In this series, we develop Apache Beam Python pipelines. The majority of them are from Building Big Data Pipelines with Apache Beam by Jan Lukavský. Mainly relying on the Java SDK, the book teaches fundamentals of Apache Beam using hands-on tasks, and we convert those tasks using the Python SDK. We focus on streaming pipelines, and they are deployed on a local (or embedded) Apache Flink cluster using the Apache Flink Runner. Beginning with setting up the development environment, we build two pipelines that obtain top K most frequent words and the word that has the longest word length in this post.

In this post, we develop an Apache Beam pipeline using the Python SDK and deploy it on an Apache Flink cluster via the Apache Flink Runner. Same as Part I, we deploy a Kafka cluster using the Strimzi Operator on a minikube cluster as the pipeline uses Apache Kafka topics for its data source and sink. Then, we develop the pipeline as a Python package and add the package to a custom Docker image so that Python user code can be executed externally. For deployment, we create a Flink session cluster via the Flink Kubernetes Operator, and deploy the pipeline using a Kubernetes job. Finally, we check the output of the application by sending messages to the input Kafka topic using a Python producer application.

Flink Kubernetes Operator acts as a control plane to manage the complete deployment lifecycle of Apache Flink applications. With the operator, we can simplify deployment and management of Python stream processing applications. In this series, we discuss how to deploy a PyFlink application and Python Apache Beam pipeline on the Flink Runner on Kubernetes. In Part 1, we first deploy a Kafka cluster on a minikube cluster as the source and sink of the PyFlink application are Kafka topics. Then, the application source is packaged in a custom Docker image and deployed on the minikube cluster using the Flink Kubernetes Operator. Finally, the output of the application is checked by sending messages to the input Kafka topic using a Python producer application.

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.

Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. In this post, we discuss how to set up a data ingestion pipeline using Kafka connectors. Fake customer and order data is ingested into Kafka topics using the MSK Data Generator. Also, we use the Confluent S3 sink connector to save the messages of the topics into a S3 bucket. The Kafka Connect servers and individual connectors are deployed using the custom resources of Strimzi on Kubernetes.

Apache Kafka has five core APIs, and we can develop applications to send/read streams of data to/from topics in a Kafka cluster using the producer and consumer APIs. While the main Kafka project maintains only the Java APIs, there are several open source projects that provide the Kafka client APIs in Python. In this post, we discuss how to develop Kafka client applications using the kafka-python package on Kubernetes.

Apache Kafka is one of the key technologies for implementing data streaming architectures. Strimzi provides a way to run an Apache Kafka cluster and related resources on Kubernetes in various deployment configurations. In this series of posts, we will discuss how to create a Kafka cluster, to develop Kafka client applications in Python and to build a data pipeline using Kafka connectors on Kubernetes.