CSVs Will Never Die And OneSchema Is Counting On It
Jan 13, 2025
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Andrew Luo, CEO of OneSchema, shares his expertise in data engineering and CRM migration, focusing on the enduring relevance of CSVs. He discusses the common challenges of CSV data, such as inconsistency and lack of standards, and explains how OneSchema uses AI for improved type validation and parsing. Andrew highlights OneSchema's potential to streamline data imports and boost efficiency, particularly for non-technical users. He also reveals plans for future innovations, including industry-specific transformation packs to enhance data management further.
CSV formats remain prevalent in organizations due to their simplicity and perceived security in handling sensitive data types.
OneSchema addresses the common challenges of working with CSVs by automating data imports and enhancing validation processes for improved accuracy.
The future of OneSchema includes expanding support for multiple data formats and incorporating AI-driven transformations to streamline diverse data management needs.
Deep dives
Challenges of Data Migrations
Data migrations are often lengthy processes that consume significant resources and lower team morale. Traditional methods can take months or even years, making teams eager for more efficient solutions. The introduction of automated solutions, like Datafold's AI-powered migration agent, significantly accelerates the process, completing migrations up to ten times faster than manual methods. This new approach not only streamlines the migration workflow but also provides guarantees on timelines, providing users with greater confidence.
The Ubiquity and Utility of CSVs
Despite advancements in data interchange formats, CSVs remain the predominant choice for many organizations. Their simplicity allows widespread accessibility, making spreadsheets a common tool for data exchange across various industries. Their continued usage is partly due to security concerns, as organizations often find CSVs less risky compared to frequently changing API keys and formats. This persistence highlights why businesses frequently rely on CSVs for sensitive data types, including governmental and healthcare information.
Limitations of CSV Data Management
CSVs present notable challenges, including inconsistent data types and difficult relational data representation, which complicate processing and analysis. Their lack of a concrete structure can lead to errors in data interpretation, particularly when it comes to understanding schema alignment. Furthermore, universally accepted standards for handling CSVs are lacking, leading to discrepancies in practices among different organizations. This chaotic landscape calls for more efficient validation and transformation tools to ensure data integrity during processing, something that OneSchema is addressing.
OneSchema: Automating CSV Imports
OneSchema automates the process of importing, validating, and transforming CSV data into standardized formats, bridging the gap between technical and non-technical users. By leveraging features like AI-powered type validation and transformation suggestions, OneSchema enhances efficiency for data teams engaged in data migrations and integrations. This automated solution not only helps organizations save time but also empowers business users, allowing them to manage their data workflows efficiently. Their approach has demonstrated significant improvements in onboarding times as companies scale and take on more clients.
The Future of Data Interchange and Integration
As the landscape of data management evolves, OneSchema is broadening its support beyond CSVs to incorporate formats like JSON and XML, making it adaptable to various data interchange scenarios. Looking ahead, they plan to explore EDI standards while also enhancing integration capabilities with existing data ecosystems. The focus on intelligent transformations will make it easier for businesses to manage their data efficiently. In a world increasingly reliant on automation, these advancements promise to address existing gaps in data management technology, paving the way for more streamlined processes.
Summary In this episode of the Data Engineering Podcast Andrew Luo, CEO of OneSchema, talks about handling CSV data in business operations. Andrew shares his background in data engineering and CRM migration, which led to the creation of OneSchema, a platform designed to automate CSV imports and improve data validation processes. He discusses the challenges of working with CSVs, including inconsistent type representation, lack of schema information, and technical complexities, and explains how OneSchema addresses these issues using multiple CSV parsers and AI for data type inference and validation. Andrew highlights the business case for OneSchema, emphasizing efficiency gains for companies dealing with large volumes of CSV data, and shares plans to expand support for other data formats and integrate AI-driven transformation packs for specific industries.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.
Your host is Tobias Macey and today I'm interviewing Andrew Luo about how OneSchema addresses the headaches of dealing with CSV data for your business
Interview
Introduction
How did you get involved in the area of data management?
Despite the years of evolution and improvement in data storage and interchange formats, CSVs are just as prevalent as ever. What are your opinions/theories on why they are so ubiquitous?
What are some of the major sources of CSV data for teams that rely on them for business and analytical processes?
The most obvious challenge with CSVs is their lack of type information, but they are notorious for having numerous other problems. What are some of the other major challenges involved with using CSVs for data interchange/ingestion?
Can you describe what you are building at OneSchema and the story behind it?
What are the core problems that you are solving, and for whom?
Can you describe how you have architected your platform to be able to manage the variety, volume, and multi-tenancy of data that you process?
How have the design and goals of the product changed since you first started working on it?
What are some of the major performance issues that you have encountered while dealing with CSV data at scale?
What are some of the most surprising things that you have learned about CSVs in the process of building OneSchema?
What are the most interesting, innovative, or unexpected ways that you have seen OneSchema used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on OneSchema?
When is OneSchema the wrong choice?
What do you have planned for the future of OneSchema?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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