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Course

ETL and ELT in Python

Intermediate

4.8+

Updated 05/2025

Learn to build effective, performant, and reliable data pipelines using Extract, Transform, and Load principles.

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PythonData Engineering4 hours14 videos53 Exercises4,450 XP20,568Statement of Accomplishment

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Course Description

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 as pandas 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.

Prerequisites

Data Warehousing Concepts Streamlined Data Ingestion with pandas

1

Introduction to Data Pipelines

Introduction to ETL and ELT Pipelines

Running an ETL Pipeline

ELT in Action

ETL and ELT Pipelines

Building ETL and ELT Pipelines

Building an ETL Pipeline

The "T" in ELT

Extracting, Transforming, and Loading Student Scores Data

2

Building ETL Pipelines

Extracting data from structured sources

Extracting data from parquet files

Pulling data from SQL databases

Building functions to extract data

Transforming data with pandas

Filtering pandas DataFrames

Transforming sales data with pandas

Validating data transformations

Persisting data with pandas

Loading sales data to a CSV file

Customizing a CSV file

Persisting data to files

Monitoring a data pipeline

Logging within a data pipeline

Handling exceptions when loading data

Monitoring and alerting within a data pipeline

3

Advanced ETL Techniques

Extracting non-tabular data

Ingesting JSON data with pandas

Reading JSON data into memory

Transforming non-tabular data

Iterating over dictionaries

Parsing data from dictionaries

Transforming JSON data

Transforming and cleaning DataFrames

Advanced data transformation with pandas

Filling missing values with pandas

Grouping data with pandas

Applying advanced transformations to DataFrames

Loading data to a SQL database with pandas

Loading data to a Postgres database

Validating data loaded to a Postgres Database

4

Deploying and Maintaining a Data Pipeline

Manually testing a data pipeline

Testing data pipelines

Validating a data pipeline at "checkpoints"

Testing a data pipeline end-to-end

Unit-testing a data pipeline

Validating a data pipeline with assert

Writing unit tests with pytest

Creating fixtures with pytest

Unit testing a data pipeline with fixtures

Running a data pipeline in production

Orchestration and ETL tools

Data pipeline architecture patterns

Running a data pipeline end-to-end

Congratulations!

ETL and ELT in Python

Course
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*4.8

from 436 reviews

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Most recent
Top reviews

CICI

about 16 hours

Mykola

about 16 hours

Great course

Becky

about 17 hours

Valentijn

about 23 hours

Thanks! After months of python and sql basics, it's exciting to see it all come together in courses like these

Tony

2 days

Good course

Viesturs

2 days

CICI

"Great course"

Mykola

Becky

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