Data Management with Timescale

Posted by
Sandra Mueller

Data Management with Timescale

On this week’s episode of the Real World IoT Podcast, host Ken Briodagh discusses the management of IoT data with Co-Founder and CEO of Timescale, Ajay Kulkarni, as well as Mike Freedman, Co-Founder and CTO of Timescale. Timescale is addressing one of the largest challenges in databases for years to come: helping developers, businesses, and society make sense of the data that our machines are generating in copious amounts. Timescale provides the only open-source time-series database that natively supports full-SQL, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL systems.

Shaping the Data

Right now in the IoT space, we are seeing businesses and companies deploying sensors to gain more data on particular things that are important to their basic structure. Collecting all of this data is only helpful if we have a way to break it down. This is of course where Timescale finds its meaning, but it's important to ask how we are going to create algorithms to deal with all of this data to improve predictability and accuracy over time. Looking at current advertisement algorithms, we see that they are falling behind due to outdated structure and when correct are usually based on lucky guesses. The IoT can't survive off lucky guesses. This is where the idea of continuous reporting comes in, which we see in every aspect of the IoT. Traditionally you would see a worker go through maintenance checks and then create a report which would be viewed by a higher up later to determine the next steps. Now we have sensors connected to each machine and device giving real time feedback which allows for more accuracy in determining current standings as well as predicting for the future.

The Fear Behind the Data

Data makes up the Internet of Things. In classic computing you have a human interacting with a machine providing inputs which then give the human information or outcomes they wanted. With this new wave of computing in IoT, data is really the life-blood of the system. Sensors provide inputs which produces data that can be monitored and broken down to produce actionable results. The scary thing, as Ken sees it, is if this system begins to bleed internally. So much data is produced by the many devices in the IoT and it is very easy to have massive amounts of wasted data from unnecessary devices to data that just sits and is never used, wasting servers and resources. The first wave of the IoT was based heavily on the notion of collecting and storing everything that you could get your hands on information wise, and now all these companies that subscribed to this idea are saying what do we do with it now. One way that this giant pool of data is being handled is moving it into a platform which separates out and manages it all as we see companies like Timescale doing.

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