BrainBlog for StarRocks by Jason Bloomberg
Online analytical processing (OLAP) has provided businesspeople with insights into data for many years – but not without its tradeoffs.
Early database technologies lacked the scalability and performance characteristics companies are familiar with today. As a result, vendors designed OLAP systems to provide analytics based on prepared summary data – data sets that were incomplete and hours or even days old.
With today’s cloud computing technologies, such limitations should no longer hold companies back. Business professionals expect real-time insights into all relevant data, without the constraints of yesterday’s legacy technologies.
The Power of Real-Time Data
The term real-time has different meanings in different contexts. Multiplayer games like Fortnite require real-time interactions, but this low-latency connotation of real-time doesn’t apply in most business situations.
Some businesses, like real-time stock trading, require data that are current to the millisecond – but this bar is higher than most organizations set for their data-centric interactions. More generally, companies require up-to-the-minute information about their businesses. Whether data are a millisecond or a minute old, however, is beside the point. What counts is that business decision makers have information current enough to make the best decisions they can without delay.
Delivering this level of real-time data, however, requires a rethink of the entire data architecture end-to-end. Data collection on web sites and devices must take place in real time. Middleware, databases, and visualization technologies must similarly operate in real time. If any component in the data lifecycle presents a bottleneck, then the performance of the entire interaction will suffer.
For this reason, modern OLAP technologies often depend upon massively parallel processing (MPP) – dividing up queries in order to run them in parallel. Here are three stories of different companies who struggled with their existing OLAP solutions in the face of real-time requirements, and who moved to the StarRocks solution and its MPP framework to resolve their OLAP bottlenecks.
Read the entire BrainBlog here.
