![]() Get a list of topics curl " curl " In order to the access from on-premise or local machine, makesure the ec2 instance on which the rest server is running has public ip or elastic ip attached. Start rest server kafka-rest-start kafka-rest.properties &Īccess the MSK via rest API with curl or rest client/browser. = ** modify the bootstrapserver and zookeeper url/ips. This framework is not full-fledged kafka client and doesn't allow all operation of kafka client, but you can do most operation on the cluster starting from fetching metadata of cluster, topic information, producing and consuming the message,etc.įirst set up the confluent repo and ec2 instance security group (Refer - Section-1: Pre Install or set up- additional kafka components) and then install/setup kafka rest proxy. To access the MSK cluster from local machine or on-premise systems, you can set up kafka Rest Proxy framework open-sourced by Confluent to acess the MSK cluster from the outside world via rest api. To access through kafka client, you need to launch ec2 instance in the same vpc of MsK and execute kafka client(producer/consumer) to acess msk cluster. Because the broker url, zookeeper connection string are private ip's of the msk cluster vpc/subnet. You cannot access MSK directly from on-premise or local machine using kafka client or kafka stream. I) How can I access AWS MSK with the kafka clients running on my on premise system? I will try to answer your questions based on my personal experience: Ideally, it should be able to perform all/most things that open source Kafka supports.Īlso if you have specific use case or requirement that is not documented, I will suggest you contact AWS support for further clarification regarding the managed part of kafka cluster (maximum number of broker allowed, reliability, cost). MSK is basically the vanilla apache kafka cluster customized and managed by aws (with predefined configuration settings based on cluster instance type, number of brokers,etc) tuned for the cloud environment. Vii) Also how reliable is MSK compared to other cloud-based kafka cluster from Azure/confluent and any performance benchmark compared to vanilla kafka? And what is the maximum number of brokers that can be lauched in cluster? Vi) How can I perform real-time predictive analysis of data flowing through MSK? ![]() V) Can I use streaming sql with MSK through ksql? How can I set up KSQL with MSK? Iv) Is it possible to integrate MSK with other AWS service (e.g Redshift,EMR,etc)? Iii) Will MSK provide some way to update some cluster or tuning configuration? Like aws glue provides parameter change for spark executr and driver memory in their managed environment. Ii) Does MSK support schema evolution and exactly once semantics? I) How can I access AWS MSK with the Kafka clients running on my on-premise system? ![]() I tried setting up the msk cluster and was validating whether msk could fulfill all the use case/requirement of our company, but currently, it lacks documentation and example. The supported APIs are available on our API coverage page, which provides information on the extent of EMR’s integration with LocalStack.I'm evaluating AWS Managed Service Kafka (MSK) and I know that currently, it is in preview mode, hence might not have all features or proper documentation. LocalStack also supports EMR Serverless to create applications and job runs, to run your Spark/PySpark jobs locally. EMR utilizes various tools in the Hadoop and Spark ecosystem, and your EMR instance is automatically configured to connect seamlessly to LocalStack’s S3 API. LocalStack Pro supports EMR and allows developers to run data analytics workloads locally. Developers can leverage these frameworks and their rich ecosystem of tools and libraries to perform complex data transformations, machine learning tasks, and real-time data processing. EMR supports various big data processing frameworks, including Hadoop MapReduce, Apache Spark, Apache Hive, and Apache Pig. Get started with Elastic MapReduce (EMR) on LocalStackĪmazon EMR (Elastic MapReduce) is a fully managed big data processing service that allows developers to effortlessly create, deploy, and manage big data applications. Accessing a resource created by LocalStack.Accessing LocalStack via the endpoint URL.Patched AWS SDKs for Lambdas (Deprecated).Getting started with the Cloud Pods CLI.Managed Workflows for Apache Airflow (MWAA).
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