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.

We developed batch and streaming pipelines in Part 2 and Part 4. Often it is faster and simpler to identify and fix bugs on the pipeline code by performing local unit testing. Moreover, especially when it comes to creating a streaming pipeline, unit testing cases can facilitate development further by using TestStream as it allows us to advance watermarks or processing time according to different scenarios. In this post, we discuss how to perform unit testing of the batch and streaming pipelines that we developed earlier.

In Part 3, we discussed the portability layer of Apache Beam as it helps understand (1) how Python pipelines run on the Flink Runner and (2) how multiple SDKs can be used in a single pipeline, followed by demonstrating local Flink and Kafka cluster creation for developing streaming pipelines. In this post, we develop a streaming pipeline that aggregates page visits by user in a fixed time window of 20 seconds. Two versions of the pipeline are created with/without relying on Beam SQL.

Beam pipelines are portable between batch and streaming semantics but not every Runner is equally capable. The Apache Flink Runner supports Python, and it has good features that allow us to develop streaming pipelines effectively. We first discuss the portability layer of Apache Beam as it helps understand (1) how a pipeline developed by the Python SDK can be executed in the Flink Runner that only understands Java JAR and (2) how multiple SDKs can be used in a single pipeline. Then we move on to how to manage local Flink and Kafka clusters using bash scripts. Finally, we end up illustrating a simple streaming pipeline, which reads and writes website visit logs from and to Kafka topics.

In this series, we discuss local development of Apache Beam pipelines using Python. A basic Beam pipeline was introduced in Part 1, followed by demonstrating how to utilise Jupyter notebooks, Beam SQL and Beam DataFrames. In this post, we discuss Batch pipelines that aggregate website visit log by user and time. The pipelines are developed with and without Beam SQL. Additionally, each pipeline is implemented on a Jupyter notebook for demonstration.

Apache Beam and Apache Flink are open-source frameworks for parallel, distributed data processing at scale. Flink has DataStream and Table/SQL APIs and the former has more capacity to develop sophisticated data streaming applications. The DataStream API of PyFlink, Flink’s Python API, however, is not as complete as its Java counterpart, and it doesn’t provide enough capability to extend when there are missing features in Python. On the other hand, Apache Beam supports more possibility to extend and/or customise its features. In this series of posts, we discuss local development of Apache Beam pipelines using Python. In Part 1, a basic Beam pipeline is introduced, followed by demonstrating how to utilise Jupyter notebooks for interactive development. It also covers Beam SQL and Beam DataFrames examples on notebooks. In subsequent posts, we will discuss batch and streaming pipeline development and concludes with illustrating unit testing of existing pipelines.

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 1 and Part 3, we developed data build tool (dbt) projects that target PostgreSQL and BigQuery using fictional pizza shop data. The data is modelled by SCD type 2 dimension tables and one transactional fact table. While the order records should be joined with dimension tables to get complete details for PostgreSQL, the fact table is denormalized using nested and repeated fields to improve query performance for BigQuery. Open Table Formats such as Apache Iceberg bring a new opportunity that implements data warehousing features in a data lake (i.e. data lakehouse) and Amazon Athena is probably the easiest way to perform such tasks on AWS. In this post, we create a new dbt project that targets Apache Iceberg where transformations are performed on Amazon Athena. Data modelling is similar to the BigQuery project where the dimension tables are modelled by the SCD type 2 approach and the fact table is denormalized using the array and struct data types.

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, we discuss practical examples of data warehouse and lakehouse development where data transformation is performed by the data build tool (dbt) and ETL is managed by Apache Airflow. In Part 1, we developed a dbt project on PostgreSQL using fictional pizza shop data. At the end, the data sets are modelled by two SCD type 2 dimension tables and one transactional fact table. In this post, we create a new dbt project that targets Google BigQuery. While the dimension tables are kept by the same SCD type 2 approach, the fact table is denormalized using nested and repeated fields, which potentially can improve query performance by pre-joining corresponding dimension records.