TimescaleDB: Building Scalable Time-Series Applications

Learn to model, ingest, query, and visualize time-series data with TimescaleDB and Grafana through real-world projects.

What you'll learn
- Understand the fundamentals and importance of time-series data across industries.
- Set up and configure TimescaleDB on local, Docker, or cloud environments.
- Explain the architecture of TimescaleDB and how it extends PostgreSQL.
- Design efficient time-series data models using hypertables and chunks.
- Implement partitioning, indexing, and schema optimization for large datasets.
- Ingest time-series data from various sources — batch, streaming, or APIs.
- Write powerful SQL queries to analyze time-based trends and patterns.
- Compare query capabilities between TimescaleDB and InfluxDB.
- Apply data retention and compression techniques for efficient storage.
- Build real-time dashboards and alerts using Grafana and TimescaleDB.
- Utilize continuous aggregates for pre-computed analytics and reporting.
- Scale TimescaleDB for high-ingest workloads using multi-node architecture.
- Integrate TimescaleDB with Kafka, Airflow, Spark, and Prometheus.
- Implement security and access controls, encryption, and compliance practices.
- Optimize database performance and automate pipeline operations.
- Build and deploy a complete IoT Monitoring System using TimescaleDB and Grafana.

Requirements
- Enthusiasm and determination to make your mark on the world!

Description
A warm welcome toTimescaleDB for Data Engineers: Building Scalable Time-Series Applicationscourse byUplatz.

What is TimescaleDB?

TimescaleDBis an open-source time-series database built on top ofPostgreSQL. It is designed to efficiently store, manage, and analyze time-stamped or sequential data such as IoT sensor readings, financial transactions, application logs, and performance metrics.

TimescaleDB combines thescalability and speed of a time-series databasewith theflexibility and reliability of PostgreSQL, allowing you to use standard SQL for queries, joins, and analytics while benefiting from specialized optimizations for time-based data.

How TimescaleDB Works

1. Built on PostgreSQL

TimescaleDB operates as anextension to PostgreSQL, not a separate database engine.This means it inherits all PostgreSQL features, including:

ACID transactions

Rich SQL capabilities

Joins, indexing, and constraints

Compatibility with PostgreSQL drivers, ORMs, and tools

You can install it locally, in Docker, or in the cloud, and continue to use familiar SQL syntax.

2. Hypertables: The Core Concept

The central idea behind TimescaleDB is thehypertable.A hypertable is a logical table that automatically partitions your data into smaller, more manageablechunksbased on time (and optionally another dimension like device ID or region).

This allows TimescaleDB to:

Speed up inserts by writing only to the latest chunk

Optimize queries by scanning only the relevant chunks

Handle billions of rows without manual partitioning

In other words, a hypertable looks like a regular SQL table but scales like a distributed time-series database.

3. Chunks and Automatic Partitioning

Each chunk represents data for a specific time range (for example, one day or one week).When you insert new data, TimescaleDB automatically places it in the appropriate chunk.When you query the data, it fetches only the chunks that match your time filters, improving performance dramatically.

This approach allows TimescaleDB to efficiently manage very large datasets over long periods.

4. Continuous Aggregates

To speed up analytics, TimescaleDB supportscontinuous aggregates—automatically maintained materialized views that store precomputed results such as hourly averages or daily totals.This reduces the cost of repeatedly computing aggregates over large datasets and enables fast, real-time dashboarding.

5. Data Retention and Compression

TimescaleDB provides automated data lifecycle management:

Retention policiesallow you to automatically drop or archive old data after a set time period.

Compression policiesreduce storage usage by up to 90% while maintaining fast query performance.

These features make TimescaleDB cost-effective for long-term time-series storage.

6. Scalability

TimescaleDB supports both vertical and horizontal scaling:

Vertical scaling:Run on larger machines with more memory and CPU.

Horizontal scaling:Use a multi-node setup with one coordinator node (for query planning) and multiple data nodes (for storing chunks).

This enables it to scale to handle terabytes or even petabytes of time-series data.

7. Real-Time Querying and Analytics

Because TimescaleDB uses standard SQL, you can perform complex analytical queries such as:

Aggregating metrics over time intervals

Comparing data between time ranges

Joining time-series data with relational tables

You can use it with visualization tools likeGrafanato build dashboards and alerts for real-time monitoring.

8. Integrations and Ecosystem

TimescaleDB integrates easily with:

Grafanafor dashboards and visualization

Kafka or MQTTfor real-time ingestion

Apache Airflow or Sparkfor data pipelines

Python, R, or BI toolsfor analytics and reporting

This makes it suitable for use in data engineering, DevOps, IoT, and analytics environments.

In summary, TimescaleDB is ascalable, SQL-based database optimized for time-series workloads. It automates data partitioning, aggregation, compression, and retention—allowing organizations to handle high-ingest, large-scale, time-dependent data while maintaining full PostgreSQL compatibility.

Course Description

Time-series data is at the heart of today’s digital world — powering everything from IoT devices and financial systems to DevOps monitoring and analytics platforms.TimescaleDB, built on top of PostgreSQL, is one of the most powerful and scalable databases for handling time-stamped data efficiently.

This comprehensive course takes you fromthe fundamentals of time-series dataall the way tobuilding real-world analytical systemsusing TimescaleDB, PostgreSQL, and GrafanaYou’ll learn how to model, ingest, query, compress, and visualize data at scale — all while leveraging standard SQL and PostgreSQL’s reliability.

Through hands-on lessons and practical examples, you’ll gain the skills to design, deploy, and manage high-performance time-series solutions for modern data-driven applications.

What You’ll Learn

Understand what time-series data is and where it’s used

Install and configure TimescaleDB on local or cloud environments

Learn how TimescaleDB extends PostgreSQL with hypertables and chunks

Design optimal schemas for time-series data modeling

Ingest large volumes of data efficiently from multiple sources

Query and aggregate time-stamped data using SQL and continuous aggregates

Implement retention and compression policies for cost-effective storage

Build real-time dashboards and alerts with TimescaleDB + Grafana

Compare TimescaleDB and InfluxDB for hybrid time-series workloads

Scale TimescaleDB using multi-node architecture for enterprise use

Integrate TimescaleDB with Kafka, Airflow, Spark, and Prometheus

Implement user access control, encryption, and security policies

Build a completeIoT Monitoring Systemproject end-to-end

Who This Course Is For

Data Engineersbuilding real-time data pipelines and analytics systems

Database Administratorsoptimizing PostgreSQL for time-series workloads

Backend Developersmanaging event logs, metrics, and telemetry data

IoT Engineersstoring and analyzing sensor or device data efficiently

DevOps Engineers / SREsmonitoring system metrics using TimescaleDB and Grafana

Data Analysts and Scientistsworking with time-based datasets

Students or Tech Enthusiastswanting to master modern data infrastructure tools

Prerequisites

Basic understanding ofSQLandrelational databases(PostgreSQL familiarity helps)

Some experience withdata analysis,ETL, orbackend development

No prior knowledge of TimescaleDB required — the course starts from scratch

TimescaleDB for Data Engineers: Building Scalable Time-Series Applications - Course Curriculum

Lecture 1 – Getting Started

Introduction to TimescaleDB

Installation (Local, Docker, Cloud)

Overview of time-series concepts and architecture

Lecture 2 – Modeling Time-Series Data

Designing time-based schemas

Understanding hypertables and chunks

Indexing and optimizing schema performance

Lecture 3 – Ingesting Data

Batch and streaming ingestion techniques

Handling late or out-of-order data

Real-world ingestion examples

Lecture 4 – Querying Timestamp Data (TimescaleDB + InfluxDB)

Writing SQL queries for time-series analysis

Using continuous aggregates

Comparing TimescaleDB vs. InfluxDB queries

Lecture 5 – Data Retention & Compression

Implementing retention policies

Compressing and archiving historical data

Managing storage and performance balance

Lecture 6 – Real-Time Queries & Alerts (TimescaleDB + InfluxDB + Grafana)

Creating live dashboards with Grafana

Building real-time alerts and thresholds

Combining multiple data sources

Lecture 7 – Scaling TimescaleDB

Vertical and horizontal scaling

Multi-node setup and distributed hypertables

Performance tuning best practices

Lecture 8 – Integrations & Pipelines

Connecting with Kafka, Spark, and Airflow

Building ETL workflows

Automating analytics pipelines

Lecture 9 – Security & Access Control

Implementing user roles and permissions

Securing connections and encryption

Compliance and auditing

Lecture 10 – IoT Monitoring System Project

Designing an IoT data model

Ingesting and visualizing sensor data

Building a full IoT dashboard with TimescaleDB + Grafana

What Makes This Course Unique

Hands-on and project-driven:You’ll build real-world systems step-by-step.

SQL-based learning:Leverage PostgreSQL knowledge for modern time-series use cases.

Performance and scalability focus:Learn to handle billions of rows efficiently.

Covers end-to-end workflow:From data modeling to visualization and alerting.

After Completing This Course

You’ll be able to confidently design and implement scalable, real-time, and analytics-ready time-series data solutions usingTimescaleDB— ready to power applications in IoT, finance, DevOps, and beyond.

Who this course is for:
- Data Engineers – who need to build robust time-series pipelines for analytics and monitoring.
- Data Analysts – who want to query and visualize time-based data using SQL and Grafana.
- Data Scientists – who need to preprocess and analyze large temporal datasets.
- BI Professionals – adding time-series analytics to existing dashboards and reports.
- Database Administrators (DBAs) – looking to optimize PostgreSQL for time-series workloads.
- Backend Developers – integrating TimescaleDB into applications for real-time metrics and logs.
- IoT Engineers – managing high-frequency sensor or telemetry data efficiently.
- DevOps Engineers / SREs – working with performance metrics, monitoring data, and Grafana dashboards.
- Students & Tech Enthusiasts – interested in learning modern data infrastructure concepts.
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