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.

Introductie tot ETL- en ELT-pijplijnen

Een ETL-pijplijn uitvoeren

ELT in de praktijk

ETL- en ELT-pipelines

ETL- en ELT-pijplijnen bouwen

Een ETL-pijplijn bouwen

De "T" in ELT

Gegevens met studentenscores extraheren, transformeren en laden

Introduction to Data 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

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

Building ETL Pipelines

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

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

Advanced ETL Techniques

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

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!

Deploying and Maintaining a Data Pipeline

scores.csv

schools_modified.csv

amazon_sales_cleaned_sql.csv

tax_rate_cleaned.csv

Course Glossary

Datapijplijnen vormen de basis van elk sterk dataplatform. Het bouwen van deze pijplijnen is een essentiële vaardigheid voor data-engineers, die enorme waarde leveren aan een bedrijf dat klaar is voor een datagedreven toekomst. Deze introductiecursus helpt je de vaardigheden aan te scherpen om effectieve, performante en betrouwbare datapijplijnen te bouwen.

De video's bevatten live-transcripts die je kunt weergeven door linksonder in de video's op "Show transcript" te klikken.
De begrippenlijst van de cursus vind je rechts in de sectie Bronnen.
Om CPE-credits te behalen moet je de cursus afronden en minimaal 70% scoren op de gekwalificeerde toets. Je kunt naar de toets navigeren door aan de rechterkant op de CPE-credits-callout te klikken.

Data Warehousing Concepts

Streamlined Data Ingestion with pandas

Leer effectieve, snelle en betrouwbare datapijplijnen bouwen met Extract, Transform en Load principes.

ETL en ELT in Python

Data-engineer in Python

Machine Learning Engineer

ETL- en ELT-pipelines

ETL en ELT in Python

Praktische interactieve oefening