We're naturally a pretty bright species.
But team us up with complex data sets and powerful
analytics tools, and we become rocket scientists.
How we got here
With technology platforms generating data at the fastest rate in human history, and with the Internet of Things unleashing a new wave of data sensors and surfaces, the frontiers of economic advantage have well and truly shifted to real-time data analysis, if they’re not already knocking on the door of AI.
Rewind the story back a decade and the data infrastructure waters were looking a bit more stagnate than they do today. With the dams of Internet giants such as Google and Amazon bursting at seams, the best traditional vendor software could do was plug temporary holes in the dyke. The pressure was building for a disruptive technology breakthrough, and it was only a matter of time.
Enter the genius of the open-source community.
The Hadoop platform and subsequent Hadoop ecosystem which emerged radically transformed the IT landscape, lowering the cost of storing and processing data, and in the process becoming the de facto big data standard.
With the arrival of Hadoop, data that was once too expensive to collect and organize soon became a valuable business resource. Scaling effortlessly on commodity machines, Hadoop soon became so cost-effective that serious analytics moved from a costly corporate luxury, to a ubiquitous business norm. This in turn pushed the frontiers of business advantage from traditional CRM and business intelligence, to statistical insight gleaned from new, untapped, and unstructured data sources.
As the Hadoop ecosystem became more established, the technology advantage frontier shifted from the storing and managing massive amounts of data, to querying that data with near real-time speed. Initiatives aimed at improving pioneering Hadoop ecosystem query engine, Hive, soon emerged, but with mixed results. Efforts to boost speed often precluded the ability to handle data volume, while efforts to handle data volume often impacted processing speed prohibitively.
Taking on the challenge with world-leading data scientists, Gruter began sponsoring Apache Tajo, a promising open-source initiative with the grand ambitions of overcoming the data speed/volume conundrum, and replacing traditional data warehousing in the process.
Led by Gruter’s own Dr. Hyun-sik Choi and having just been granted Top-Level Project status by the Apache Foundation, Tajo has since posted blazingly fast query processing times on Petabyte-scale data sets in both lab and field conditions. More importantly, it achieves this query speed running advanced work tasks on commodity servers—all the while using license-free, open-source software.
With the Apache Tajo community specializing in sophisticated data querying techniques, Tajo is at the forefront of the merging of the Hadoop and traditional SQL markets. And that has in turn placed Gruter—with its expertise in the Hadoop ecosystem—front and center of the big data race.
As the Apache Tajo community pushes the boundaries of data querying technology, Gruter has set about building an end-to-end analytics hub which puts powerful data crunching capabilities on every desk in the modern enterprise.
And that means designing a system which readies massive data sets for analysis at standard officeware speeds, rendering them accessible across the enterprise through an intuitive and highly-productive officeware solution.
As the final pieces of the big data puzzle come together, Gruter is on the verge of launching something special for the new year.
Enter Qrytica, the Gruter data analytics hub powered by Tajo-on-Hadoop and set to launch in January 2015.
With the release of Qrytica, what was once the domain of expensive, white-coated data analysts is about to become the new office norm. Qrytica: We’re all analysts now.