Why we chose PostgreSQL over building a new database for IoT

The Untold Story of Timescale

Why PostgreSQL is Built to Last

PostgreSQL's journey since the 1980s is fascinating.

In this talk, Timescale CTO Mike Freedman explains how his team enhanced PostgreSQL's capabilities for IoT, real-time analytics, and AI applications.

🔹 How IoT pushed us to rethink databases
🔹 Why we built Timescale on PostgreSQL instead of creating a new database
🔹 How Hypertables and Hyperstore accelerate performance at scale
🔹 The future of PostgreSQL: powering AI and real-time data

How IoT pushed us to rethink databases

The rise of IoT has significantly transformed the data landscape, necessitating a reevaluation of traditional database systems.

IoT devices generate vast volumes of data, which traditional databases struggle to manage efficiently. This challenge led to the development of new data infrastructure solutions capable of handling the increased volume, velocity, and variety of IoT data.

TimescaleDB was born out of this need, initially focusing on solving IoT data management issues.

Why we built Timescale on PostgreSQL instead of creating a new database

Timescale chose to build its database on PostgreSQL rather than creating a new one — this decision was driven by the desire to leverage PostgreSQL's robustness and versatility while extending its capabilities to handle time-series data and real-time analytics.

By enhancing PostgreSQL, Timescale aimed to provide a full database platform rather than a niche solution, ensuring compatibility and familiarity for developers.

How Hypertables and Hyperstore accelerate performance at scale

TimescaleDB improves performance through two key features:

1. Hypertables: Hypertables automatically partition time-series data by time, enhancing insert and query performance.
2. Hyperstore: A hybrid row-columnar engine that accelerates queries on these hypertables while reducing costs through native columnar compression.

This architecture allows TimescaleDB to efficiently manage large-scale data, making it suitable for high-performance applications.

The future of PostgreSQL: powering AI and real-time data

With advancements like TimescaleDB and extensions such as pgvector, PostgreSQL is becoming a powerful platform for handling complex data workloads. The future of PostgreSQL involves integrating AI capabilities, enabling seamless real-time analytics, and providing a robust foundation for modern data-driven applications.

💻 𝗙𝗶𝗻𝗱 𝗨𝘀 𝗢𝗻𝗹𝗶𝗻𝗲!

🔍 Website ⇒ https://tsdb.co/homepage
🔍 Slack ⇒ https://slack.timescale.com
🔍 GitHub ⇒ https://github.com/timescale
🔍 Timescale Blog ⇒ https://tsdb.co/blog
🔍 Timescale Documentation ⇒ https://tsdb.co/docs