Introduction

The Extract, Transform, Load (ETL) process is fundamental in data engineering, ensuring that data is collected from various sources, transformed into a suitable format for analysis, and loaded into data warehouses or other storage systems. This course covers the ETL process in depth, focusing on how to use modern ETL tools and techniques to automate and optimize data workflows. Participants will gain hands-on experience with popular ETL tools like Apache NiFi, Talend, and SQL Server Integration Services (SSIS), and learn how to implement data pipelines that ensure data consistency, quality, and scalability.

By the end of this course, participants will be equipped with the skills to design, implement, and optimize ETL workflows, using both on-premise and cloud-based solutions to handle large datasets efficiently.

Objectives

By the end of this course, participants will:

• Understand the core components and steps of the ETL process.
• Learn how to choose the right ETL tool based on business and technical requirements.
• Gain hands-on experience in using ETL tools like Apache NiFi, Talend, and SSIS.
• Learn how to automate and schedule ETL workflows for real-time and batch processing.
• Understand how to manage data quality, transformation, and data mapping.
• Gain skills in optimizing ETL processes for performance and scalability.
• Learn how to handle error handling and logging in ETL pipelines.
• Understand the best practices for data governance and security in ETL workflows.

Who Should Attend?

This course is ideal for:

• Data engineers and ETL developers looking to deepen their skills in ETL process design and implementation.
• IT professionals who need to build or optimize data pipelines.
• Data analysts and business intelligence professionals who want to understand how ETL feeds their data for reporting and analysis.
• Anyone looking to learn how to use popular ETL tools like Talend, Apache NiFi, and SSIS.

Day 1: Introduction to ETL Processes

Morning Session: Overview of ETL Concepts

• What is ETL? The role of ETL in modern data architectures.
• Core ETL components: Extract, Transform, and Load.
• Data sources and targets: Relational databases, NoSQL databases, flat files, cloud storage.
• Batch vs. real-time ETL processes.
• Hands-on: Explore sample data sources and define an ETL pipeline for a sample use case.

Afternoon Session: Data Extraction Techniques

• Extracting data from different sources: Databases (SQL, NoSQL), APIs, web scraping, and file formats (CSV, JSON, XML).
• Best practices for data extraction: Handling large datasets, incremental loading.
• Introduction to data connectors and integration with external systems.
• Hands-on: Extract data from a relational database and a REST API.

Day 2: Data Transformation in ETL

Morning Session: Data Transformation Fundamentals

• Data transformation overview: Data cleaning, aggregation, filtering, and mapping.
• Common transformations: Normalization, denormalization, type conversion, and data validation.
• Handling missing data: Imputation, default values, and dropping records.
• Using SQL for transformation: Writing SQL queries for data manipulation.
• Hands-on: Implement common data transformations using SQL or Python.

Afternoon Session: Advanced Data Transformation Techniques

• Data enrichment: Integrating external data sources into transformations.
• Working with different data formats: JSON, XML, Avro, and Parquet.
• Time-based transformations: Handling timestamps, date formats, and time zones.
• Building custom transformation functions.
• Hands-on: Use Python to implement custom transformations for complex datasets.

Day 3: ETL Tools and Platforms

Morning Session: Introduction to ETL Tools

• Overview of popular ETL tools: Talend, Apache NiFi, SQL Server Integration Services (SSIS), Apache Airflow, and Pentaho.
• Comparing ETL tools: On-premise vs. cloud-based solutions, scalability, and ease of use.
• Choosing the right ETL tool for different use cases: Simple vs. complex ETL workflows.
• Key features of Talend, Apache NiFi, and SSIS.
• Hands-on: Set up and explore the basic features of Talend or SSIS.

Afternoon Session: Using Apache NiFi and Talend for ETL

• Introduction to Apache NiFi: Drag-and-drop interface, real-time data flow management, and building data pipelines.
• Introduction to Talend: Visual design for data integration, transformation, and automation.
• Building ETL workflows in NiFi and Talend: Extract, transform, and load with simple tools.
• Hands-on: Build a simple ETL pipeline in Apache NiFi or Talend to automate a sample data extraction, transformation, and loading process.

Day 4: Data Loading, Performance Optimization, and Automation

Morning Session: Data Loading and Storage

• Different types of data loading: Full load, incremental load, and delta load.
• Loading data into data warehouses, databases, and cloud storage (e.g., Amazon S3, Google Cloud Storage).
• Data partitioning and indexing strategies for faster loads.
• Hands-on: Load transformed data into a data warehouse or cloud storage using Talend.

Afternoon Session: ETL Performance Optimization and Automation

• Performance tuning: Optimizing ETL workflows for large datasets.
• Parallel processing and distributed ETL processing.
• Automating ETL workflows: Scheduling and triggering ETL jobs (using Apache Airflow or Talend).
• Error handling and logging in ETL pipelines.
• Hands-on: Optimize an ETL workflow to improve processing speed and automate data extraction using Apache Airflow.

Day 5: Advanced ETL Topics and Data Governance

Morning Session: Advanced ETL and Real-Time Processing

• Introduction to real-time ETL: Stream processing and event-driven data pipelines.
• Using tools like Apache Kafka and Apache Spark for real-time ETL.
• Handling streaming data and batch data in hybrid ETL pipelines.
• Hands-on: Implement a simple real-time ETL pipeline using Apache Kafka or Apache Spark.

Afternoon Session: Data Governance, Security, and Best Practices

• Best practices for ensuring data quality and consistency in ETL processes.
• Data governance in ETL: Data lineage, auditing, and version control.
• Ensuring data privacy and security: Encryption, access control, and secure data transfers.
• Hands-on: Implement security measures in an ETL workflow and ensure data governance.

Materials and Tools:

• Required tools: Talend, Apache NiFi, SQL Server Integration Services (SSIS), Python, Apache Kafka, Apache Airflow
• Sample datasets: Customer data, sales data, log files, and API data.
• Recommended resources: Online guides and tutorials for Talend, Apache NiFi, and other ETL tools.