QuestDB is an open-source SQL database designed to process time-series data. They recently secured a $2.3 million seed round, as well as the talents of David Simmons, their new Head of Developer Relations. A long time RIoT member and supporter, I had heard a lot of great things about David’s talks and was excited to experience my first one. He did not disappoint.
Data First, Things Second. David believes it’s not really the Internet of ‘Things’ but the Internet of ‘DATA.’ When the term IoT was coined, it truly was about the actual things and how they connected us and connected to other connected devices. Overtime, we have discovered that the things are great! They make our lives simpler in a lot of ways, our jobs easier, and make our large world feel a little smaller. But, what really matters is the data we collect with the things. So why data? David says that part is easy to understand. Data enables us to predict the future. Paging Nostradamus!
Time-series data is basically any data that has a timestamp. Traditionally it is a high flow, constant stream of data. Think of monitoring temperature changes throughout the day- often taken minute intervals. David says time-series data makes it easier to predict the future by understanding the past. We collect data to model systems we are implementing and use that data to make predictions and solve complex problems. Things like a digital twin, which is a digital replica of an ‘object’ that is created by using software and data, become incredibly valuable. For instance, engineers designing jet engines no longer have to sit on a plane to understand how it functions. A digital twin makes a real life digital representation.
The second half of David’s talk was filled with thought provoking questions and fascinating demos. Take a few minutes to watch David and learn more about QuestDB. One final piece of information that was my key takeaway is that data must be actionable. David ultimately says that to get the right data you need to understand what answers you’re looking for. What data do you need to get those answers? And what kinds of devices do you need to collect the data, to get the answers you’re looking for?