Free Download TimescaleDB Building Scalable Time-Series Applications
Published 10/2025
Created by Uplatz Training
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 10 Lectures ( 8h 32m ) | Size: 4.8 GB
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 to TimescaleDB for Data Engineers: Building Scalable Time-Series Applications course by Uplatz.What is TimescaleDB?TimescaleDB is an open-source time-series database built on top of PostgreSQL. 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 the scalability and speed of a time-series database with the flexibility 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 Works1. Built on PostgreSQLTimescaleDB operates as an extension to PostgreSQL, not a separate database engine.This means it inherits all PostgreSQL features, including:ACID transactionsRich SQL capabilitiesJoins, indexing, and constraintsCompatibility with PostgreSQL drivers, ORMs, and toolsYou can install it locally, in Docker, or in the cloud, and continue to use familiar SQL syntax.2. Hypertables: The Core ConceptThe central idea behind TimescaleDB is the hypertable.A hypertable is a logical table that automatically partitions your data into smaller, more manageable chunks based on time (and optionally another dimension like device ID or region).This allows TimescaleDB to:Speed up inserts by writing only to the latest chunkOptimize queries by scanning only the relevant chunksHandle billions of rows without manual partitioningIn other words, a hypertable looks like a regular SQL table but scales like a distributed time-series database.3. Chunks and Automatic PartitioningEach 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 AggregatesTo speed up analytics, TimescaleDB supports continuous 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 CompressionTimescaleDB provides automated data lifecycle management:Retention policies allow you to automatically drop or archive old data after a set time period.Compression policies reduce storage usage by up to 90% while maintaining fast query performance.These features make TimescaleDB cost-effective for long-term time-series storage.6. ScalabilityTimescaleDB 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 AnalyticsBecause TimescaleDB uses standard SQL, you can perform complex analytical queries such as:Aggregating metrics over time intervalsComparing data between time rangesJoining time-series data with relational tablesYou can use it with visualization tools like Grafana to build dashboards and alerts for real-time monitoring.8. Integrations and EcosystemTimescaleDB integrates easily with:Grafana for dashboards and visualizationKafka or MQTT for real-time ingestionApache Airflow or Spark for data pipelinesPython, R, or BI tools for analytics and reportingThis makes it suitable for use in data engineering, DevOps, IoT, and analytics environments.In summary, TimescaleDB is a scalable, 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 DescriptionTime-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 from the fundamentals of time-series data all the way to building real-world analytical systems using 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 LearnUnderstand what time-series data is and where it's usedInstall and configure TimescaleDB on local or cloud environmentsLearn how TimescaleDB extends PostgreSQL with hypertables and chunksDesign optimal schemas for time-series data modelingIngest large volumes of data efficiently from multiple sourcesQuery and aggregate time-stamped data using SQL and continuous aggregatesImplement retention and compression policies for cost-effective storageBuild real-time dashboards and alerts with TimescaleDB + GrafanaCompare TimescaleDB and InfluxDB for hybrid time-series workloadsScale TimescaleDB using multi-node architecture for enterprise useIntegrate TimescaleDB with Kafka, Airflow, Spark, and PrometheusImplement user access control, encryption, and security policiesBuild a complete IoT Monitoring System project end-to-endWho This Course Is ForData Engineers building real-time data pipelines and analytics systemsDatabase Administrators optimizing PostgreSQL for time-series workloadsBackend Developers managing event logs, metrics, and telemetry dataIoT Engineers storing and analyzing sensor or device data efficientlyDevOps Engineers / SREs monitoring system metrics using TimescaleDB and GrafanaData Analysts and Scientists working with time-based datasetsStudents or Tech Enthusiasts wanting to master modern data infrastructure toolsPrerequisitesBasic understanding of SQL and relational databases (PostgreSQL familiarity helps)Some experience with data analysis, ETL, or backend developmentNo prior knowledge of TimescaleDB required - the course starts from scratchTimescaleDB for Data Engineers: Building Scalable Time-Series Applications - Course CurriculumLecture 1 - Getting StartedIntroduction to TimescaleDBInstallation (Local, Docker, Cloud)Overview of time-series concepts and architectureLecture 2 - Modeling Time-Series DataDesigning time-based schemasUnderstanding hypertables and chunksIndexing and optimizing schema performanceLecture 3 - Ingesting DataBatch and streaming ingestion techniquesHandling late or out-of-order dataReal-world ingestion examplesLecture 4 - Querying Timestamp Data (TimescaleDB + InfluxDB)Writing SQL queries for time-series analysisUsing continuous aggregatesComparing TimescaleDB vs. InfluxDB queriesLecture 5 - Data Retention & CompressionImplementing retention policiesCompressing and archiving historical dataManaging storage and performance balanceLecture 6 - Real-Time Queries & Alerts (TimescaleDB + InfluxDB + Grafana)Creating live dashboards with GrafanaBuilding real-time alerts and thresholdsCombining multiple data sourcesLecture 7 - Scaling TimescaleDBVertical and horizontal scalingMulti-node setup and distributed hypertablesPerformance tuning best practicesLecture 8 - Integrations & PipelinesConnecting with Kafka, Spark, and AirflowBuilding ETL workflowsAutomating analytics pipelinesLecture 9 - Security & Access ControlImplementing user roles and permissionsSecuring connections and encryptionCompliance and auditingLecture 10 - IoT Monitoring System ProjectDesigning an IoT data modelIngesting and visualizing sensor dataBuilding a full IoT dashboard with TimescaleDB + GrafanaWhat Makes This Course UniqueHands-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 CourseYou'll be able to confidently design and implement scalable, real-time, and analytics-ready time-series data solutions using TimescaleDB - 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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