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.

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.

Stream processing technology is becoming more and more popular with companies big and small because it provides superior solutions for many established use cases such as data analytics, ETL, and transactional applications, but also facilitates novel applications, software architectures, and business opportunities. Beginning with traditional data infrastructures and application/data development patterns, this post introduces stateful stream processing and demonstrates to what extent it can improve the traditional development patterns. A consulting company can partner with her clients on their journeys of adopting stateful stream processing, and it can bring huge opportunities. Those opportunities are summarised at the end.

In the previous post, we discussed how to develop a data pipeline from Apache Kafka into OpenSearch locally using Docker. The pipeline will be deployed on AWS using Amazon MSK, Amazon MSK Connect and Amazon OpenSearch Service using Terraform in this post. First the infrastructure will be deployed that covers a VPC, VPN server, MSK Cluster and OpenSearch domain. Then Kafka source and sink connectors will be deployed on MSK Connect, followed by performing quick data analysis.

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.