Lenses.io
Confluent
Striim
Amazon Kinesis
Calisti
Fivetran
Azure Event Hubs
HVR
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
Lenses.io delivers DataOps for any self-managed or Cloud Apache Kafka including AWS MSK, Azure HDInsight and Confluent Cloud. Lenses provides self-service platform administration, security, governance and monitoring for Kafka.
Lenses makes engineering teams working with Apache Kafka successful by improving productivity and reducing complexity leading up to 95% faster time to market and reduced operational cost. This means faster and more predictable delivery of strategic real-time projects.
Lenses.io Amazon EMRBased on our record, Amazon EMR should be more popular than Lenses.io. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I liked https://lenses.io/ Lots of capabilities but it's not free as I know. Source: about 1 year ago
Currently I'm using Lenses:https://lenses.io/ as UI tool, but while turning on kafka-start-server server.properties and launch the UI on localhost, it failed to connect:. Source: about 2 years ago
Oh thats sad to hear ... lenses.io is so powerful I am not sure how I would have gotten by to this point without it! Source: about 2 years ago
In Addition to the more critical replys from others, why Kafka still makes sense imo: - you could organist everything with custom pipeline/services at your scale. The cost of maintaining those and keep the technology up to date is growing exponentially with every new Service. Kafka offers ansinge Plattform with a couple components which need to be kept up to date. - deploying, managing and monitoring those service... Source: over 2 years ago
Https://lenses.io/ Kafka UI is pretty solid. Nice to see an open source alternative here. - Source: Hacker News / over 2 years ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
Confluent - Confluent offers a real-time data platform built around Apache Kafka.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Striim - Striim provides an end-to-end, real-time data integration and streaming analytics platform.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost