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.
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.
Kafka Connect can be an effective tool to ingest data from Apache Kafka into OpenSearch. In this post, we will discuss how to develop a data pipeline from Apache Kafka into OpenSearch locally using Docker while the pipeline will be deployed on AWS in the next post. Fake impressions and clicks data will be pushed into Kafka topics using a Kafka source connector and those records will be ingested into OpenSearch indexes using a sink connector for near-real time analytics.
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.
This series aims to help those who are new to Apache Flink and Amazon Managed Service for Apache Flink by re-implementing a simple fraud detection application that is discussed in an AWS workshop titled AWS Kafka and DynamoDB for real time fraud detection. In part 1, I demonstrated how to develop the application locally, and the app will be deployed via Amazon Managed Service for Apache Flink in this post.
Apache Flink is widely used for building real-time stream processing applications. On AWS, Amazon Managed Service for Apache Flink is the easiest option to develop a Flink app as it provides the underlying infrastructure. Re-implementing a solution from an AWS workshop, this series of posts discuss how to develop and deploy a fraud detection app using Kafka, Flink and DynamoDB. Part 1 covers local development using Docker while deployment via KDA will be discussed in part 2.
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.
In Part 3, we developed a data ingestion pipeline using Kafka Connect source and sink connectors without enabling schemas. Later we discussed the benefits of schema registry when developing Kafka applications in Part 5. In this post, I'll demonstrate how to enhance the existing data ingestion pipeline by integrating AWS Glue Schema Registry.
The suite of Apache Camel Kafka connectors and the Kinesis Kafka connector from the AWS Labs can be effective for building data ingestion pipelines that integrate AWS services. In this post, I will illustrate how to develop the Camel DynamoDB sink connector using Docker. Fake order data will be generated using the MSK Data Generator source connector, and the sink connector will be configured to consume the topic messages to ingest them into a DynamoDB table.
Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. In this post, I will illustrate how to set up a data ingestion pipeline using Kafka connectors. Fake customer and order data will be ingested into the corresponding topics using the MSK Data Generator source connector. The topic messages will then be saved into a S3 bucket using the Confluent S3 sink connector.