TimescaleDB: Building Scalable Time-Series Applications
Date: 21 December 2025 | Posted by: AD-TEAM
TimescaleDB: Building Scalable Time-Series Applications
Last updated 10/2025
Duration: 8h 33m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 4.78 GB
Genre: eLearning | Language: English
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.
More Info
RapidGator
https://rapidgator.net/file/806d99d004d13e981b0e3923ca1e9eae/timescaledb.building.scalable.timeseries.applications.part1.rar
https://rapidgator.net/file/90320d35ac74ea5123062dc0d0a0cb99/timescaledb.building.scalable.timeseries.applications.part2.rar
https://rapidgator.net/file/cda4af62a9f266fcbc46915d9bfb31e3/timescaledb.building.scalable.timeseries.applications.part3.rar
https://rapidgator.net/file/99d3713ce797644a634141eb9ce79484/timescaledb.building.scalable.timeseries.applications.part4.rar
https://rapidgator.net/file/f43f4b021d92e6d9aa570bef8a798052/timescaledb.building.scalable.timeseries.applications.part5.rar
NitroFlare
https://nitroflare.com/view/601FE941870B973/timescaledb.building.scalable.timeseries.applications.part1.rar
https://nitroflare.com/view/0941526801905F8/timescaledb.building.scalable.timeseries.applications.part2.rar
https://nitroflare.com/view/236FE271D3BB696/timescaledb.building.scalable.timeseries.applications.part3.rar
https://nitroflare.com/view/49E22AAB437A185/timescaledb.building.scalable.timeseries.applications.part4.rar
https://nitroflare.com/view/C0EEE6B041BB712/timescaledb.building.scalable.timeseries.applications.part5.rar
DDownload
https://ddownload.com/iiolzele3nle/timescaledb.building.scalable.timeseries.applications.part1.rar
https://ddownload.com/sfo2v9cpdbj6/timescaledb.building.scalable.timeseries.applications.part2.rar
https://ddownload.com/01o4edfkv966/timescaledb.building.scalable.timeseries.applications.part3.rar
https://ddownload.com/89kzqia74fmt/timescaledb.building.scalable.timeseries.applications.part4.rar
https://ddownload.com/15cvs0igdvme/timescaledb.building.scalable.timeseries.applications.part5.rar
See full article