Simplifying data analytics by creating continuously up-to-date aggregates
Who is this presentation for?Data engineers, data architects, developers
One characteristic of time series data workloads (e.g., data from servers, sensors, IoT devices, finance, or even satellites) is that the dataset grows very quickly.
So given this high velocity and continuous workload of handling streams of time series data, insert performance is paramount. But what’s the use of inserting and storing a significant amount of data if you can’t analyze, visualize, and act on it in real time? Or if you’re limited to a snapshot or summary over the most recent data rather than the ability to gain arbitrary insights over time. Historical aggregates are key.
David Kohn details how to transparently and efficiently maintain continuous data aggregates over time, which is critical for speeding up queries using precalculated results. Yet in many time series settings such as IoT devices, server monitoring, even financial applications, data may arrive late or out of order, which typically leads to incorrect results or triggers expensive recomputation on every late input. You’ll discover how such late data is correctly and efficiently handled in TimescaleDB’s continuous aggregates and how this simplifies building data-intensive time series applications.
- General knowledge of databases or streaming data architectures
What you'll learn
- Learn why it’s important to implement the proper infrastructure when managing time series workloads
- Discover how creating continuously up-to-date aggregates can speed up query performance and lead to more useful analysis
After a BS in environmental engineering at Yale, founding an electrochemistry startup, joining a battery startup, and doing crazy things with PostgreSQL for Moat (an ad-analytics company), David joined Timescale to focus on research and development. He also cooks, does pottery and builds furniture.
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