Run a basic Apache Beam pipeline in Python, then develop interactively in Jupyter notebooks with Beam SQL and Beam DataFrames examples.

Orchestrate the Amazon Athena and Iceberg pizza shop dbt project with Apache Airflow, running the dimension and denormalised fact table builds.

Model pizza shop data as Apache Iceberg tables transformed on Amazon Athena with dbt, using array and struct types to denormalise the fact table.

Orchestrate the BigQuery pizza shop dbt project with Apache Airflow, running the SCD type 2 dimension tables and the denormalised fact table.

Model pizza shop data on Google BigQuery with dbt, keeping SCD type 2 dimensions and denormalising the fact table with nested and repeated fields.

Orchestrate the PostgreSQL pizza shop dbt project with Apache Airflow, scheduling the SCD type 2 dimension and fact table builds as an ETL process.

Model fictional pizza shop data on PostgreSQL with dbt, building two SCD type 2 dimension tables and one transactional fact table.

Ingest fake customer and order data into Kafka with the MSK Data Generator and save it to S3, with connectors deployed as Strimzi custom resources.

Send and read Kafka topic records from Python client applications built with the kafka-python package and deployed on Kubernetes.

Create a Kafka cluster on Kubernetes with Strimzi, the first step in a series on Python clients and Kafka Connect pipelines on Kubernetes.