Google Cloud Dataflow
Google BigQuery
Amazon EMR
Databricks
Google Cloud Dataproc
Qubole
Snowflake
Amazon Kinesis
Lenses.io
Confluent
Striim
Amazon Kinesis
Calisti
Fivetran
Azure Event Hubs
HVR
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.
Google Cloud Dataflow Lenses.ioBased on our record, Google Cloud Dataflow should be more popular than Lenses.io. It has been mentiond 14 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.
Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 1 year ago
This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 1 year ago
I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 1 year ago
You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 1 year ago
It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / almost 2 years ago
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
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Confluent - Confluent offers a real-time data platform built around Apache Kafka.
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
Striim - Striim provides an end-to-end, real-time data integration and streaming analytics platform.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
