Training: ETL 2 - Advanced Automation with ETL
Duration:
3 days (21 hours)
Learning Objectives:
- Master advanced automation of ETL processes.
- Manage complex ETL workflows and integrate them into existing systems.
- Optimize ETL performance for large data volumes.
- Implement error handling, recovery mechanisms, and monitoring.
- Learn to integrate ETL with scheduling tools, APIs, and cloud technologies.
Target Audience:
- ETL administrators, developers, data architects, and project managers with a solid understanding of basic ETL processes.
Prerequisites:
- Advanced knowledge of ETL (Extract, Transform, Load), common ETL tools (Talend, Informatica, SSIS, etc.), and databases.
Detailed Program
Day 1: Automating Complex ETL Processes
Morning: Introduction to Advanced Automation
- Review of ETL automation concepts.
- Scheduling and task management tools: cron, Apache Airflow, Talend Scheduler.
- Managing dependencies between ETL processes.
Practical Workshop:
- Setting up automated ETL flows using a scheduling tool (e.g., Talend or Airflow).
Afternoon: Complex ETL Flows
- Real-time vs. batch data processing.
- Integrating data from multiple and heterogeneous sources (databases, APIs, files).
- Automating continuous data integration (streaming).
Practical Workshop:
- Building a complex ETL flow with both real-time and batch data.
Day 2: Error Handling, Recovery, and Workflow Monitoring
Morning: Error Handling and Recovery
- Error handling mechanisms in ETL processes.
- Handling process failures and partial recoveries (checkpointing).
- Setting up incident recovery and error handling in complex ETL flows.
Practical Workshop:
- Implementing error handling and recovery mechanisms in an ETL flow.
Afternoon: Monitoring ETL Flows
- Performance monitoring of ETL workflows: logs, alerts, and metrics.
- ETL tracking tools: Talend Administration Center, Informatica Administrator, Airflow UI.
- Optimizing ETL performance: resource management and bottleneck reduction.
Practical Workshop:
- Implementing performance monitoring and control in an ETL workflow.
Day 3: Integration with APIs, Cloud, and Advanced Technologies
Morning: ETL Integration with APIs and External Tools
- Calling APIs and managing data exchanges between ETL and external systems.
- Integrating ETL with messaging systems (Kafka, RabbitMQ) for real-time flows.
- Securing exchanges and managing API authentications (OAuth, tokens).
Practical Workshop:
- Integrating ETL with an external API for data extraction and loading.
Afternoon: Automation and Deployment in the Cloud
- Deploying an ETL process in a cloud environment (AWS, Azure, GCP).
- Using cloud services for ETL orchestration and automation (AWS Glue, Azure Data Factory, Google Cloud Dataflow).
- Setting up cloud storage solutions for ETL processes (S3, Blob Storage, BigQuery).
Practical Workshop:
- Deploying an automated ETL workflow on a cloud platform (AWS, Azure, or GCP).
Teaching Methods:
- Theoretical sessions with live demonstrations.
- Hands-on workshops focused on real-world use cases and modern tools.
- Documentation and training materials provided.
Evaluation and Follow-up:
- Quizzes and practical evaluations to test acquired skills.
- Final project: fully implementing an automated and integrated ETL process.