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7 Big Data Trends That Will Dominate 2016

Big Data Trends

Today’s Big Data professionals & aspirants will have to adapt to new technologies at a faster rate than ever before. Big data analytics is going to witness some interesting trends in the coming year, and if you want to stay ahead of the competition, you’ll have to prepare for them.
which you will need to keep an eye on: 1. IOT will gain momentum Internet of things (IOT) is the concept of connecting any device to the internet or to each other through the internet.  IOT is widely used in many industries. IoT will generate large demand for big data professionals to process & analyze the massive amounts of data being thrown up from the billions of sensors in millions of devices and machines. IOT growth IoT spending The above numbers clearly show that the market for IOT is growing at an incredible rate. GE’s Industrial Internet Insights Report predicts that the “Internet of Things” will add $10 to $15 trillion to global GDP in the next 20 years, and employer competition for skills in this space is on the rise as a result. Both technology and non-technology companies are hiring for IOT. IoT jobs IoT jobs
 Source: Wanted analytics
Fastest growing IOT job positions include systems software developers (215% growth in the past year), information security analysts (113% growth), and computer systems engineers (110% growth). Adding to this, GE has advertised 2,104 jobs looking for skills needed to support their industrial internet initiative in the last two years. 2. The rise of Chief Data Officers(CDO)
A CDO is a senior executive who is responsible for a firm’s data and information strategy, governance, control, policy development, and effective exploitation. The CDO is the one who will be accountable for information privacy and protection, information governance, data quality, data lifecycle management (how long to keep the data) and exploit data assets to create business value. As data analytics becomes critical for key business processes, like gaining customer intelligence, improving sales, productivity and revenue, capturing data has become a top priority for many organizations. But besides obtaining data, organizations have different approaches to manage data as a corporate asset, based on Gartner’s survey of CEOs and senior business executives. Chief Data Officer As different businesses use data in different ways, today’s organizations need executives who can develop a digital business strategy that focuses on core business model. 3. Organizations across the board will embrace Data-Driven culture Real cultural change is required for companies to succeed with data. Cultural change isn’t just  about which database you use or how many data scientists you have, but rather it is a complex interaction between the data you have, where it is stored and how people work with it, and what problems are considered worth solving. It requires spreading data through your organization, not just by adding a few data scientists, but to enable everyone in the organization to access the data and see how they can use the data. A good example of data-driven culture is Facebook. Facebook used HiPal to provide access to data to all their employees. HiPal allowed any user to see what data others in the company were accessing; it also lowered the cost of data access and created the expectation that you needed data to support your business decisions. It was a major foundation for Facebook’s growth strategy and international expansion. More organizations will embed data-driven culture in their DNA, with the help of CDOs. 4. NoSQL will become more popular NoSQL (not only SQL) is a database mechanism that is used to store, analyze and access large amounts of unstructured data, whereas, SQL databases are based on rows and columns system, equipped to handle structured data. If a company has billions of files as data, you would need thousands of columns and tabs. This makes SQL unfit to handle big data. NoSQL is ideal for companies that have large amounts of structured and unstructured data that doesn’t fit in a traditional SQL database. NoSQL databases can be broken into four broad buckets: Key-value store, document, wide-column and graph databases. The following image shows the pros and cons of a SQL and NoSQL database. SQL vs. NoSQL
                                                                       Source: Networkworld.com
The global NoSQL market, is forecast to reach $4.2 billion by the end of 2020, growing at 35.1% CAGR during 2014 – 2020. Global NoSQL Market
                                                                Source: alliedmarketresearch
The mass adoption of NoSQL databases by companies has created a huge demand for professionals with NoSQL skills. 5. Machine learning adoption will boom Enterprises around the globe have started to implement machine learning techniques to   analyze huge data sets and gain insights from these massive data sets. Major cloud service providers, like Amazon and Microsoft, not only offer analytics as a service but also, provide machine learning APIs and open source machine learning tools. These machine learning capabilities offered by cloud service providers enables developers to build applications – like fraud detection, face recognition, medical diagnoses, etc.– that find patterns in large volumes of data. Of course, tech giants have had machine learning features for years (for recommending related products, to identify spam emails, etc.). You need to have advanced statistics, mathematics and scientific knowledge to build these products. Today, developers can efficiently run machine learning algorithms on large datasets using these APIs and tools. This trend has made machine learning accessible to a wider audience (developers). In 2016, many industriies will start to implement machine learning techniques at a large scale.  6. Spark and Real-Time Analytics will be the next big thing Spark is a leading open source big data infrastructure framework used to store and process large data sets. Since Spark’s introduction to the Apache Software Foundation in 2014, it has received massive interest from developers, enterprise software providers and independent software vendors looking to capitalize on its in-memory processing speed and cohesive, consistent APIs. Spark is used in machine learning projects due to its ability to process real-time data efficiently. A good example for real-time streaming is a recommendation engine; similar products are shown based on your browsing history. Recommendation engine in Spark Spark provides dramatically increased data processing speed compared to Hadoop and is now the largest big data open source project. According to a survey by Databricks, Spark is the most active open source project in the Big Data community with over 600 contributors in the last 12 months (up from 315 in the previous 12-24 months).  Spark helps a broad range of audience (e.g. 41% data engineers, 22% data scientists, etc.) to solve big data problems. 7. More applications will use Deep Learning Deep learning is a branch of machine learning, and it takes us one step closer to artificial intelligence. While not having to reach the level of real artificial intelligence, superior technologies that use deep learning techniques include face recognition, voice identification, emotion detection, natural language processing, and audience profile data. Consider Microsoft’s Xbox One and Sony’s PS4, two leading gaming consoles that both have the ability (through Kinect and the PlayStation Camera, respectively) to recognize and sign in players by face recognition when they enter a room. Both systems respond to voice commands, and all of these capabilities are used in-game. Some of the promising application that use deep learning are, Cortana, Google Now, Siri, and Amazon Echo.  Conclusion These seven big data trends mark the beginning of a new era for big data analytics.  Be on top of all these developments, learn new skills where you can, and enjoy the ride in 2016. Edvancer’s Certified Hadoop & Big Data Expert course prepares you for the big data world

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