Introduction

Extract, Transform, Load (ETL) processes are the backbone of modern data integration, analytics, and business intelligence systems. As businesses generate vast amounts of structured and unstructured data, efficient ETL pipelines ensure data accuracy, quality, and accessibility for decision-making.

This 5-day hands-on training course provides a deep dive into modern ETL architectures, tools, and best practices. Participants will learn to design, develop, and optimize ETL pipelines using leading ETL tools (Apache NiFi, Talend, dbt, Airflow, and cloud-native solutions like AWS Glue, Azure Data Factory, and Google Dataflow).

Objectives

By the end of this course, participants will be able to:

• Understand the fundamentals of ETL and modern ELT paradigms.
• Design scalable and efficient ETL pipelines for structured and unstructured data.
• Work with batch and real-time data ingestion techniques.
• Optimize performance, cost-efficiency, and automation in ETL processes.
• Implement data governance, quality, and security in ETL workflows.
• Use leading ETL tools such as Apache NiFi, dbt, Talend, Apache Airflow, AWS Glue, and Azure Data Factory.
• Integrate ETL with Data Warehouses (Snowflake, Redshift, BigQuery) and Data Lakes.
• Understand future trends in ETL automation, AI-driven data transformation, and DataOps.

Who Should Attend?

This course is ideal for:

• Data Engineers designing and building ETL pipelines.
• Data Architects implementing scalable data integration strategies.
• BI & Analytics Professionals working with ETL for reporting and insights.
• Database Administrators (DBAs) managing data migrations and integrations.
• Cloud Engineers & DevOps Teams handling cloud-based ETL workflows.
• IT Managers & CTOs looking to modernize data pipelines.

Day 1: ETL Fundamentals & Architecture

• Introduction to ETL & Data Integration

  • What is ETL? How does it differ from ELT?
  • ETL vs. ELT: When to use each approach
  • Traditional vs. modern ETL architectures

• Understanding ETL Components

  • Extract: Data sources, APIs, databases, streaming data
  • Transform: Data cleansing, validation, enrichment, aggregation
  • Load: Staging vs. final destination (Data Warehouse, Data Lake)

• ETL System Design & Scalability

  • Batch vs. real-time ETL processing
  • Microservices vs. monolithic ETL frameworks
  • On-premises vs. cloud-based ETL

• Hands-on Lab:

  • Designing a simple ETL pipeline using Apache NiFi

Day 2: ETL Tools & Technologies

• Open-Source & Cloud-Native ETL Tools

  • Apache NiFi, Talend, Apache Airflow, dbt
  • Cloud ETL: AWS Glue, Azure Data Factory, Google Dataflow
  • Comparing tools: Strengths, weaknesses, and use cases

• Data Extraction Techniques

  • Working with APIs (REST, GraphQL)
  • Streaming data extraction (Kafka, Flink, Spark Streaming)
  • Extracting data from legacy databases

• Data Transformation Best Practices

  • Data quality checks and validation techniques
  • Schema evolution and handling changes in data sources
  • ETL debugging and monitoring strategies

• Hands-on Lab:

  • Building an ETL pipeline using Talend to extract and transform data

Day 3: ETL Optimization, Automation & Governance

• Performance Optimization Strategies

  • Parallel processing and distributed computing
  • Data partitioning, indexing, and caching for faster ETL
  • Optimizing ETL for cloud-based architectures

• Data Governance in ETL Pipelines

  • Data lineage tracking and metadata management
  • Role-based access control (RBAC) for ETL processes
  • Ensuring compliance (GDPR, HIPAA, SOC 2) in ETL workflows

• Automating ETL with Apache Airflow & Orchestration Tools

  • Introduction to workflow orchestration
  • Scheduling, monitoring, and error handling in Apache Airflow
  • Automating ETL tasks with CI/CD pipelines

• Hands-on Lab:

  • Implementing an end-to-end ETL pipeline in Apache Airflow

Day 4: Cloud ETL & Integration with Data Warehouses & Data Lakes

• ETL for Cloud Data Warehouses

  • Connecting ETL pipelines to Snowflake, Redshift, BigQuery
  • Best practices for ELT in cloud data warehousing

• ETL for Data Lakes & Lakehouses

  • Ingesting structured and unstructured data into Data Lakes
  • Using Delta Lake, Apache Hudi, and Iceberg for ETL pipelines
  • Real-time streaming ETL with AWS Kinesis and Azure Event Hubs

• ETL & AI/ML Data Preparation

  • Automating data transformation for AI & Machine Learning
  • Feature engineering in ETL pipelines
  • Leveraging AI-driven ETL (DataRobot, Google AutoML)

• Hands-on Lab:

  • Integrating an ETL pipeline with Snowflake and a Data Lake

Day 5: Future Trends, Best Practices & Final Project

• Emerging Trends in ETL & DataOps

  • The rise of ETL as a Service (ETLaaS)
  • AI-driven ETL automation and self-healing pipelines
  • Low-code and no-code ETL solutions

• Best Practices for Enterprise ETL Pipelines

  • Designing cost-efficient ETL architectures
  • Ensuring scalability and high availability
  • Avoiding common ETL pitfalls

• Final Project: End-to-End ETL Implementation

  • Design and implement a real-world ETL workflow
  • Apply best practices in data integration, security, and performance

• Course Wrap-Up & Certification

  • Review of key concepts
  • Q&A and discussions on real-world challenges
  • Certification of completion