DataOps
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.
Here are 152 public repositories matching this topic...
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Nov 27, 2017 - R
DataOps for Government
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Sep 13, 2018
This is a simple DataOps attempt using Liquibase..
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Mar 13, 2019 - Python
Step-by-step instructions on how to set up a virtual machine for Data Science usiing Cloud Infrastructures
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Nov 7, 2019 - R
HokStack - Run Hadoop Stack on Kubernetes
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May 10, 2020 - Shell
Cloud Native Data Management Landscape
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May 17, 2020
In-deprecation. For Lenses please check lensesio/lenses-helm-charts. Soon Stream Reactor will also get its own Helm repository.
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Aug 2, 2020 - Smarty
Azure Team Data Science Process project template
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Oct 19, 2020
A DBT package to perform DataOps & administrative CI/CD on your data warehouse.
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May 11, 2021
This repository contains source code based on a guide on how to use Cloud Build and Cloud Composer to create a CI/CD pipeline for building, testing, and deploying a data processing workflow
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Jul 12, 2021 - Shell
This repo hosts an example of a full data pipeline that shows how to implement CI/CD on Azure DevOps by testing data quality with great_expectations and code quality with SonarQube
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Sep 6, 2021 - Python
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Oct 18, 2021 - Shell
The best Python package for comparing two dataframes
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Dec 29, 2021 - Python