In Part 5, we developed a dbt project that that targets Apache Iceberg where transformations are performed on Amazon Athena. Two dimension tables that keep product and user records are created as Type 2 slowly changing dimension (SCD Type 2) tables, and one transactional fact table is built to keep pizza orders. To improve query performance, the fact table is denormalized to pre-join records from the dimension tables using the array and struct data types. In this post, we discuss how to set up an ETL process on the project using Apache Airflow.

In Part 3, we developed a dbt project that targets Google BigQuery with fictional pizza shop data. Two dimension tables that keep product and user records are created as Type 2 slowly changing dimension (SCD Type 2) tables, and one transactional fact table is built to keep pizza orders. The fact table is denormalized using nested and repeated fields for improving query performance. In this post, we discuss how to set up an ETL process on the project using Apache Airflow.

In this series of posts, we discuss data warehouse/lakehouse examples using data build tool (dbt) including ETL orchestration with Apache Airflow. In Part 1, we developed a dbt project on PostgreSQL with fictional pizza shop data. Two dimension tables that keep product and user records are created as Type 2 slowly changing dimension (SCD Type 2) tables, and one transactional fact table is built to keep pizza orders. In this post, we discuss how to set up an ETL process on the project using Apache Airflow.

The data build tool (dbt) is a popular data transformation tool for data warehouse development. Moreover, it can be used for data lakehouse development thanks to open table formats such as Apache Iceberg, Apache Hudi and Delta Lake. dbt supports key AWS analytics services and I wrote a series of posts that discuss how to utilise dbt with Redshift, Glue, EMR on EC2, EMR on EKS, and Athena. Those posts focus on platform integration, however, they do not show realistic ETL scenarios. In this series of posts, we discuss practical data warehouse/lakehouse examples including ETL orchestration with Apache Airflow. As a starting point, we develop a dbt project on PostgreSQL using fictional pizza shop data in this post.

Amazon MSK can be configured as an event source of a Lambda function. Lambda internally polls for new messages from the event source and then synchronously invokes the target Lambda function. With this feature, we can develop a Kafka consumer application in serverless environment where developers can focus on application logic. In this lab, we will discuss how to create a Kafka consumer using a Lambda function.

Apache Flink became generally available for Amazon EMR on EKS from the EMR 6.15.0 releases. As it is integrated with the Glue Data Catalog, it can be particularly useful if we develop real time data ingestion/processing via Flink and build analytical queries using Spark (or any other tools or services that can access to the Glue Data Catalog). In this post, we will discuss how to set up a local development environment for Apache Flink and Spark using the EMR container images. After illustrating the environment setup, we will discuss a solution where data ingestion/processing is performed in real time using Apache Flink and the processed data is consumed by Apache Spark for analysis.

Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. It makes it simple to quickly define connectors that move large collections of data into and out of Kafka. In this lab, we will discuss how to create a data pipeline that ingests data from a Kafka topic into a DynamoDB table using the Camel DynamoDB sink connector.

The value of data can be maximised when it is used without delay. With Apache Flink, we can build streaming analytics applications that incorporate the latest events with low latency. In this lab, we will create a Pyflink application that writes accumulated taxi rides data into an OpenSearch cluster. It aggregates the number of trips/passengers and trip durations by vendor ID for a window of 5 seconds. The data is then used to create a chart that monitors the status of taxi rides in the OpenSearch Dashboard.

In this lab, we will create a Pyflink application that exports Kafka topic messages into a S3 bucket. The app enriches the records by adding a new column using a user defined function and writes them via the FileSystem SQL connector. This allows us to achieve a simpler architecture compared to the original lab where the records are sent into Amazon Kinesis Data Firehose, enriched by a separate Lambda function and written to a S3 bucket afterwards. While the records are being written to the S3 bucket, a Glue table will be created to query them on Amazon Athena.

In this lab, we will create a Pyflink application that reads records from S3 and sends them into a Kafka topic. A custom pipeline Jar file will be created as the Kafka cluster is authenticated by IAM, and it will be demonstrated how to execute the app in a Flink cluster deployed on Docker as well as locally as a typical Python app. We can assume the S3 data is static metadata that needs to be joined into another stream, and this exercise can be useful for data enrichment.