AI-Driven Real-Time Data Systems with Redis & TimescaleDB
Certificate of Completion by Code.Hub
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.
By the end of this module, participants will be able to:
- Design real-time data architectures using Redis and TimescaleDB
- 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
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
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
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
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
Roles:
- .NET Developer
- Backend Developer
- Software Engineer
- ΑI Engineer
Seniority:
- Junior
- Mid
- Basic understanding of databases, SQL, and backend development concepts
- Familiarity with APIs and general programming
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
Interested for
