Home Events AI-Driven Real-Time Data Systems with Redis & TimescaleDB

AI-Driven Real-Time Data Systems with Redis & TimescaleDB

Description

This training focuses on building real-time, AI-driven data systems using Redis for low-latency processing and TimescaleDB for time-series analytics. Participants will learn how to design architectures that ingest, process, and analyze streaming data while integrating Generative AI for intelligent insights and decision-making. The course covers event-driven systems, caching strategies, time-series modeling, and real-time pipelines. Hands-on labs demonstrate how to combine Redis streams, TimescaleDB queries, and LLMs to detect anomalies, generate insights, and automate responses. Emphasis is placed on performance, scalability, and system reliability in production environments. By the end of the course, participants will be able to build intelligent systems that operate on live data and provide actionable insights in real time.

 

🕒 Duration: 16 hours

👥 Target Audience:

  • Roles: .NET Developer, Backend Developer, Software Engineer, ΑI Engineer

 

  • Seniority: Junior, Mid
Webinar Content

 

Module 1: Foundations of Real-Time Systems
 Introduction
to
Real-Time Architectures
  • What is real-time data processing
  • Batch vs streaming systems
  • Use cases (IoT, finance, monitoring)

 

  • AI Practice: Use LLMs to design a real-time architecture for a business scenario
Redis Fundamentals
  • – Redis data structures
  • – Caching vs streaming
  • – Redis Pub/Sub and Streams

 

  • AI Practice: Generate Redis usage patterns for low-latency systems
Module 2: Time-Series Data Systems
 TimescaleDB Fundamentals
  • – Time-series data concepts
  • – Hypertables and indexing
  • – Writing efficient time-based queries

 

  • AI Practice: Generate optimized SQL queries for time-series analysis
Data Modeling for Time-Series
  • Schema design for metrics/events
  • Retention and compression
  • Query performance tuning

 

  • AI Practice: Design a time-series schema using AI suggestions
Module 3: Streaming & Integration
 Redis Streams Pipelines
  • Event ingestion with Redis Streams
  • Consumer groups
  • Stream processing patterns

 

  • AI Practice: Build a streaming pipeline using AI-generated logic
Redis + TimescaleDB Integration
  • Moving data from streams to storage
  • Real-time + historical analytics

 

  • AI Practice: Generate pipeline code for ingestion and storage
Module 4: AI Integration
 AI for Real-Time Insights
  • Using LLMs for anomaly detection
  • Pattern recognition in time-series
  • Alert generation

 

  • AI Practice: Use AI to detect anomalies in sample data streams
Capstone: Intelligent Real-Time
System
  • Build end-to-end system
  • Streaming + storage + AI insights
  • Dashboard/API integration

 

  • AI Practice: Develop a full AI-driven real-time application
Learning Objectives:

After attending this webinar participants will be able to:

  • Design real-time data architectures using Redis and TimescaleDBs
  • Implement streaming pipelines and event-driven processing systems
  • Integrate AI models to generate insights from live and time-series data
  • Build anomaly detection and alerting systems using AI-enhanced logic
  • Optimize performance and scalability for real-time data applications
Prerequisite Knowledge
  • Basic understanding of databases, SQL, and backend development concepts
  • Familiarity with APIs and general programming

 

Tags:

Date

28 May 2026, 16:00-19:00

Cost

Free

Labels

AI-Native
Category

Organizer

Code.Hub
Email
[email protected]

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