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The Top 3 Big Data Trends of 2016

According to a recent study from LinkedIn, the number of data scientists has doubled over the last four years. Job postings are also dramatically up, and these trends reflect increased demand for sophisticated data analysis skills. With this demand has also emerged a variety of different ways of working with data, which will shape the Big Data landscape in 2016 and beyond. Let’s take a look at the top three trends.

1. The cost of doing Big Data analytics will go way, way down in 2016. Big Data used to be the domain of large, well-resourced companies that could invest huge amounts of resources into building internal databases. As the “Big Data” revolution took off, and more companies grew interested in analytics, entrepreneurs began innovating ways to push the cost of Big Data down.

Over the past few years, this trajectory has continued, and every year, the cost of analytics decreases. We are now at a tipping point with these technologies. In 2016, the cost will go down so significantly that companies of all sizes will be able to use sophisticated data analytics.

2. SQL will become the dominant query language for NoSQL Big Data implementations. SQL was created as a special-purpose programming language for managing data held in a relational database system. Then NoSQL systems (initially meaning “Non SQL” or “Non Relational”) became popular with the burst of data generated in the Web 2.0 world, which didn’t mesh well in the Relational model. The philosophies of NoSQL databases were different and SQL was not the best fit for using them. As a result, many NoSQL databases replaced SQL with their own brand new, special purpose query languages.

Now the pendulum is swinging back towards SQL. To start, there is much more know-how for SQL around, as generations of computer scientists have their careers focusing on relational databases and using SQL as their query language. In fact, a recent developer survey from Stack Overflow found that SQL is one of the most popular languages, as well as one of the most lucrative. As the SQL ecosystem matures, developers will move away from the immature query languages created around NoSQL databases, and back to using SQL as the query language, even for NoSQL databases.

Even the term NoSQL has since been extended to also mean “Not Only SQL”, indicating support for SQL-based interfaces even if the core database isn’t relational.

3. Hadoop and associated technologies will grow by more than 100%, mainly driven by the demand for Big Data analytics.

The global Hadoop market was valued at $1.5 billion in 2012, and is expected to reach $50.2 billion by 2020, according to Allied Market Research.  The expansion of the Hadoop market is driven by a “sudden spurt” in demand for big data analytics. As businesses and consumers generate more activity online, businesses are faced with an explosion of raw, structured and unstructured data. As a result, they need affordable analytics, which Hadoop helps provide. In addition, Hadoop will become a mainstream platform for enterprise analytics as SQL performance is improved.

The combination of exploding interest in data science and a shortage of data scientists creates an urgent need for technologies that make data science accessible to all, without costly software or hard-to-find staff. However, the software industry is chaotic and polyglottic, which means the tools of the past are not sufficient. While mathematics has enabled huge advances in other forms of science and engineering, it has played a minimal role in software and programming, until now. Data algebra will help will bring sanity and structure to the software industry by turning data into a universal language.

CharlieSilverHeadshotContributed by: Charles H. Silver, CEO of Algebraix Data. Charles has been building companies and creating liquidity events for shareholders for more than 25 years. In the late 1990s, as cofounder of RealAge, Inc., he built the company from scratch, conceived its business plan, raised over $15 million in capital, negotiated key strategic relationships and, most important, positioned the company for profitability, which enabled it to survive the dot-com bust. In 2007, RealAge was sold very successfully to the Hearst Corporation. After graduating from the University of Michigan and prior to his entrepreneurial career, Charles served for two years as a staff member in the United States Congress.

 

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Comments

  1. Now a day’s big data analytics are common in most of the industry. Last year we showed Big Data uses in healthcare, Weather forecasting and some other industry as well. I can say Big-Data analytics is the next trillion dollar biz market..

    • That’s right. And yes, the cost of these system analytics will definitely cost lower because the market is growing and there are a lot more competitors that are offering the same service. It’s about time to work on the quality of service even more to gain customers from the others.

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