This article was originally published on ITProPortal.
Employees up and down the value chain are eager to dive into big data, hunting for golden nuggets of intelligence to help them make smarter decisions, grow customer relationships and improve business efficiency. To do this, they’ve been faced with a dizzying array of technologies – from open source projects to commercial software products – as they try to wrestle big data to the ground.
Today, a lot of the headlines and momentum focus around some combination of Hadoop, Spark and Redshift – all of which can be springboards for big data work. It’s important to step back, though, and look at where we are in big data’s evolution.
In many ways, big data is in the midst of transition. Hadoop is hitting its pre-teen years, having launched in April 2006 as an official Apache project – and then taking the software world by storm as a framework for distributed storage and processing of data, based on commodity hardware. Apache Spark is now hitting its strides as a “lightning fast” streaming engine for large-scale data processing. And various cloud data warehousing and analytics platforms are emerging, from big names (Amazon Redshift, Microsoft Azure HDInsight and Google BigQuery) to upstart players like Snowflake, Qubole and Confluent.
The challenge is that most big data progress over the past decade has been limited to big companies with big engineering and data science teams. The systems are often complex, immature, hard to manage and change frequently – which might be fine if you’re in Silicon Valley, but doesn’t play well in the rest of the world. What if you’re a consumer goods company like Clorox, or a midsize bank in the Midwest, or a large telco in Australia? Can this be done without deploying 100 Java engineers who know the technology inside and out?
At the end of the day, most companies just want better data and faster answers – they don’t want the technology headaches that come along with it. Fortunately, the “mega trend” of big data is now colliding with another mega trend: cloud computing. While Hadoop and other big data platforms have been maturing slowly, the cloud ecosystem has been maturing more quickly – and the cloud can now help fix a lot of what has hindered big data’s progress.
The problems customers have encountered with on-premises Hadoop are often the same problems that were faced with on-premises legacy systems: there simply aren’t enough of the right people to get everything done. Companies want cutting-edge capabilities, but they don’t want to deal with bugs and broken integrations and rapidly changing versions. Plus, consumption models are changing – we want to consume data, storage and compute on demand. We don’t want to overbuy. We want access to infrastructure when and how we want it, with just as much as we need but more.
Big Data’s Tipping Point is in the Cloud
In short, the tipping point for big data is about to happen – and it will happen via the cloud. The first wave of “big data via the cloud” was simple: companies like Cloudera put their software on Amazon. But what’s “truly cloud” is not having to manage Hadoop or Spark – moving the complexity back into a hosted infrastructure, so someone else manages it for you. To that end, Amazon, Microsoft and Google now deliver “managed Hadoop” and “managed Spark” – you just worry about the data you have, the questions you have and the answers you want. No need to spin up a cluster, research new products or worry about version management. Just load your data and start processing.
There are three significant and not always obvious benefits to managing big data via the cloud: 1) Predictability – the infrastructure and management burden shifts to cloud providers, and you simply consume services that you can scale up or down as needed; 2) Economics – unlike on-premises Hadoop, where compute and storage were intermingled, the cloud separates compute and storage so you can provision accordingly and benefit from commodity economics; and 3) Innovation – new software, infrastructure and best practices will be deployed continuously by cloud providers, so you can take full advantage without all the upfront time and cost.
Of course, there’s still plenty of hard work to do, but it’s more focused on the data and the business, and not the infrastructure. The great news for mainstream customers (well beyond Silicon Valley) is that another mega-trend is kicking in to revolutionize data integration and data consumption – and that’s the move to self-service. Thanks to new tools and platforms, “self-service integration” is making it fast and easy to create automated data pipelines with no coding, and “self-service analytics” is making it easy for analysts and business users to manipulate data without IT intervention.
All told, these trends are driving a democratization of data that’s very exciting – and will drive significant impact across horizontal functions and vertical industries. Data is thus becoming a more fluid, dynamic and accessible resource for all organizations. IT no longer holds the keys to the kingdom – and developers no longer control the workflow. Just in the nick of time, too, as the volume and velocity of data from digital and social media, mobile tools and edge devices threaten to overwhelm us all. Once the full promise of the Internet of Things, Artificial Intelligence and Machine Learning begins to take hold, the data overflow will be truly inundating.
The only remaining question: What do you want to do with your data?
Ravi Dharnikota is the Chief Enterprise Architect at SnapLogic.