My main use case for Datafold is as a data observability platform that we leverage for many of our data products, particularly because our data is very susceptible to any kind of breakdown or catastrophe due to its confidential nature. Datafold helps us identify, prioritize, and deep dive into data quality issues which can occur anytime. The platform finds these issues and investigates them in a proactive manner, and apprises us to rectify them before they hit production. Currently, we are using it to improve the quality of our organizational assessments and tests. It helps us test our various products and services, validate various parameters, and monitor whether the production cycle is happening in a synchronous manner. It is assisting us significantly in terms of migrating data from less secure platforms to more secure platforms as per clientele need. Datafold is continuously working 24/7 contextualizing the data ensuring that migrations happen accurately, as well as reconciling data to ensure there are no gaps, providing complete monitoring from start to end. Our organization started deploying Datafold primarily due to outstanding peer feedback we received regarding its user-friendly interface and the quality of data monitoring, which is amazing. Since we deal in agriculture, where personal farmer data must not leak, Datafold helps us simplify the complicated process of collecting diverse data in a streamlined manner and track it from start to end, ensuring a high level of accuracy is maintained during data collection, transferring, migrating, and ultimately monitoring. This has significantly improved our operations, and we are continuing with Datafold.
My main use case for Datafold is to automatically compare data differences through data diffing, where I can compare datasets row-by-row, column-by-column, to surface unexpected changes. I also use it for CI/CD integration for data pipelines, where we embed data quality checks into GitHub and GitLab pull requests. Another interesting feature I use Datafold for is column-level lineage to trace how specific columns flow through SQL transformations across the data stack we have. Recently, for data diffing, there was one time we wanted to conduct a lot of comparisons on a very large dataset to be sure that all was in check before we migrated to Snowflake, which is a complex and resource-heavy process. We used Datafold to do the automatic checks across rows and columns to ensure that when we completed the migration, no data was lost and the migration into Snowflake was very smooth. That was in March, and it was very useful. For a friend who uses Datafold in their enterprise, an engineer made a change from an SQL model and did not realize it would cascade and alter downstream dashboard metrics. However, with the help of Datafold integrated into the pull request workflow, the data diff actually surfaced exactly which columns changed, which rows were affected, and by how much.
Datafold enhances data engineering by streamlining data quality and security processes, offering robust insights and automation for faster and more accurate analytics outcomes.
Datafold provides a comprehensive set of tools for data engineers to manage and validate data pipelines while ensuring data accuracy. By automating data quality checks and offering in-depth analytics, it supports a seamless transition from data collection to actionable insights. Datafold reduces risks associated with...
My main use case for Datafold is as a data observability platform that we leverage for many of our data products, particularly because our data is very susceptible to any kind of breakdown or catastrophe due to its confidential nature. Datafold helps us identify, prioritize, and deep dive into data quality issues which can occur anytime. The platform finds these issues and investigates them in a proactive manner, and apprises us to rectify them before they hit production. Currently, we are using it to improve the quality of our organizational assessments and tests. It helps us test our various products and services, validate various parameters, and monitor whether the production cycle is happening in a synchronous manner. It is assisting us significantly in terms of migrating data from less secure platforms to more secure platforms as per clientele need. Datafold is continuously working 24/7 contextualizing the data ensuring that migrations happen accurately, as well as reconciling data to ensure there are no gaps, providing complete monitoring from start to end. Our organization started deploying Datafold primarily due to outstanding peer feedback we received regarding its user-friendly interface and the quality of data monitoring, which is amazing. Since we deal in agriculture, where personal farmer data must not leak, Datafold helps us simplify the complicated process of collecting diverse data in a streamlined manner and track it from start to end, ensuring a high level of accuracy is maintained during data collection, transferring, migrating, and ultimately monitoring. This has significantly improved our operations, and we are continuing with Datafold.
My main use case for Datafold is to automatically compare data differences through data diffing, where I can compare datasets row-by-row, column-by-column, to surface unexpected changes. I also use it for CI/CD integration for data pipelines, where we embed data quality checks into GitHub and GitLab pull requests. Another interesting feature I use Datafold for is column-level lineage to trace how specific columns flow through SQL transformations across the data stack we have. Recently, for data diffing, there was one time we wanted to conduct a lot of comparisons on a very large dataset to be sure that all was in check before we migrated to Snowflake, which is a complex and resource-heavy process. We used Datafold to do the automatic checks across rows and columns to ensure that when we completed the migration, no data was lost and the migration into Snowflake was very smooth. That was in March, and it was very useful. For a friend who uses Datafold in their enterprise, an engineer made a change from an SQL model and did not realize it would cascade and alter downstream dashboard metrics. However, with the help of Datafold integrated into the pull request workflow, the data diff actually surfaced exactly which columns changed, which rows were affected, and by how much.