“End-of-day batch processing is great for understanding what happened yesterday. Real-time streaming analytics provides up-to-the-millisecond intelligence.” This is how Joel Shore, news writer for TechTarget’s SearchCloudApplications site, highlights the benefits of real-time streaming analytics as opposed to traditional methods in his Q&A session with Steve Wilkes, founder and CTO of Striim.
Life moves at an event-driven pace, not batch capture, so being armed with information the moment data is created is important, especially in fast-paced environments such as finance and retail where the data is only actionable for so long.
One standout conversational topic in the interview is when Steve talks about the importance of in-memory processing for streaming analytics. Based on the incredible amount of data the world is generating today, it’s no longer feasible to store data and analyze it later. Steve states, “When you think about real-time processing, you have to decide: What is it that I need to know right now, and what is it about my data that could make it volatile or perishable?”
Rather than landing on disk fist, in-memory computing (IMC) allows companies to gain insights from their data while it’s streaming before it loses operational value, thus allowing for critical data-driven decision-making.
While at Cloud Expo, Steve presented a track entitled, “Streaming Analytics at the Intersection of IoT, Enterprise and Cloud: Four Case Studies.” Following this session, Joel and Steve discussed at length how streaming analytics can be used in environments such as cloud, on-premise and at the edge. In their Q&A, they further discuss the flexibility and integration of streaming analytics in these digital environments.
With memory costs dropping and processing growing more powerful, streaming analytics is the evolutionary next step in data management. Learn more about what Steve has to say by reading this Q&A: What You Need to Know About Real-Time Streaming Analytics.