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title: Best TimescaleDB posts of 2024 | daily.dev
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# Best of TimescaleDB — 2024

1. 1  
[](/posts/building-a-high-performance-sensor-data-api-with-fastapi-and-postgres-timescaledb-extension-gvfsxlgvo "Building a High-Performance Sensor Data API with FastAPI and Postgres' TimescaleDB Extension")  
Article  
![Avatar of community](https://media.daily.dev/image/upload/t_logo,f_auto/v1655817725/logos/community)Community Picks · 2y  
Building a High-Performance Sensor Data API with FastAPI and Postgres' TimescaleDB Extension  
This guide helps you build a high-performance API for streaming, storing, and querying sensor data using FastAPI and TimescaleDB. It covers setting up the project, database, and creating API endpoints that efficiently manage and analyze sensor data, leveraging TimescaleDB's advanced time-series features for optimal performance.  
29
2. 2  
[](/posts/timescale-timescaledb-an-open-source-time-series-sql-database-optimized-for-fast-ingest-and-complex-q1pvlxtcb "timescale/timescaledb: An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.")  
Article  
![Avatar of community](https://media.daily.dev/image/upload/t_logo,f_auto/v1655817725/logos/community)Community Picks · 2y  
timescale/timescaledb: An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.  
TimescaleDB is an open-source, time-series SQL database built as a PostgreSQL extension, offering automatic partitioning and full SQL support. It presents data as hypertables, which are abstractions of many individual tables, making management and usage similar to standard PostgreSQL. Timescale also offers a fully managed cloud version with a 30-day free trial. The post includes details on creating tables, converting them to hypertables, and performing SQL queries.  
19
3. 3  
[](/posts/industrial-iot-visualization-why-united-manufacturing-hub-chose-grafana-to-power-its-iiot-platform-qc39nsjjn "Industrial IoT visualization: Why United Manufacturing Hub chose Grafana to power its IIoT platform")  
Article  
![Avatar of grafana](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/769cb8df467e4c428abd75e0c2491d1d)Grafana Labs · 2y  
Industrial IoT visualization: Why United Manufacturing Hub chose Grafana to power its IIoT platform  
United Manufacturing Hub (UMH) has chosen Grafana for its industrial IoT (IIoT) platform due to its flexibility, cost-effectiveness, and ease of use. Unlike traditional industrial tools, Grafana offers visually compelling dashboards and an extensive online community for support. Integrating seamlessly with TimescaleDB, it allows for real-time data visualization from various sources. While Grafana cannot directly interface with industrial controllers, workarounds exist to facilitate this communication. UMH also leverages Grafana Cloud for monitoring internal systems.  
16
4. 4  
[](/posts/postgresql-data-cleaning-vs-python-data-cleaning-ptvdx6a37 "PostgreSQL Data Cleaning vs. Python Data Cleaning")  
Article  
![Avatar of tigerdata](https://media.daily.dev/image/upload/s--bsnY3goS--/f_auto/v1756960371/squads/timescale?_a=BAMClqZW0)TigerData (Creators of TimescaleDB) · 2y  
PostgreSQL Data Cleaning vs. Python Data Cleaning  
This post explores how PostgreSQL and TimescaleDB can be used for efficient data cleaning tasks, replacing the need for tools like Excel, R, or Python. By cleaning data directly within the database, tasks can be performed more efficiently, saving time in the long run.  
12  
1

[See all TimescaleDB archives](/tags/timescaledb/best-of)

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