Name: Great Expectations
Author: Open Source

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### Data Quality Management
Users consistently praise Great Expectations for its robust data quality management capabilities. They highlight the ease of use, automation of data quality checks, and the ability to ensure data accuracy and reliability. The tool's efficiency in reducing manual work, improving data quality, and enhancing decision-making processes is widely acknowledged by data professionals. Additionally, the open-source nature of Great Expectations makes it accessible to a wide range of users, further solidifying its reputation as a valuable asset in data quality management.

### Data Validation and Testing
Users generally agree that Great Expectations excels in data validation and testing capabilities. They appreciate the simplicity and efficiency of the declarative nature of expectations, automated profiling, and validation tools. The tool's ability to handle various data issues, such as type mismatches, outliers, and data drift, is highly valued by users. Additionally, the Data Docs feature is praised for its ability to provide comprehensive data documentation, especially for complex databases. While some users find the initial setup cumbersome, the overall consensus is that Great Expectations significantly aids in automating data testing, reducing manual work, and ensuring data quality and reliability.

### Data Documentation and Profiling
Users unanimously praise Great Expectations for its exceptional data documentation and profiling capabilities. The tool's rich documentation and automated profiling features have garnered high acclaim from users, who find it invaluable for ensuring data quality and streamlining their data validation processes. The ability to easily configure the data context using the Great Expectations CLI has been highlighted as a particularly user-friendly aspect of the tool. Additionally, users appreciate how Great Expectations simplifies the generation of comprehensive data documentation, saving them time and effort in manual documentation tasks.

### Python Integration and Libraries
Reviewers have expressed a mix of opinions regarding Great Expectations' Python integration and libraries capabilities. While many users appreciate the ease of use and the ability to focus on data analysis rather than data quality concerns, some have highlighted limitations in terms of compatibility with all data sources. The need for occasional custom code integration has been mentioned as a drawback, requiring additional time and coding knowledge. Despite these challenges, the overall consensus leans towards the positive side, acknowledging the efficiency and reliability that Great Expectations brings to automating data testing processes.

### Automation and Pipeline Support
While some users appreciate the time-saving aspect of automating data quality checks and the ability to monitor data quality over time, others express concerns about the tool's limitations, such as slow Git processing and the need for dashboard improvements. The consensus seems to be that Great Expectations provides a reliable solution for automating data testing but may require enhancements in certain areas to optimize the pipeline support functionality.

Great Expectations has proven to be a valuable tool for solving various business problems related to data quality and maintenance. Users have found that the software effectively bridges communication gaps between teams, ensuring proper data validation and maintenance. By automating the profiling process, Great Expectations reduces manual effort and workload for users. This feature, triggered via the command line, has been particularly beneficial in streamlining the data testing and validation process. Additionally, the automatic documentation generation provided by Great Expectations has been highly praised by users for reducing their effort and workload in managing increasing project documents. 

Data professionals have also found Great Expectations invaluable in addressing data quality challenges and minimizing the risk of errors. With the ability to define rules for data and detect issues early on, users find it easier to understand complex datasets and communicate their properties effectively. This tool not only reduces errors but also improves reliability and understanding, saving organizations time and money while enabling better data-driven decision-making. The central repository in Great Expectations allows teams to monitor data quality over time and quickly catch any data issues, providing confidence in their data. Moreover, users appreciate how Great Expectations streamlines knowledge capture, facilitates rich data documentation, and efficiently manages large datasets. The software's wide range of data quality checks, including outliers validation and an enriched document library, further supports users' work. Lastly, the Data Docs feature has been widely praised as superb for helping users develop a comprehensive understanding of underlying mechanisms without the need for manual verification of every column. Overall, Great Expectations serves as a reliable and efficient solution that solves critical business problems related to data integrity, maintenance, and communication.

Data Validation and Management: Many users have found Great Expectations to be extremely helpful in maintaining and validating data. It has effectively bridged the gap between teams and facilitated the tracking of large code bases. The platform's variety of features and ease of use have simplified data quality management, allowing users to focus on data analysis instead.

Early Detection of Data Issues: Users have praised Great Expectations for its impressive ability to catch data issues early on, preventing them from impacting data products. This has contributed to better decision-making and organizational development. The automated profiling and validation tools provided by the platform are considered life-savers by many reviewers.

Open-Source Nature: The open-source nature of Great Expectations has been highly appreciated by users as it makes the tool accessible to everyone without any financial cost. This accessibility ensures that accurate, reliable, and consistent data can be maintained across different projects.

Ongoing Maintenance and Updates: Users have expressed the need for ongoing maintenance and updates to ensure compatibility with tools and services, which has been mentioned by multiple reviewers. 

Limited Data Source Support: Some users have found that Great Expectations does not support certain data sources, requiring a strong understanding of data validation and testing concepts. This limitation has been mentioned by several reviewers.

Challenging Setup Process: Reviewers with limited technical backgrounds have found it challenging to set up and use Great Expectations. Multiple users have shared this sentiment about the setup process.

Great Expectations is a shared, open standard, open source and free tool for data quality, supported by Superconductive. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.  The tool can be used for validating, documenting, and profiling data to maintain quality and improve communication between teams.

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Great Expectations

Data Quality

Great Expectations 201: Customizing for Collaboration with your Data Team

John Joyce (Acryl Data) gives a demo of Displaying Data Quality Checks in the DataHub UI during the February Town Hall. 

Learn more about DataHub: https://datahubproject.io
Join us on Slack: http://slack.datahubproject.io
Follow us on Twitter: https://twitter.com/datahubproject

Great Expectations Outcomes in DataHub

This video was taken during the March 2022 Great Expectations monthly community event. You can join the next one here: https://greatexpectations.io/meetup and join the discussion here: http://greatexpectations.io/slack

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This presentation is given by Maggie Hays & John Joyce is a demo of DataHub and its integration with Great Expectations.

Slides: https://docs.google.com/presentation/d/1VHUvxzzn5aSGy7PUAAiCsCJwZm4KtMRwQQanmnQrosQ/edit?usp=sharing

DataHub and Great Expectations Integration Demo

Opening to Great Expectations Demo VHS (1998)

Open-Source Spotlight - Great Expectations (Data Quality Platform) - James Campbell

The Expectations Gallery Demo

Great Expectations Webinar: New Capabilities in New-Style Data Sources

Biodanza Great Expectations TV interview & demo - Kate Clement

Open Source

Data quality tools are designed to ensure that business data is as reliable as possible so that it can provide a solid basis for effective decision-making

Data source connectivity

Ease of connection to multiple data sources

Data profiling

Ability to find anomalies and hidden relationships between data elements

Master data management (MDM) integration

Data element standardization

Parsing and standardization of data elements according to predefined rules

Match and merge

Ability to identify potential matches and then merge

Address verification

Data format checking and valid address checking

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What is Great Expectations?