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.

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.

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.

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.

In this lab, we will create a Kafka producer application using AWS Lambda, which sends fake taxi ride data into a Kafka topic on Amazon MSK. A configurable number of the producer Lambda function will be invoked by an Amazon EventBridge schedule rule. In this way we are able to generate test data concurrently based on the desired volume of messages.

Building Apache Flink Applications in Java by Confluent is a course to introduce Apache Flink through a series of hands-on exercises. Utilising the Flink DataStream API, the course develops three Flink applications from ingesting source data into calculating usage statistics. As part of learning the Flink DataStream API in Pyflink, I converted the Java apps into Python equivalent while performing the course exercises in Pyflink. This post summarises the progress of the conversion and shows the final output.

This series updates a real time analytics app based on Amazon Kinesis from an AWS workshop. Data is ingested from multiple sources into a Kafka cluster instead and Flink (Pyflink) apps are used extensively for data ingesting and processing. As an introduction, this post compares the original architecture with the new architecture, and the app will be implemented in subsequent posts.