deequ
Snowplow
deequ | Snowplow | |
---|---|---|
17 | 21 | |
3,126 | 6,737 | |
0.6% | 0.2% | |
7.4 | 8.7 | |
14 days ago | about 1 month ago | |
Scala | Scala | |
Apache License 2.0 | Apache License 2.0 |
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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.
Snowplow
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Open-source data collection & modeling platform for product analytics
We’ve also thought about Ops :-). There’s a backend 'Collector' that stores data in Postgres, for instance to use while developing locally, or if you want to get set up quickly. But there’s also full integration with Snowplow, which works seamlessly with an existing Snowplow setup as well.
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What are the different ways to collect large amounts of data, like millions of rows?
Sure thing! Say you run an online store. Your source systems could be the inventory, orders or customer databases. You could also track click/site behavior with something like snowplow. An ERP system is essentially just a combination of what I mentioned previously. Another good example is a CRM such as Salesforce or Zendesk. Hopefully that helps!
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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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We should start looking for google analytics alternatives
I added Snowplow Analytics to a site with a lot of traffic. It was a very basic implementation, where data is collected with Snowplow, stored in google big query, and visualized in google data studio. The data is collected from the caching/web server combined with a client-side tracker.
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The Big Data Game – Because even a simple query can send you on an unexpected journey. Help the 8-bit data engineer to get the data
Well if you have to structure and create Schema and manage Data Warehouses, you need a tool to do that, so in the background you see SnowPlow, which helps you do just that. Make the data into some kind of sensible structure so that later on business analysts can come see whats up. Want to do a quarterly report on how you performed, go to the application that goes to the data warehouse and builds your report for you. Want to compare to other similar companies in the portfolio to see how they are performing, same story. Data scientists will build and structure the data and store it and manipulate it and extract the value from it so that the analysts and sales people can then come in and do some selling. Show the customers what they got for their money and guarantee the renewal.
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Click tracking solution for links and buttons on website
if you want self host, check out https://github.com/snowplow/snowplow
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Reference Data Stack for Data-Driven Startups
We also have telemetry set up on our Monosi product which is collected through Snowplow,. As with Airbyte, we chose Snowplow because of its open source offering and because of their scalable event ingestion framework. There are other open source options to consider including Jitsu and RudderStack or closed source options like Segment. Since we started building our product with just a CLI offering, we didn’t need a full CDP solution so we chose Snowplow.
- Austrian Data Protection Authority declares Google Analytics as not compliant with GDPR. Decision relevant for almost all EU websites.
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Ask HN: Best alternatives to Google Analytics in 2021?
https://matomo.org
That's the only full featured open source competitor I am aware of, so it should be mentioned.
https://snowplowanalytics.com/
Somewhat FOSS. There was a story there, but I don't remember the details.
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Cookie-based tracking is dead
I added Snowplow Analytics to a site with a lot of traffic. It was a very basic implementation, where data is collected with Snowplow, stored in google big query, and visualized in google data studio. The data is collected from the caching/web server combined with a 1st part cookie set in the user's browser.
What are some alternatives?
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
PostHog - 🦔 PostHog provides open-source product analytics, session recording, feature flagging and A/B testing that you can self-host.
azure-kusto-spark - Apache Spark Connector for Azure Kusto
Rudderstack - Privacy and Security focused Segment-alternative, in Golang and React
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.
Matomo - Empowering People Ethically with the leading open source alternative to Google Analytics that gives you full control over your data. Matomo lets you easily collect data from websites & apps and visualise this data and extract insights. Privacy is built-in. Liberating Web Analytics. Star us on Github? +1. And we love Pull Requests!
Quill - Compile-time Language Integrated Queries for Scala
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
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.
jitsu - Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days
re_data - re_data - fix data issues before your users & CEO would discover them 😊
Druid - Apache Druid: a high performance real-time analytics database.