Mastering Time Series Data Management with Postgres + TimescaleDB

Time series data is a type of data that is collected over time, often in regular intervals. It is commonly used in various applications such as financial trading, IoT, sensor data, weather data, etc. While postgres can handle any data effectively, when it comes to time-series data, timescaleDB is a class apart. Built as an extension of postgres, timescaleDB is one of the most popular extensions in postgres and that excludes its popularity in handling time-series data.
In this talk, we'll discuss:
- What is timescaleDB?
- Setting and installing timescaleDB (we'll use docker image)
- Load real-world data into the database
- Creating a hypertable. What's happening under the hood? and compare to a normal postgres table
- Using Continous Aggregates
- Using timescaleDB compression
- A path forward


After this talk, you'll have an understanding of some nuances, challenges around time-series and how to handle it effectively using timescaleDB

Jashanpreet is a highly skilled Full Stack Developer with over 5 years of experience in developing robust applications. He has worked on a variety of projects, spanning different industries and domains. Throughout his career, Jashanpreet has consistently chosen Postgres as his primary database due to its open-source nature, outstanding performance, ease of use, and versatility in handling various use cases such as geospatial and financial data.