TimescaleDB
Unlock Scalable Time-Series Analytics with TimescaleDB – Combine SQL Power with Time-Series PerformancePreview TimescaleDB course
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A real-time DevOps monitoring solution using Grafana
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An energy consumption dashboard with continuous aggregates
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A financial tick data pipeline with automatic compression
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Install and configure TimescaleDB
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Create and manage hypertables for time-series efficiency
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Query large-scale time-series data using advanced SQL
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Set up retention policies and compression
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Integrate with Grafana for dashboarding
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Use PostgreSQL tooling with time-series enhancements
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PostgreSQL users working with time-stamped data
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DevOps and SREs building scalable monitoring solutions
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Data engineers managing IoT or telemetry data
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Finance analysts handling real-time pricing feeds
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Anyone looking to scale SQL-based analytics on time-series workloads
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Start with PostgreSQL Basics – Revisit core SQL commands if needed
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Use SQL for Everything – Leverage TimescaleDB’s SQL-native design
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Build Hypertables Early – Practice time-based partitioning for performance
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Visualize Your Data – Connect TimescaleDB to Grafana for real-time insights
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Implement Retention and Compression – Keep storage under control
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Experiment with Continuous Aggregates – Optimize repetitive queries
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Monitor with pg_stat_statements – Track query performance and usage
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
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Install and configure TimescaleDB locally and in the cloud
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Create hypertables to store time-series data efficiently
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Use SQL to query time-series data with filters, grouping, and joins
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Manage large datasets with retention and native compression
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Create real-time dashboards using TimescaleDB + Grafana
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Implement continuous aggregates to accelerate queries
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Tune performance using indexes and analyze query execution plans
Course Syllabus
Module 1: Introduction to Time-Series and TimescaleDB
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Time-Series vs Relational Data
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What is TimescaleDB?
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Use Cases and Benefits
Module 2: Installing TimescaleDB
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Installation on Linux, Mac, Docker
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Cloud Deployment on AWS/RDS
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Integrating with Existing PostgreSQL Databases
Module 3: TimescaleDB Architecture and Concepts
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Hypertables and Chunking
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Partitioning by Time and Space
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Comparison with PostgreSQL Native Tables
Module 4: Writing and Querying Data
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Inserting and Updating Time-Series Data
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Querying with SQL: filters, aggregates, time functions
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Using Window Functions and Time Buckets
Module 5: Indexing, Retention, and Compression
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Index Strategies for Time-Series
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Creating Retention Policies
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Native Compression for Storage Optimization
Module 6: Continuous Aggregates
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Introduction to Materialized Views
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Creating and Refreshing Continuous Aggregates
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Performance Tuning for Aggregates
Module 7: Advanced SQL for Time-Series
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Time-Bucket Join and Gap Filling
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Anomaly Detection with SQL
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Time Shifting and Time Zone Adjustments
Module 8: Visualizing with Grafana
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Connecting TimescaleDB to Grafana
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Building Time-Series Dashboards
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Alerting and Thresholds
Module 9: Maintenance and Monitoring
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Analyzing Query Plans with EXPLAIN
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Using pg_stat_statements
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Backups, Security, and Scaling
Module 10: Projects and Capstone Exercises
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Energy Usage Tracker
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DevOps Metrics Dashboard
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Financial Market Data Monitor
Module 11: TimescaleDB Interview Questions & Answers
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Architecture and Querying
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Optimization and Scaling
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Integration Scenarios
Upon successful completion, learners will receive a Certificate of Completion from Uplatz that validates their proficiency in working with time-series data using TimescaleDB. The certification is ideal for professionals seeking roles in time-series analytics, PostgreSQL development, or data infrastructure engineering.
With the rapid growth of real-time analytics in every industry, TimescaleDB is becoming a top choice for managing time-based data in a scalable way. This course prepares you for roles such as:
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Time-Series Data Engineer
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PostgreSQL Developer
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DevOps Data Analyst
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IoT Analytics Engineer
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Infrastructure Monitoring Specialist
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What is TimescaleDB and how is it different from PostgreSQL?
Answer: TimescaleDB is a time-series database built on PostgreSQL. It extends PostgreSQL with hypertables, time-based partitioning, compression, and continuous aggregates, enabling it to handle time-series data more efficiently than standard PostgreSQL tables. -
What is a hypertable in TimescaleDB?
Answer: A hypertable is a logical abstraction of a time-series table, automatically partitioned into chunks based on time (and optionally space). It enables scalable inserts and fast queries over time-based data. -
How does TimescaleDB handle high-volume time-series inserts?
Answer: TimescaleDB partitions data into smaller chunks using hypertables, allowing efficient storage and retrieval. It also uses indexes and background workers for query and insert optimization. -
What are continuous aggregates and how do they help?
Answer: Continuous aggregates are materialized views that automatically refresh on a schedule. They reduce computation time by pre-aggregating commonly queried metrics (e.g., hourly averages). -
How is data retention managed in TimescaleDB?
Answer: You can define retention policies to automatically drop old chunks of data after a specified time interval, saving storage and improving performance. -
What kind of compression does TimescaleDB offer?
Answer: TimescaleDB provides native columnar compression for older data chunks. It reduces storage and improves query speed, especially on historical data. -
Can TimescaleDB be used with Grafana?
Answer: Yes, Grafana can connect to TimescaleDB as a PostgreSQL source. It allows you to create rich visualizations and alerts for real-time and historical metrics. -
What is the time_bucket() function used for?
Answer: Thetime_bucket()
function groups time-series data into fixed-size intervals, enabling aggregation like average, sum, or count over consistent time windows (e.g., 10-minute buckets). -
How does TimescaleDB maintain SQL compatibility?
Answer: TimescaleDB is a PostgreSQL extension, so all SQL features and tools available in PostgreSQL work seamlessly with TimescaleDB, including joins, indexes, triggers, and stored procedures. -
What are the advantages of TimescaleDB for IoT or DevOps monitoring?
Answer: TimescaleDB offers high ingest speed, time-based queries, retention policies, and seamless visualization integration—making it ideal for storing and querying high-frequency time-stamped metrics.