Accelerate Migration Of Your Data Warehouse with Datafold's AI Powered Migration Agent
Oct 27, 2024
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Gleb Mezhanskiy, CEO and co-founder of DataFold, shares his extensive experience in data management from his time at Autodesk and Lyft. He dives into the complexities of data migrations, detailing challenges like technical debt and the need for effective parity between systems. Gleb reveals how DataFold leverages AI to automate data migration processes, significantly reducing time and effort. He also discusses the importance of monitoring data integrity in real-time and offers insights into choosing the right models for secure data handling.
Automatic monitoring tools like DataFold are crucial for identifying real-time data discrepancies, ensuring integrity and preventing costly mistakes during migrations.
AI-driven data migration agents significantly enhance efficiency by automating processes, enabling quicker transitions from legacy systems to advanced data environments.
Deep dives
Real-Time Data Monitoring for Integrity
Automatic monitoring of data through tools like DataFold is essential for identifying discrepancies and anomalies in real time. These monitors track cross-database data diffs, schema changes, and custom data tests, enabling teams to catch issues before they escalate. By maintaining data integrity, organizations can prevent costly mistakes that often arise from data mismanagement. This proactive approach allows for smoother operations across the entire data stack, enhancing overall efficiency.
Lessons Learned from Data Migration Challenges
Data migrations are complex and often fraught with challenges, as highlighted by the speaker's experience migrating Lyft's data warehouse to a data lake. The migration faced significant delays partly due to unanticipated technical debt and the need to achieve data parity between systems. Learning from these challenges emphasizes the importance of a structured approach that prioritizes maintaining consistency in data outputs while transitioning to new frameworks. Avoiding substantial changes in models or definitions during migrations can significantly reduce complications and speed up the process.
Motivations Behind Data Migration
Organizations typically decide to migrate their data infrastructure due to limitations in scalability, performance, and cost associated with legacy systems. Common motivations include handling increased data volume, improving return on investment, and fostering better interoperability with modern data tools. The necessity for data teams to provide timely insights drives the need for more advanced analytical capabilities, which outdated systems cannot support. Hence, successful migrations not only address current pain points but also pave the way for future growth and innovation.
The Impact of AI on Migration Processes
The introduction of a data migration agent that utilizes AI significantly streamlines the migration process, reducing the time and manual effort required for data transitions. This agent automates the translation and reconciliation of code, which traditionally consumes the majority of migration time. By enabling data teams to focus on higher-level strategic tasks rather than low-level operations, AI fosters greater efficiency and productivity. Ultimately, the goal is to see migration projects completed in weeks instead of years, allowing organizations to quickly leverage the benefits of their new data environments.
Summary Gleb Mezhanskiy, CEO and co-founder of DataFold, joins Tobias Macey to discuss the challenges and innovations in data migrations. Gleb shares his experiences building and scaling data platforms at companies like Autodesk and Lyft, and how these experiences inspired the creation of DataFold to address data quality issues across teams. He outlines the complexities of data migrations, including common pitfalls such as technical debt and the importance of achieving parity between old and new systems. Gleb also discusses DataFold's innovative use of AI and large language models (LLMs) to automate translation and reconciliation processes in data migrations, reducing time and effort required for migrations. Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
Imagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!
Your host is Tobias Macey and today I'm welcoming back Gleb Mezhanskiy to talk about Datafold's experience bringing AI to bear on the problem of migrating your data stack
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what the Data Migration Agent is and the story behind it?
What is the core problem that you are targeting with the agent?
What are the biggest time sinks in the process of database and tooling migration that teams run into?
Can you describe the architecture of your agent?
What was your selection and evaluation process for the LLM that you are using?
What were some of the main unknowns that you had to discover going into the project?
What are some of the evolutions in the ecosystem that occurred either during the development process or since your initial launch that have caused you to second-guess elements of the design?
In terms of SQL translation there are libraries such as SQLGlot and the work being done with SDF that aim to address that through AST parsing and subsequent dialect generation. What are the ways that approach is insufficient in the context of a platform migration?
How does the approach you are taking with the combination of data-diffing and automated translation help build confidence in the migration target?
What are the most interesting, innovative, or unexpected ways that you have seen the Data Migration Agent used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI powered migration assistant?
When is the data migration agent the wrong choice?
What do you have planned for the future of applications of AI at Datafold?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
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