From 49fa51e8f8c844526c6672def984693baed24393 Mon Sep 17 00:00:00 2001 From: cbergh Date: Thu, 23 May 2024 10:15:18 -0400 Subject: [PATCH] Create DataKitchen.md (#65) * Create DataKitchen.md new open source data observability and data quality product * Apply suggestions from code review Add metadata --------- Co-authored-by: JP (he/him) --- Tools/Data Quality/DataKitchen.md | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 Tools/Data Quality/DataKitchen.md diff --git a/Tools/Data Quality/DataKitchen.md b/Tools/Data Quality/DataKitchen.md new file mode 100644 index 0000000..aaac3c1 --- /dev/null +++ b/Tools/Data Quality/DataKitchen.md @@ -0,0 +1,29 @@ +--- +Aliases: [Data Kitchen] +Tags: [seedling] +publish: true +--- + +![apache 2.0 license Badge](https://img.shields.io/badge/License%20-%20Apache%202.0%20-%20blue) +![PRs Badge](https://img.shields.io/badge/PRs%20-%20Welcome%20-%20green) +[![Documentation](https://img.shields.io/badge/docs-On%20datakitchen.io-06A04A?style=flat)](https://docs.datakitchen.io/articles/#!open-source-data-observability/data-observability-overview) +[![Static Badge](https://img.shields.io/badge/Slack-Join%20Discussion-blue?style=flat&logo=slack)](https://data-observability.slack.com) + +*

Data breaks. Servers break. Your toolchain breaks. Ensure your data team is the first to know and the first to solve with visibility across and down your data estate. Save time with simple, fast data quality test generation and execution. Trust your data, tools, and systems end to end.

* + +DataKitchen Open Source Data Observability product suite (released April 2024). +* [**DataOps TestGen**](https://docs.datakitchen.io/articles/dataops-testgen-help/dataops-testgen-help) is a data quality verification tool that does five main tasks: (1) data profiling, (2) new dataset screening and hygiene review, (3) algorithmic generation of data quality validation tests, (4) ongoing production testing of new data refreshes and (5) continuous periodic monitoring of datasets for anomalies [(GitHub)](https://github.com/DataKitchen/dataops-testgen). +* [**DataOps Observability**](https://docs.datakitchen.io/articles/dataops-observability-help/dataops-observability-help) monitors every tool used in the journey of data from data source to customer value, from any team development environment into production, across every tool, team, data set, environment, and project so that problems are detected, localized, and understood immediately [(GitHub)](https://github.com/DataKitchen/dataops-observability). + +![DatKitchen Open Source Data Observability](https://datakitchen.io/wp-content/uploads/2024/04/both-products.png) + +For background on why we build this product check out the articles on ['why we open sourced'](https://datakitchen.io/why-we-open-sourced-our-data-observability-products/), [manifesto](https://datajourneymanifesto.org/), [free book](https://datakitchen.io/the-dataops-cookbook/), and [top data observability and DataOps articles](https://datakitchen.io/datakitchen-resource-guide-to-data-journeys-data-observability-dataops/). + +%% wiki footer: Please don't edit anything below this line %% + +## This note in GitHub + +[Edit In GitHub](https://github.dev/data-engineering-community/data-engineering-wiki/blob/main/Tools/Data%20Quality/DataKitchen.md "git-hub-edit-note") | [Copy this note](https://raw.githubusercontent.com/data-engineering-community/data-engineering-wiki/main/Tools/Data%20Quality/DataKitchen.md "git-hub-copy-note") + +Was this page helpful? +[👍](https://tally.so/r/mOaxjk?rating=Yes&url=https://dataengineering.wiki/Tools/Data%20Quality/DataKitchen) or [👎](https://tally.so/r/mOaxjk?rating=No&url=https://dataengineering.wiki/Tools/Data%20Quality/DataKitchen)