide supporting evidence for trademark infringement and/or intellectual property violation claims. In other words, these data that were forgotten and ignored could well be the same that can help these organizations to be the game changer.

8. Data Security

The importance of data security cannot be stressed enough. Companies have to boot up and tighten their data access permissions to ensure that each data user has the right permissions to access the relevant data. With the deluge of data that is flooding warehouses and repositories, data security has become all the more pertinent. Companies will create or revise existing data access permission policies. They are also likely to implement technologies that monitor and detect potential data exfiltration by users. What is exfiltration you ask? data exfiltration is a process in which users—without authorization—copy, transfer, or retrieve data that exceeds their access clearances.

9. The Demise of on Premise Data Platforms

Don’t panic! We are talking about purely on premise-based data platforms. And don’t fret yet because their decline has begun. The notion hasn’t taken a complete wipe-out. YET. But they are vanishing as more organizations begin to rely completely on public cloud services or hybrid models.

10. Bridging The Talent Gap

Big Data technicians, take note. There is an obvious talent gap in data analytics and this gap is going to further widen as demand shoots up. Our experts believe that Organizations and academic institutions will collaborate meticulously to generate skills and talent to meet the demand for data engineers. Having spoken about the importance of data analytics in the beginning of this article, it is pretty clear that business expects employees to perceive, appreciate and work with analytics. According to McKinsey, the shortage of personnel in 2017 is at about 200,000 for the US alone. This figure can easily double on a global scale.
While academic institutions are scrambling to put together degree programs in data science, boot camp-style schools are mushrooming everywhere to provide 12–14 weeks of training in. Such programmes impart essential data science skills to their participants and equip them with cutting-edge methods to better market themselves.

Why AcadGild?

So even with all that competition out there, what makes AcadGild stand out?
Because we not only provide you with an avenue to Become a Big Data and Hadoop Administrator in Just 12 Weeks.
We have Highly Experienced Mentors who have been in the Big Data domain too understand each and every nuance of big Data. We provide you with Lifetime Access to Dashboard. Also, as part of the course, you will Develop 2 Real-Time Projects in Big Data. Moreover, you can be rest assured that our experts will always be around to clear your queries as only AcadGild provide you with 24X7 Coding Support and a Free Job Preparation Week.

Conclusion

Concluding, if 2016 was a heck of a year for Big Data, 2017 has got us is a flurry of excitement. Of all the trends that have been listed above, analytics seems to project. Mainly because organizations need them to drive customer satisfaction. With the number of connected devices multiplying by the second, the data lakes are flooded with a wide variety of data. And so far they have been sitting idle. Especially those dark data that have been presumably lost in the file cabinets. But 2017 is going to be pivotal year where analytics will be the nervous system that touches every point of data to reveal untapped potentials.
But this nervous system is prone to external attacks and ample security measure has to be implemented. Incidents, like hacking lightbulbs and remotely setting off fire alarms, might sound fun and harmless. But in a different perspective, these are deadly. For instance, the ability to remotely override a car’s brake system or change the functions of a “smart” pacemaker doesn’t sound that funny. They are lethal weapons if not just dangerous.
Another fact that seems to catch our eye is the evolution on Artificial Intelligence and Machine Learning. The world is increasingly moving towards self-service mode and that is exactly where Big Data analytics is chartered to go to.
For more interesting articles, stay tuned to AcadGild!

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