Big data is a big deal – and a big opportunity. The challenge is that most big data progress has been limited to big companies with big engineering and data science teams. The systems can be complex, immature and hard to manage. That might be OK if you’re a well-trained developer in Silicon Valley, but it doesn’t play well if you’re a jack-of-all-trades IT leader at a bank in Atlanta or an on-the-move business leader in Amsterdam. How can you tap into big data if you don’t have an army of engineers who stay steeped in the latest tech?
Fortunately, help is on the way thanks to several trends that move tech from the foreground to the background.
First and foremost, big data is moving into the cloud. Amazon, Microsoft and Google now deliver Hadoop and Spark “as a service,” eliminating the need to spin up a cluster, research new software products or worry about version management. Companies are increasingly moving their big data workloads into the cloud, hiding complexity from their users while relying on the world’s best data center professionals to manage the tech. They want access to infrastructure when and how they want it, with just as much as they need but more.
Next up is the emergence of “serverless” computing. This builds on the cloud trend, while removing even more tech dependencies. Just load your data and start processing. Tell your cloud provider what you want to do, how much data you want to crunch and where you want to run it – and they will spin up the infrastructure when you need it, and spin it down when you don’t. That’s incredible for retailers and CPG companies, for example, who may have seasonal businesses and therefore fluctuating data needs throughout the year. They can do critical data analytics as needed without having to pay for robust infrastructure during the off-peak periods.
The third big trend is the move to self-service, fueled by new tools and platforms that democratize both data integration and data consumption. “Self-service integration” makes it fast and easy to connect systems, create data pipelines and automate processes without the need for intensive coding.
