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TimescaleDB Course – PostgreSQL for Time-Series Data Analytics Table
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About TimescaleDB Course – PostgreSQL for Time-Series Data
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Learn how to supercharge PostgreSQL for time-series data and heavy analytics workloads using the TimescaleDB extension. In this comprehensive course, you will master hypertables, continuous aggregates, and columnar storage by building real-world projects like an AI agent flight recorder and an EV fleet telemetry dashboard. Created by @beau 🏗️ Tiger Data provided a grant to make this course possible. Get $1,000 credit on Tiger Cloud to follow along: https://tsdb.co/fcc-learn-tsdb Chapters - 0:00:00 Introduction & Database Comparison - 0:01:06 Course Overview - 0:01:28 Sponsor (Tiger Data & Tigercloud) - 0:02:27 What is Time Series Data? - 0:04:19 Projects Overview - 0:05:24 It's Just PostgreSQL - 0:06:54 Example Data Setup - 0:10:20 The 20% of SQL You Need - 0:19:34 Understanding EXPLAIN & ANALYZE - 0:24:59 Pages & Row Storage - 0:26:09 MVCC, Dead Rows & Vacuuming - 0:29:27 Native Postgres Partitioning vs. TimescaleDB - 0:32:42 What is a Hypertable? - 0:33:14 Materialized Views & Summary Tables - 0:37:05 The Dashboard Problem (Web Analytics Scenario) - 0:44:14 Creating a Hypertable - 0:48:43 The Golden Rule: Always Filter by Time - 0:49:31 How Big Should a Chunk Be? - 0:54:21 Testing Hypertable Performance - 0:59:14 Indexing & Chunk Skipping - 1:03:50 Global Unique Indexes & UUID v7 - 1:13:45 Data Locality & Reordering - 1:15:42 The Column Store - 1:18:36 Columnar Compression Techniques - 1:22:58 Segment By & Order By - 1:26:15 Converting Chunks to Column Store - 1:29:03 Compressed Indexes & Bloom Filters - 1:32:27 Continuous Aggregates - 1:38:08 Real-Time Aggregates - 1:40:30 Aggregate Ladders - 1:44:50 Hyperfunctions & Gap Filling - 1:50:16 State Aggregates - 1:52:28 Approximate Aggregates & Percentiles - 1:55:58 HyperLogLog (Approximate Count Distinct) - 1:57:26 Other Toolkit Features - 1:59:30 Data Retention Policies - 2:04:47 Tiered Storage - 2:08:42 Docker Setup & Environment Configuration - 2:16:35 Project 1: AI Agent Flight Recorder - 2:22:04 Project 1: Schema Design - 2:28:22 Project 1: Ingestion Script & Batch Copy - 2:35:45 Project 1: Optimizing the Database - 2:50:56 Project 1: Querying Raw Data - 3:00:13 Project 1: API & Dashboard Integration - 3:15:54 Project 2: EV Fleet Telemetry & TigerCloud - 3:22:20 Project 2: Schema Design - 3:25:52 Project 2: The Analytics Join Myth - 3:27:26 Project 2: Segment By Benchmarking - 3:42:32 Project 2: Finding Anomalies - 4:02:13 Project 2: Semantic Search (pgvector) - 4:08:47 TigerCloud Console Overview - 4:14:26 12-Step Production Checklist
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