It’s only been a couple years since running a cloud-based data warehouse was considered a novel approach to handling large datasets. 2012, to be precise, was when Amazon Web Services debuted Redshift and the Business Intelligence world was gifted a detailed glimpse of what the future would look like, big data style. Flexible, redundant, scalable and efficient, Redshift was everything the Analytical Big Data ecosystem was waiting for. The framework featured a massively parallel processing architecture that provided unparalleled processing capabilities at a more than scalable cost point.

Fast forward to 2018, and Redshift is far from being the only massively parallel processing player on the big data analytics block. Concurrent with the evolution of big data from unconventional to mainstream, has been the spawning of various other worthy MPP competitors. Now more than ever, it’s evident that Big Data and its associated technologies are evolving at a hyper rapid pace. What’s there to keep track off? Well, quite a lot actually and I’e got the essentials rounded up for your reading pleasure.

Surprise, Redshift is no longer numero uno

That title now belongs to Snowflake, at least, according to the DBT Slack developer community. When pictured from the backdrop of several key performance indexes, such as cluster-based speed of compute, number of queries handled per second, and overall cost to run, the relatively new kid in the block, Snowflake, is upstaging Redshift by a significant margin and has become Top Dog. Trailing slightly behind these two is BigQuery from google. But even it, like Redshift, has its own unique use cases where it stands out from the pack. Which emphasizes one point – benchmarks aside, the most suitable MPP framework is often dependent on your data processing and analysis needs.

There’s much more big data to churn through

This comes as no surprise, with the ubiquity of internet-connected devices and the trend towards an ‘everything online’ state (summarily put – the internet of things), it was only a matter of time before Big Data got even bigger. For the smart and modern enterprise, this positively translates to more churn data, more insights and invariably increased productivity. In fact, per Pentaho, an analytics for business firm, IoT and the massive data pool it creates will translate to over $11.1 trillion in economic value. The only catch is a concurrent drive to advance existing processing and analytics architecture must be effectuated. Massively Parallel processing frameworks like Snowflake and Redshift must incorporate newer techniques to offset the challenges brought on by the world’s growing data needs. And speaking of evolving technologies and their impacts.

Handling big data is set to get less technical

That would be thanks to recent advancements in the field of artificial intelligence and machine learning. As a whole, these two have birthed a new variant of processing data tagged cognitive computing – the same architecture used in notable AI frameworks like IBM Watson and Facebook M. If and when these get paired with existing Massively Parallel Processing frameworks, expect an even greater degree of autonomy in MPP systems. Ultimately, this would lower the barrier of technicality required to access and use these systems. Great news for small and medium business enterprises and everyone else who’s looking to cut operational costs.

Cloud computing remains integral to the success of big data

Redshift is not the only MPP framework running a cloud-based architecture, as a matter of fact virtually every other key player in the industry including Snowflake, Azure SQL Data Warehouse from Microsoft and Google’s BigQuery is in one way or the other integrating cloud computing into its operational framework. And for good reasons too.

Aside from the cost of operation factor, cloud computing further improves on the functionality and flexibility of MPP systems. It allows clients to conveniently specify and allocate the exact computing resources they need, as opposed to subscribing to a huge resource block and later utilizing just a fraction of it or vice versa.

In all, the future of big data is in part tied to advancements in the technologies that make handling and processing it much easier. As these frameworks improve, expect widespread adoption and a corresponding uptick in the applications and efficiencies of Big Data. It’s a positive feedback mechanism, one that guarantees increased farming of Big Data, promulgation of the Internet of Things and a commensurate heightening in the extent and rate of innovation (as we are now seeing in the MPP sector).

All of this comes together to paint a genuinely intriguing future for Big Data and provided key stakeholders remain invested in the system; it is a future that looks set to manifest sooner rather than later.

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