kafka-manager
deequ
kafka-manager | deequ | |
---|---|---|
13 | 17 | |
11,676 | 3,134 | |
0.2% | 0.9% | |
0.0 | 7.4 | |
9 months ago | 5 days ago | |
Scala | Scala | |
Apache License 2.0 | Apache License 2.0 |
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kafka-manager
- FLaNK Stack Weekly 16 October 2023
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UI for Apache Kafka - An open-source tool for monitoring and managing Apache Kafka Clusters - v0.17 release
Are there any comparison to CMAK or Kafdrop ?
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Running multi-broker Kafka using docker
Dockerized kafka manager (Yahoo CMAK)
- UI for Serverless AWS MSK (Kafka)
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A list of GUI tools for working with Apache Kafka
Cluster Manager for Apache Kafka (CMAK)
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What companies/startups are using Scala (open source projects on github)?
There are so many of them in big data, e.g. Kafka, Spark, Flink, Delta, Snowplow, Finagle, Deequ, CMAK, OpenWhisk, Snowflake, TheHive, TVM-VTA, etc.
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Is there recommed UI for Kafka like RabbitMQ?
We're using CMAK (previously known as kafka manager) https://github.com/yahoo/CMAK
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Any advice on setting up or working with Kafka?
Some random tips: * Use SSDs, not magnetic disks. Our Kafka brokers used to use magnetic disks for more throughput, but this caused wayyyy more problems, like very slow broker restarts. * You'll want to install something like Burrow so you can get better lag metrics. * You might want to install CMAK. It's a web interface for common ops tasks.
- What kind of monitoring tools are people using for their Kafka Deployment?
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Gathering opinions on kafka management tools
I've used Yahoo's CMAK before for these things -
deequ
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[Data Quality] Deequ Feedback request
There's no straightforward way to drop and rerun a metric collection. For example, say you detect a problem in your data. You fix it, rerun the pipeline, and replace the bad data with the good. You'd want your metrics history to reflect the true state of your data. But the "bad run" cannot be dropped. Issue
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Thoughts on a business rules engine
I had similar requirements for QA reporting on large and diverse data sets. I implemented data check pipelines, with rules in AWS Deequ (https://github.com/awslabs/deequ) running on an Apache Spark cluster. The Deequ worked well for me, but there were a few cases where I opted to write the rule checks in the data store to improve throughput (i.e. SQL checks on critical data elements on the database).
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Building a data quality solution for devs and business people
Hey all! At the companies where I've worked as a developer, I've found that business stakeholders typically want a concrete way to check and assure the quality of data that pipelines are producing, before other downstream systems and users get impacted. I've tested solutions like Deequ, but I found that it made building compliance and data rules a bit more complicated and put a greater emphasis on developers to get the rules right that business was expecting. I also experienced issues with running checks in parallel and getting row level details about the failures.
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deequ VS cuallee - a user suggested alternative
2 projects | 30 Nov 2022
- November 15-19, 2022 FLiP Stack Weekly
- What are your favourite GitHub repos that shows how data engineering should be done?
- Well designed scala/spark project
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Soda Core (OSS) is now GA! So, why should you add checks to your data pipelines?
GE is arguably the most well known OSS alternative to Soda Core. The third option is deequ, originally developed and released in OSS by AWS. Our community has told us that Soda Core is different because itโs easy to get going and embed into data pipelines. And it also allows some of the check authoring work to be moved to other members of the data team. I'm sure there are also scenarios where Soda Core is not the best option. For example, when you only use Pandas dataframes or develop in Scala.
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Congrats on hitting the v1 milestone, whylabs! You're r/MLOps OSS tool of the month!
I wonder how this compares with tools like DeeQu (https://github.com/awslabs/python-deequ - requires Spark) or Pandas Profiling? One plus side I can see is that it doesn't require Apache Spark to run profiling (though a quick look at the code indicates that they are working on Spark support) and can work with real time systems.
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What companies/startups are using Scala (open source projects on github)?
There are so many of them in big data, e.g. Kafka, Spark, Flink, Delta, Snowplow, Finagle, Deequ, CMAK, OpenWhisk, Snowflake, TheHive, TVM-VTA, etc.
What are some alternatives?
akhq - Kafka GUI for Apache Kafka to manage topics, topics data, consumers group, schema registry, connect and more...
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
kowl - Redpanda Console is a developer-friendly UI for managing your Kafka/Redpanda workloads. Console gives you a simple, interactive approach for gaining visibility into your topics, masking data, managing consumer groups, and exploring real-time data with time-travel debugging. [Moved to: https://github.com/redpanda-data/console]
azure-kusto-spark - Apache Spark Connector for Azure Kusto
kafka-ui - Open-Source Web UI for Apache Kafka Management
dbt-data-reliability - dbt package that is part of Elementary, the dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
kafdrop - Kafka Web UI
Quill - Compile-time Language Integrated Queries for Scala
Burrow - Kafka Consumer Lag Checking
BigDL - Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max). A PyTorch LLM library that seamlessly integrates with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, etc.
kafka_exporter - Kafka exporter for Prometheus
re_data - re_data - fix data issues before your users & CEO would discover them ๐