Datafold

For the past year, we've listened intently to the challenges data teams face—ensuring data integrity across their entire stack, scaling pipelines, and maintaining parity across complex systems. Today, we're taking a significant step forward to address these needs. Introducing the new Datafold: Unified, Proactive, Powerful. Datafold now supports Monitors, a versatile new product designed to give data teams real-time visibility into data issues across their entire stack. Whether it’s spotting unexpected schema changes, validating data across databases, or detecting anomalies in key metrics, Datafold’s new Monitors help you quickly resolve issues before they impact your business. Learn more about how Datafold and Monitors are helping modern data teams proactively prevent data quality issues, and automate data testing across their entire stack: https://lnkd.in/gqf_S9y9

November ?? has been very busy for the Datafold team! Check out all the product updates we've been working on: ?? REST API access for Datafold monitors ?? Use your local time zone in the Datafold app (goodbye UTC...if you want!) ? ?Attach a CSV of failed data test records directly to notifications—so you don’t have to go digging around in your warehouse for problematic data ...and many more ?? https://lnkd.in/gwA3nqAw

The Datafold team had such a great time chatting with folks about their data migrations and data quality pain points at Databricks's Amsterdam World Tour stop! Next stop: Atlanta ?? !

The new Power BI integration in Datafold is here! Now, easily get complete visibility into your data from source to Power BI. The integration specifically supports: - ?? Column-Level Lineage: Track data from source to Power BI dashboards using column-level lineage in the Datafold Data Explorer. - ?? Impact Analysis: See exactly which Power BI assets will be impacted by code changes, directly in the Datafold CI comment. With the Power BI integration, you can proactively identify potential data quality issues before they affect one of your business's most important tools. Learn more about it below ?? https://lnkd.in/gAbC2d73

Why do data teams spend months or years on manual data migrations? Because they didn’t have a better option. Until now??? https://lnkd.in/grRFYx7D Meet the Datafold Migration Agent (DMA) — the first AI-powered, full-cycle migration solution that automatically translates and validates parity of source and target database objects. ? Translate to and from any SQL dialect ? Translate between orchestration frameworks (Airflow, dbt, stored procedures) ? Translate GUI-based frameworks (Informatica, Microstrategy, Matillion) ? Continuously improve code translations until data parity is reached The Datafold Migration Agent is the fastest path from planning to stakeholder approval. If you have a migration planned, let’s talk about how we can accelerate your timeline by 5-10x.?

?? New Podcast Episode Alert: Tackling Data Migrations with Datafold! ?? This week, Gleb Mezhanskiy chats with Tobias Macey about the highs, lows, and unexpected challenges of data migrations. From scaling data platforms at companies like Autodesk and Lyft, Gleb brings his personal knowledge of the past, present, and future of data migrations. ??? Key Insights: - Overcoming common migration pitfalls, like (dreaded) technical debt - The value of data parity – making old & new systems match up perfectly - How Datafold is leveraging AI and LLMs to make data migrations faster, more reliable, and way less painful Whether you’re wrestling with a data migration right now or simply interested in the latest AI applications, this episode is packed with takeaways. Tune in! https://lnkd.in/gq8PbW9A

At Coalesce 2024, Gleb Mezhanskiy dropped some serious knowledge on automating the conversion of legacy transformation code (yep, those old stored procedures) into shiny new #dbt models. ??? If you're thinking of modernizing your data stack, this talk is a must-watch. Catch the replay below and learn how to future-proof your data infrastructure without sacrificing data quality or speed. https://lnkd.in/gg6Z_aki

So you're thinking of getting into data diffing? You might want to read this first. In our newest blog post, Insung Ko and Elliot G. break down data diffing best practices in Datafold. Learn how data engineers are using Datafold's: - Sampling - Filtering - Monitors as code - Efficient hashing algorithm to manage data quality at scale. https://lnkd.in/gskhZcmQ

ICYMI — The Datafold docs site got a major upgrade! ?? We’ve revamped our docs site to make it easier than ever to find everything you need for seamless data quality management across your entire stack. Whether you're looking for setup guides, troubleshooting help, or best practices, our upgraded docs have you covered. https://lnkd.in/gbduUkw5

Data monitoring isn’t just about checking boxes—it’s about choosing the right monitors for each stage of the data lifecycle. In our latest blog from Elliot G. and Nick Carchedi, we dive into three core scenarios where data monitors help prevent costly errors: 1?? Validating data during a migration: Ensure data parity between legacy systems and new warehouses with Data Diff Monitors. 2?? Catching schema changes early: Use Schema Change and Metric Monitors to prevent upstream changes from disrupting downstream processes. 3?? Maintaining production data quality: Keep your production data reliable with Data Test and Metric Monitors. Discover how a proactive approach to monitoring helps teams catch issues early, build data trust, and save time https://lnkd.in/g6kdF5xV