Proactive data monitoring: A strategy for end-to-end data observability
This guide explores:
- How leveraging data diffs, shift-left testing, and upstream monitoring helps prevent data quality issues before they start
- The four key monitors that ensure comprehensive data quality coverage
- A practical approach to implementing end-to-end data observability
Proactive data monitoring: A strategy for end-to-end data observability
This guide explores:
- How leveraging data diffs, shift-left testing, and upstream monitoring helps prevent data quality issues before they start
- The four key monitors that ensure comprehensive data quality coverage
- A practical approach to implementing end-to-end data observability
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Proactively prevent errors from entering your data pipeline
Go from 0 to 100% test coverage of your ELT and BI Code.- See how a change to the source code anywhere in your pipeline impacts the produced data, downstream columns, metrics and BI dashboards.
- Integrate into CI/CD workflow to prevent data regressions.
Be the first to know about data incidents
- With ML-based smart alerts, be the first to know when there’s an anomaly with your data pipeline so you can inform relevant stakeholders, stay ahead of the data outages, and identify the root causes.
- Integrate into CI/CD workflow to prevent data regressions.
Find the data you can trust
- Datafold unifies search and documentation for all datasets and BI dashboards across your data platform and helps data users find relevant and trustworthy data faster and instantly assess the quality and profile of any dataset.
- Datafold automatically indexes all data in warehouses and imports descriptions and metadata from ELT tools such as Airflow and dbt, providing a single source of truth for data producers and consumers
Accelerate your move to new data stacks
- Whether you’re migrating to a new database or changing ETL tools, you’ll need to know which data to migrate while also proving to your stakeholders that the migration was successful.
- Data Catalog with profiling and lineage help you identify core data vs columns or tables that can be deprecated, while Data Diff showcases exactly which rows or columns are different in the migration.
HANDLE DATA WITH CARE
Integrations with the entire modern data stack
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Our progress would not be possible without the support of our early adopters
"Datafold makes it a lot easier to understand the impact of your change on downstream data. The tool is super easy to use and does a great job highlighting exactly where there are differences in your data in a digestible way".
Zachary Baustein
Lead Data Analyst
“Datafold's column-level lineage gives confidence in the whole system. If my stakeholders ask “why is this dashboard out of date?” I can answer in 25 seconds instead of digging through pull requests for hours. As a product owner, I can understand how the rest of the company makes decisions based on the data we produce. It brings data confidence and visibility to the company.”
Maura Church
Director of Data Science
“When everything is correct, Datafold clearly saves time on testing; but when something is wrong or there’s an error, it saves unimaginable amounts of time that would go into finding and fixing bad data.”
Ezgi Ozcan
Product Analyst
"Datafold makes it a lot easier to understand the impact of your change on downstream data. The tool is super easy to use and does a great job highlighting exactly where there are differences in your data in a digestible way".
Dave Wallace
Sr Data Engineer
"Datafold is a game-changer— there is so much value in actually understanding the effect of your pull request. It gives me the confidence that my code does what I expect it to do."
Josh Devlin
Analytics Engineer
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