In this episode of The Dashboard Effect Podcast, Brick and Landon break down the differences between schema on read and schema on write. They discuss when to use each approach, the pros and cons, and real-world applications for businesses of different sizes. If you're navigating the complexities of data lakes, data warehouses, or lakehouses, this episode will give you clarity.

Tune in to learn how these data strategies can impact your reporting, analytics, and overall business performance.

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Looking for smarter, faster ways to make data-driven decisions? At Blue Margin, we specialize in making data accessible and actionable for mid-market companies. Visit bluemargin.com to learn how we can help you unlock the full potential of your data.

In this episode of The Dashboard Effect Podcast, host Brick Thompson and Blue Margin’s Director of Solutions, Landon Ochs, dive into the nuances of semantic models and the bronze, silver, and gold layers within a data lake house. They explain the roles of each layer, how data is transformed at each stage, and why collaboration with business users is essential for accurate and actionable BI reporting.

Through this episode, you'll learn why different layers serve different purposes—from storing raw data to pre-joining tables for efficiency to creating finalized data ready for reporting. They also discuss how the semantic model is designed to simplify complex data relationships, making reporting easier for non-technical users.

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Blue Margin increases enterprise value for PE-backed, mid-market companies by serving as their fractional data team. We advise on, build, and manage data platforms. Our strategy, proven with over 300 companies to-date, expands multiples through data transformation, as presented in our book, The Dashboard Effect.

For a free, downloadable copy click https://bit.ly/3RmBGWQ, or buy a hardcopy or Kindle version on Amazon (https://amzn.to/3UTvTLm).

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In this episode of The Dashboard Effect, Brick chats with Landon, Director of Solutions at Blue Margin, about a surprising challenge faced while extracting data from NetSuite. Despite NetSuite’s well-developed API, handling massive datasets proved problematic. Landon shares how they solved the issue using an unexpected solution—the ODBC connector.

They discuss the limitations of the API, how ODBC outperformed it for large data sets, and the unconventional yet effective workaround using a virtual machine. If you’re working with NetSuite or other cloud-based systems and experiencing similar challenges, you’ll want to hear this episode.

Key Takeaways:

• Why NetSuite’s API struggled with millions of records
• How ODBC became the unexpected hero for pulling large data sets
• The importance of getting creative with data extraction

Listen in for tips and insights that can help with your own data pipeline challenges!