Video demonstration: Building a Simple Data Lake on AWS. Build a simple data lake on AWS using a combination of services, including Amazon MWAA, AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, and Amazon S3.
Video demonstration: Building a Data Lake with Apache Airflow. Programmatically build a simple Data Lake on AWS using Amazon Managed Workflows for Apache Airflow, AWS Glue, and Amazon Athena.
Video demonstration: Lakehouse Automation on AWS with Apache Airflow. Programmatically load data into and upload data from Amazon Redshift using Apache Airflow.
Blog post: DevOps for DataOps: Building a CI/CD Pipeline for Apache Airflow DAGs. Build an effective CI/CD pipeline to test and deploy your Apache Airflow DAGs to Amazon MWAA using GitHub Actions.
DevOps for DataOps
Building a Data Lake with Apache Airflow
Lakehouse Automation on AWS with Apache Airflow
Amazon Redshift TICKIT Sample Database
tickit.saas.categorytickit.saas.eventtickit.saas.venuetickit.crm.userstickit.datetickit.listingtickit.sales
+-------------+--------------------------------------------------------------------+
| Prefix | Description |
+-------------+--------------------------------------------------------------------+
| _source | Data Source metadata only (org. call _raw in video) |
| _raw | Raw/Bronze data from data sources (org. call _converted in video) |
| _refined | Refined/Silver data - raw data with initial ELT/cleansing applied |
| _aggregated | Gold/Aggregated data - aggregated/joined refined data |
+-------------+--------------------------------------------------------------------+
There were two small changes made to the source code, as compared to the video demonstration, to help clarify the flow
of data in the demonstration. The prefix for the (7) data source AWS Glue Data Catalog table’s prefix was switched
from raw_ from source_. Also, the (7) Raw/Bronze AWS Glue Data Catalog table’s prefix was switched from converted_
to raw_. The final data flow is 1) source_, 2) raw_, 3) refined_, and 4) agg_ (aggregated).
DATA_LAKE_BUCKET="your-data-lake-bucket"
aws s3 rm "s3://${DATA_LAKE_BUCKET}/tickit/" --recursive
aws glue delete-database --name tickit_demo
aws glue create-database \
--database-input '{"Name": "tickit_demo", "Description": "Track sales activity for the fictional TICKIT web site"}'
aws glue get-tables \
--database-name tickit_demo \
--query "TableList[].Name" \
--output table
aws glue start-crawler --name tickit_postgresql
aws glue start-crawler --name tickit_mysql
aws glue start-crawler --name tickit_mssql
aws glue get-tables \
--database-name tickit_demo \
--query "TableList[].Name" \
--expression "source_*" \
--output table
aws glue start-job-run --job-name tickit_public_category_raw
aws glue start-job-run --job-name tickit_public_date_raw
aws glue start-job-run --job-name tickit_public_event_raw
aws glue start-job-run --job-name tickit_public_listing_raw
aws glue start-job-run --job-name tickit_public_sales_raw
aws glue start-job-run --job-name tickit_public_users_raw
aws glue start-job-run --job-name tickit_public_venue_raw
aws glue start-job-run --job-name tickit_public_category_refine
aws glue start-job-run --job-name tickit_public_date_refine
aws glue start-job-run --job-name tickit_public_event_refine
aws glue start-job-run --job-name tickit_public_listing_refine
aws glue start-job-run --job-name tickit_public_sales_refine
aws glue start-job-run --job-name tickit_public_users_refine
aws glue start-job-run --job-name tickit_public_venue_refine
aws glue get-tables \
--database-name tickit_demo \
--query "TableList[].Name" \
--output table
aws s3api list-objects-v2 \
--bucket ${DATA_LAKE_BUCKET} \
--prefix "tickit/" \
--query "Contents[].Key" \
--output table
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