This repository contains materials for various workshops created by TigerData, demonstrating the power of TimescaleDB for different applications.
-
TigerData Cloud account (get free 30 days at https://console.cloud.timescale.com/signup)
-
Optional, but recommended - install psql CLI https://www.tigerdata.com/blog/how-to-install-psql-on-mac-ubuntu-debian-windows
Most workshops run as a single .sql script (via psql or the TigerData Console). Where noted, a workshop includes a small Python helper or generates/downloads its own data — see each workshop's README.md for the exact steps.
Build a search engine over EV charging-station maintenance reports and compare three approaches in PostgreSQL: BM25 keyword search (pg_textsearch), vector semantic search (pgvector HNSW), and hybrid search that fuses the two with Reciprocal Rank Fusion. Embeddings are generated by a small local model — no API key required.
>> Go to the Hybrid Search Workshop
Analyze real cryptocurrency tick data: convert a raw ticks table into a hypertable, build OHLCV candlestick views, apply columnar compression, and pre-aggregate with continuous aggregates. Includes both a psql version (auto-downloads the dataset) and a Console/UI version (S3 import).
Build a production-grade well-monitoring database from 15-minute wellhead telemetry: hypertables, columnar compression, daily continuous aggregates, tiered storage, retention policies, and PostGIS spatial queries (wells within a radius, nearest wells). Includes an optional Grafana dashboard. Self-generating data.
>> Go to the Well Production Monitoring Workshop
Analyze high-frequency (5-second) hydraulic-fracturing stage telemetry: per-stage hypertables with sparse indexes, columnar compression, 1-minute continuous aggregates, and design-vs-actual joins against the engineered pump schedule. Self-generating data.
>> Go to the Completion Monitoring Workshop
Simulate an IoT sensor dataset and run time-series analytics on it: hypertables, configurable sparse indexes, columnar compression, continuous aggregates, tiered storage, and retention. Self-generating data.
>> Go to the IoT Data Workshop
Model Overall Equipment Effectiveness (OEE) for three production lines: a high-frequency OEE + environmental hypertable, columnar compression, and a 1-hour continuous aggregate for instant plant-level analytics. Self-generating data.
Track health metrics from wearable devices (heart rate, blood pressure, SpO₂, temperature, steps, sleep): hypertables with bloom/minmax sparse indexes, columnar compression, continuous aggregates, and health-analytics queries. Self-generating data (~2M rows).
>> Go to the Biowearables Workshop
Ingest and analyze real Bitcoin blockchain data (blocks 900,000–900,999): hypertables with columnar compression, address balance and fund-flow tracing, and coinbase/miner analysis. The script downloads the dataset at runtime. Includes an optional agentic (MCP) analysis section.
>> Go to the Blockchain Workshop
Use the Tiger MCP server with an AI coding assistant (e.g., Claude Code) to design an optimized schema, analyze time-series data, and perform database-optimization tasks through natural-language prompts. Prompt-driven rather than a fixed SQL script; includes a Python generator for the sample dataset.
>> Go to the Agentic Postgres Workshop
To get started, navigate to the directory of the workshop you are interested in and follow the instructions in its respective README.md file.
This project is licensed under the MIT License - see the LICENSE file for details.