Course
ETL and ELT in Python
IntermediateSkill Level
Updated 01/2026PythonData Engineering4 hr14 videos53 Exercises4,450 XP35,851Statement of Accomplishment
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Empowering Analytics with Data Pipelines
Data pipelines are at the foundation of every strong data platform. Building these pipelines is an essential skill for data engineers, who provide incredible value to a business ready to step into a data-driven future. This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.Building and Maintaining ETL Solutions
Throughout this course, you’ll dive into the complete process of building a data pipeline. You’ll grow skills leveraging Python libraries such aspandas and json to extract data from structured and unstructured sources before it’s transformed and persisted for downstream use. Along the way, you’ll develop confidence tools and techniques such as architecture diagrams, unit-tests, and monitoring that will help to set your data pipelines out from the rest. As you progress, you’ll put your new-found skills to the test with hands-on exercises.
Supercharge Data Workflows
After completing this course, you’ll be ready to design, develop and use data pipelines to supercharge your data workflow in your job, new career, or personal project.Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Assess data integrity and pipeline performance using logging, validation checkpoints, and automated unit or end-to-end tests
- Differentiate ETL and ELT architectures in terms of process sequence, tooling, and appropriate storage targets
- Evaluate deployment and orchestration options that schedule, monitor, and retry pipelines in production environments
- Identify the essential stages and components of Python-based data pipelines, including data sources, transformations, and destinations
- Recognize pandas and SQL techniques for extracting, transforming, and loading both tabular and non-tabular datasets
Prerequisites
Data Warehousing ConceptsStreamlined Data Ingestion with pandas1
Introduction to Data Pipelines
Get ready to discover how data is collected, processed, and moved using data pipelines. You will explore the qualities of the best data pipelines, and prepare to design and build your own.
2
Building ETL Pipelines
Dive into leveraging pandas to extract, transform, and load data as you build your first data pipelines. Learn how to make your ETL logic reusable, and apply logging and exception handling to your pipelines.
3
Advanced ETL Techniques
Supercharge your workflow with advanced data pipelining techniques, such as working with non-tabular data and persisting DataFrames to SQL databases. Discover tooling to tackle advanced transformations with pandas, and uncover best-practices for working with complex data.
4
Deploying and Maintaining a Data Pipeline
In this final chapter, you’ll create frameworks to validate and test data pipelines before shipping them into production. After you’ve tested your pipeline, you’ll explore techniques to run your data pipeline end-to-end, all while allowing for visibility into pipeline performance.
ETL and ELT in Python
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FAQs
What is the difference between ETL and ELT?
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both data integration methods that move data from one place to another. The main difference between the two is when the data is transformed. ETL transforms data on a separate processing server before loading it into the data warehouse while ELT transforms the data directly within the data warehouse itself.
What tools and programming languages are used in this course?
This course uses pandas, SQL, Parquet files, and data pipelines.
Who should take this course?
This course is ideal for aspiring data engineers and people who want to learn about data pipelines.
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