ETL dan ELT di Python

MenengahTingkat Keterampilan

Diperbarui 01/2026

Pelajari cara membangun data pipeline yang efektif, memiliki performa tinggi, dan andal menggunakan prinsip Extract, Transform, dan Load.

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Deskripsi Kursus

Persyaratan

Data Warehousing Concepts Streamlined Data Ingestion with pandas

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.

Introduction to ETL and ELT Pipelines

50 XP

Running an ETL Pipeline

100 XP

ELT in Action

100 XP

ETL and ELT Pipelines

50 XP

Building ETL and ELT Pipelines

50 XP

Building an ETL Pipeline

100 XP

The "T" in ELT

100 XP

Extracting, Transforming, and Loading Student Scores Data

100 XP

Mulai Bab

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.

Extracting data from structured sources

50 XP

Extracting data from parquet files

100 XP

Pulling data from SQL databases

100 XP

Building functions to extract data

100 XP

Transforming data with pandas

50 XP

Filtering pandas DataFrames

100 XP

Transforming sales data with pandas

100 XP

Validating data transformations

100 XP

Persisting data with pandas

50 XP

Loading sales data to a CSV file

100 XP

Customizing a CSV file

100 XP

Persisting data to files

100 XP

Monitoring a data pipeline

50 XP

Logging within a data pipeline

100 XP

Handling exceptions when loading data

100 XP

Monitoring and alerting within a data pipeline

100 XP

Mulai Bab

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.

Extracting non-tabular data

50 XP

Ingesting JSON data with pandas

100 XP

Reading JSON data into memory

100 XP

Transforming non-tabular data

50 XP

Iterating over dictionaries

100 XP

Parsing data from dictionaries

100 XP

Transforming JSON data

100 XP

Transforming and cleaning DataFrames

100 XP

Advanced data transformation with pandas

50 XP

Filling missing values with pandas

100 XP

Grouping data with pandas

100 XP

Applying advanced transformations to DataFrames

100 XP

Loading data to a SQL database with pandas

50 XP

Loading data to a Postgres database

100 XP

Validating data loaded to a Postgres Database

100 XP

Mulai Bab

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.

Manually testing a data pipeline

50 XP

Testing data pipelines

50 XP

Validating a data pipeline at "checkpoints"

100 XP

Testing a data pipeline end-to-end

100 XP

Unit-testing a data pipeline

50 XP

Validating a data pipeline with assert

100 XP