Training Course on ETL/ELT Pipelines with Modern Data Stacks

Course Title: Training Course on ETL/ELT Pipelines with Modern Data Stacks

Executive Summary

This two-week intensive course equips data professionals with the knowledge and skills to design, build, and manage robust ETL/ELT pipelines using modern data stack technologies. Participants will gain hands-on experience with cloud-based data warehouses, data integration tools, and orchestration platforms. The course covers data ingestion, transformation, loading, and monitoring techniques, emphasizing scalability, performance, and reliability. Real-world case studies and practical exercises will enable participants to apply learned concepts to solve common data engineering challenges. By the end of the course, participants will be able to build efficient data pipelines that support data-driven decision-making in their organizations, leveraging the power of modern cloud-based data platforms.

Introduction

In today’s data-driven world, organizations rely heavily on efficient and reliable data pipelines to extract, transform, and load (ETL) data from various sources into data warehouses for analysis and reporting. Modern data stacks, leveraging cloud computing and advanced data integration tools, have revolutionized the way data pipelines are built and managed. This course provides a comprehensive overview of ETL/ELT concepts and techniques, focusing on modern cloud-based data stacks. Participants will learn how to design and implement data pipelines using tools like Apache Airflow, DBT, and cloud data warehouses such as Snowflake, BigQuery, and Redshift. The course emphasizes hands-on experience, enabling participants to build and deploy data pipelines that are scalable, performant, and reliable. By the end of the course, participants will be proficient in building and managing ETL/ELT pipelines using modern data stack technologies, empowering them to drive data-driven insights within their organizations.

Course Outcomes

• Understand the principles of ETL/ELT and its role in data warehousing.
• Design and build scalable and reliable data pipelines using modern data stack technologies.
• Utilize cloud-based data warehouses like Snowflake, BigQuery, and Redshift.
• Implement data integration and transformation techniques using tools like DBT.
• Orchestrate and monitor data pipelines using Apache Airflow.
• Apply data quality and testing best practices to ensure data accuracy.
• Optimize data pipeline performance for large datasets.

Training Methodologies

• Interactive lectures and presentations
• Hands-on labs and coding exercises
• Real-world case studies and group discussions
• Demonstrations of modern data stack technologies
• Individual and group projects
• Peer review and feedback sessions
• Q&A sessions with industry experts

Benefits to Participants

• Gain in-depth knowledge of ETL/ELT principles and modern data stack technologies.
• Develop hands-on skills in building and managing data pipelines.
• Enhance career prospects in data engineering and data warehousing.
• Learn best practices for data quality, testing, and performance optimization.
• Network with industry experts and peers.
• Earn a certificate of completion demonstrating proficiency in ETL/ELT pipelines.
• Become proficient in using Apache Airflow, DBT, and cloud data warehouses.

Benefits to Sending Organization

• Improved data quality and reliability for better decision-making.
• Increased efficiency and automation of data integration processes.
• Reduced data warehousing costs through cloud-based solutions.
• Enhanced ability to handle large datasets and complex data transformations.
• Improved data governance and compliance.
• Empowered data engineering teams with the latest technologies and best practices.
• Accelerated time-to-insight through faster and more reliable data pipelines.

Target Participants

• Data Engineers
• Data Architects
• Data Warehouse Developers
• ETL Developers
• Business Intelligence Developers
• Data Scientists
• Database Administrators

WEEK 1: Foundations of ETL/ELT and Cloud Data Warehouses

Module 1: Introduction to ETL/ELT Concepts

1. Overview of data warehousing and business intelligence.
2. Introduction to ETL and ELT processes.
3. Data integration challenges and solutions.
4. The role of data pipelines in modern data architectures.
5. Batch vs. stream processing.
6. Understanding data quality and data governance.
7. Introduction to modern data stack technologies.

Module 2: Cloud Data Warehouse Fundamentals

1. Introduction to cloud computing and its benefits for data warehousing.
2. Overview of popular cloud data warehouses: Snowflake, BigQuery, Redshift.
3. Choosing the right cloud data warehouse for your needs.
4. Data modeling techniques for cloud data warehouses.
5. Schema design and optimization.
6. Security and access control in cloud data warehouses.
7. Cost management and performance considerations.

Module 3: Data Ingestion and Extraction

1. Techniques for extracting data from various sources: databases, APIs, files.
2. Using connectors and data integration tools for data ingestion.
3. Handling different data formats: JSON, CSV, XML.
4. Implementing change data capture (CDC) for real-time data ingestion.
5. Best practices for data extraction and loading.
6. Data validation and error handling.
7. Hands-on lab: Extracting data from a relational database.

Module 4: Data Transformation and Cleansing

1. Data transformation techniques: cleansing, standardization, deduplication.
2. Using SQL and Python for data transformation.
3. Introduction to DBT (Data Build Tool) for data transformation.
4. Implementing data quality checks and validation rules.
5. Handling missing and inconsistent data.
6. Data masking and anonymization for data privacy.
7. Hands-on lab: Transforming data using DBT.

Module 5: Loading Data into Cloud Data Warehouses

1. Techniques for loading data into cloud data warehouses: bulk loading, streaming ingestion.
2. Optimizing data loading performance.
3. Handling data errors and exceptions.
4. Data partitioning and clustering for performance optimization.
5. Implementing data versioning and audit trails.
6. Best practices for data loading and validation.
7. Hands-on lab: Loading data into Snowflake.

WEEK 2: Data Pipeline Orchestration, Monitoring, and Optimization

Module 6: Introduction to Data Pipeline Orchestration

1. Overview of data pipeline orchestration tools: Apache Airflow, Prefect.
2. Understanding the concepts of DAGs, tasks, and operators.
3. Scheduling and triggering data pipelines.
4. Managing dependencies between tasks.
5. Error handling and retries.
6. Monitoring and alerting.
7. Choosing the right orchestration tool for your needs.

Module 7: Building Data Pipelines with Apache Airflow

1. Setting up Apache Airflow.
2. Creating and managing DAGs.
3. Using different operators for data integration, transformation, and loading.
4. Implementing branching and conditional logic.
5. Using variables and connections.
6. Monitoring and troubleshooting data pipelines.
7. Hands-on lab: Building a data pipeline with Apache Airflow.

Module 8: Data Pipeline Monitoring and Alerting

1. Implementing data pipeline monitoring using Airflow’s UI and logs.
2. Setting up alerts for data pipeline failures and performance issues.
3. Using external monitoring tools: Prometheus, Grafana.
4. Analyzing data pipeline performance metrics.
5. Identifying and resolving bottlenecks.
6. Best practices for data pipeline monitoring and alerting.
7. Hands-on lab: Setting up monitoring and alerting for a data pipeline.

Module 9: Data Pipeline Optimization and Performance Tuning

1. Techniques for optimizing data pipeline performance.
2. SQL optimization for cloud data warehouses.
3. Data partitioning and clustering.
4. Using indexes and materialized views.
5. Caching and data compression.
6. Performance testing and benchmarking.
7. Best practices for data pipeline optimization.

Module 10: Data Quality and Testing

1. Implementing data quality checks and validation rules.
2. Using DBT for data testing.
3. Writing unit tests and integration tests for data pipelines.
4. Implementing data lineage and data governance.
5. Data profiling and data discovery.
6. Best practices for data quality and testing.
7. Hands-on lab: Implementing data quality checks and testing in a data pipeline.

Action Plan for Implementation

1. Identify a specific data integration challenge within your organization.
2. Design an ETL/ELT pipeline solution using modern data stack technologies.
3. Develop a proof-of-concept data pipeline using the tools and techniques learned in the course.
4. Present the solution to stakeholders and gather feedback.
5. Implement the data pipeline in a production environment.
6. Monitor and optimize the data pipeline for performance and reliability.
7. Share your learnings and best practices with your team.