- Basic IT knowledge
Audience
- IT professionals
ETL is a data integration process commonly used in data warehousing.
This instructor-led, live training (online or onsite) is aimed at beginner-level IT professionals who wish to gain a fundamental understanding of ETL processes within the IT context.
By the end of this training, participants will be able to:
- Understand the basics of ETL processes and their role in IT.
- Learn about different ETL tools and modules.
- Gain hands-on experience in designing and implementing ETL workflows.
- Understand data quality, transformation techniques, and data loading best practices.
- Explore ETL monitoring, troubleshooting, and automation techniques.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Introduction to IT and Data Management
- Overview of IT in data management
- Importance of data integration in IT projects
- Introduction to data warehousing and business intelligence
Understanding ETL
- What is ETL? (Extract, Transform, Load)
- Differences between ETL and ELT
- Typical ETL process flow and architecture
Extract Phase: Data Extraction
- Sources of data (databases, APIs, files, etc.)
- Data extraction techniques and tools
- Handling structured and unstructured data
Transform Phase: Data Transformation
- Data cleansing, validation, and normalization
- Aggregation and calculation techniques
- Managing data quality and consistency
Load Phase: Data Loading
- Loading data into different types of storage (data warehouses, data lakes)
- Incremental vs. full data loads
- Handling loading errors and conflicts
Designing a Basic ETL Workflow
- Setting up an ETL tool
- Building a simple ETL pipeline from extraction to loading
- Working with sample data for practical understanding
Advanced ETL Techniques
- Real-time vs. batch processing
- Working with different data formats (JSON, XML, CSV)
- Data partitioning and indexing for performance
ETL Tools and Modules Overview
- Review of popular ETL tools (Informatica, Talend, Apache Nifi, SSIS)
- Open-source vs. commercial solutions
- Integration with cloud platforms (AWS Glue, Google Dataflow, Azure Data Factory)
Automation and Scheduling
- Scheduling ETL jobs
- Using cron jobs and scheduling tools
- Automating error handling and retries
Monitoring and Troubleshooting ETL Processes
- Logging and monitoring ETL workflows
- Common issues and solutions in ETL processes
- Performance tuning tips
Data Governance and Compliance in ETL
- Ensuring data privacy and security
- Managing metadata and data lineage
- Compliance with data regulations (GDPR, HIPAA)
Building an Advanced ETL Workflow
- Implementing transformations and data quality checks
- Connecting multiple data sources
- Deploying ETL workflows to production
Summary and Next Steps