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.
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.
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.
In this series of posts, we discuss a Flink (Pyflink) application that reads/writes from/to Kafka topics. In the previous posts, I demonstrated a Pyflink app that targets a local Kafka cluster as well as a Kafka cluster on Amazon MSK. The app was executed in a virtual environment as well as in a local Flink cluster for improved monitoring. In this post, the app will be deployed via Amazon Managed Service for Apache Flink.
In this series of posts, we discuss a Flink (Pyflink) application that reads/writes from/to Kafka topics. In part 1, an app that targets a local Kafka cluster was created. In this post, we will update the app by connecting a Kafka cluster on Amazon MSK. The Kafka cluster is authenticated by IAM and the app has additional jar dependency. As Amazon Managed Service for Apache Flink does not allow you to specify multiple pipeline jar files, we have to build a custom Uber Jar that combines multiple jar files. Same as part 1, the app will be executed in a virtual environment as well as in a local Flink cluster for improved monitoring with the updated pipeline jar file.
As part of investigating how to utilize Kafka Connect effectively for AWS services integration, I demonstrated how to develop the Camel DynamoDB sink connector using Docker in Part 2. Fake order data was generated using the MSK Data Generator source connector, and the sink connector was configured to consume the topic messages to ingest them into a DynamoDB table. In this post, I will illustrate how to deploy the data ingestion applications using Amazon MSK and MSK Connect.
Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. It can be used to build real-time data pipeline on AWS effectively. In this post, I will introduce available Kafka connectors mainly for AWS services integration. Also, developing and deploying some of them will be covered in later posts.
Streaming ingestion from Kafka (MSK) into Redshift and Athena can be much simpler as they now support direct integration. In part 2, we discuss an end-to-end streaming ingestion solution using EventBridge, Lambda, MSK and Athena. We also use AWS SAM integrated with Terraform for developing the producer Lambda function locally.
Streaming ingestion from Kafka (MSK) into Redshift and Athena can be much simpler as they now support direct integration. In part 1, we discuss an end-to-end streaming ingestion solution using EventBridge, Lambda, MSK and Redshift. We also use AWS SAM integrated with Terraform for developing the producer Lambda function locally.