Get all your data testing, documentation, and profiling in Secoda.
Add to SecodaGreat Expectations is a shared, open standard for data quality. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.
The Secoda and great-expectations connection allows users to add data validation to their big data strategies. This process allows users to quickly identify discrepancies in their data, so they can address them quickly and accurately. It also gives users valuable insights into data trends and anomalies that can help inform decision-making. It helps ensure that data is accurate and up to date, leading to increased efficiency in working with data. By utilizing Secoda, users can also view their data quickly and easily, giving them greater control over their data.
Secoda provides data lineage diagrams that are easy to access and read. To view them, simply open the project dashboard and click on the Dataset Lineage section in the left-hand navigation. For each dataset, you will be able to see how it was created and changed over time, from what sources it was derived, and how it was used. The labels and structure of the diagram is intuitive and helpful to understanding how the data was created and used.
A data dictionary can help streamline the usage of a data catalog using great-expectations. By allowing users to quickly and easily document data elements and their corresponding formats, a data dictionary offers a comprehensive look into the structure and expected use of a given data set. This helps streamline data exploration, utilization and validation to ensure accuracy of results. Secoda provides an easy to use, no code integration that helps users create their own data dictionary for great-expectations, which helps them quickly achieve their data goals.
Sharing great-expectations knowledge with everyone at your company can provide invaluable benefits. It helps create an environment of understanding and could potentially reduce the amount of time spent on tasks. Additionally, it encourages collaboration and communication, creating a more positive and productive work environment.
Creating a single source of truth based on great-expectations metadata helps to ensure that data is updated and consistent across all systems. This helps to reduce redundancy and mistakes, while allowing the organization to trace issues quickly and accurately, allowing the data to be analyzed and acted upon in a timely manner. It also serves as an automated compliance tool, ensuring data governance requirements are met. Through this approach, organizations can trust that data entering their systems is valid and reliable.
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