s3-sqs-connector
deequ
s3-sqs-connector | deequ | |
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6 | 17 | |
16 | 3,134 | |
- | 0.9% | |
0.0 | 7.4 | |
18 days ago | 5 days ago | |
Scala | Scala | |
Apache License 2.0 | Apache License 2.0 |
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s3-sqs-connector
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Upload to S3 -> AWS lambda with some Scala Spark code -> Process -> Write back to S3
Are you planning on uploading and processing many files to S3? If so I would use something like Structured Streaming with the FileSource which can detect new files uploaded to S3 and process them in on a "standard" Spark cluster. You can then build a very easy to deploy and operate cluster on EKS/Kubernetes. I would check out: https://github.com/qubole/s3-sqs-connector once the number of files you upload start to get really large. Glue could also be used to achieve roughly the same thing and without the hassle of managing the EKS/K8s clusters.
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 it’s 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?
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
Jupyter Scala - A Scala kernel for Jupyter
azure-kusto-spark - Apache Spark Connector for Azure Kusto
LearningSparkV2 - This is the github repo for Learning Spark: Lightning-Fast Data Analytics [2nd Edition]
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.
Spark Utils - Basic framework utilities to quickly start writing production ready Apache Spark applications
Quill - Compile-time Language Integrated Queries for Scala
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]
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.
re_data - re_data - fix data issues before your users & CEO would discover them 😊
SynapseML - Simple and Distributed Machine Learning