With the release of dbt Fusion, dbt Labs has introduced a new engine that promises significant performance improvements and advanced capabilities over the familiar dbt Core. Fusion is more than just a technical upgrade: it represents a fundamental shift in how dbt is developed, distributed, and licensed. In this post, we break down the pros […]
With the recent release of dbt 1.1, users may start to see warnings like this: Warning: while parsing model config: Ignore unexpected key “meta” dbt 1.1 introduces a subtle but important shift in how configuration properties are structured for sources, models, and other resources. What’s Changed? Historically, dbt allowed you to set properties like freshness,
Avoiding Config Warnings while Updating Your dbt Project to v1.1 Read More »
For growth-stage companies, data-driven decision-making is a critical pillar of success. As teams scale, they need fast, reliable, and flexible access to insights across the business. But many organizations find themselves trapped by the very tools they rely on to deliver those insights. This is the story of one such company and how they broke
A well-defined semantic layer is the foundation of effective business intelligence (BI). By ensuring consistent data definitions and organized metrics, you empower teams to extract insights with clarity and precision. While ‘semantic layer’ was a buzzword a few years back, this blog explores why a clear semantic layer is still critical and how to implement
Why a Clear Semantic Layer is Essential for Analytics Read More »
Understanding Embedded Analytics Embedded analytics is the integration of analytical capabilities such as dashboards, visualizations, and reports directly into an application’s user interface. Instead of requiring users to navigate to a separate business intelligence (BI) tool, embedded analytics brings insights directly to where users are working, leading to more seamless decision-making and improved user experience.
What is Embedded Analytics? Staring from the top, embedded analytics is the integration of data visualization and business intelligence (BI) capabilities directly within applications, websites, or portals. Instead of requiring users to switch to a separate BI tool, embedded analytics brings insights directly into the workflow, making data-driven decision-making more seamless and efficient. Why Use
The data science landscape is constantly evolving, and Google has contributed to this evolution with its latest offering, Google Colab’s Data Science Agent. This tool represents a significant advancement in the way we approach data analysis, blending cutting-edge large language model (LLM) technology with the user-friendly accessibility of jupyter notebooks. We took it for a
Open source software (OSS) is everywhere, but a lot of people still have questions. What’s the big deal? Is it actually free? How do companies make money from it? And most importantly should you be using it? This guide breaks it all down so you can make sense of the open-source ecosystem without drowning in
Recently, I participated in the dbt Fantasy Football Data Modeling Challenge, hosted by Paradime & Lightdash. Over 300 SQL and dbt experts tackled real NFL and fantasy football data. My approach, which placed 2nd overall, focused on building a self-service data mart for dynamic exploration of scoring trends and player performance. I used dbt for
Building a Data Mart with dbt, Lightdash, and Paradime Read More »
To stay ahead of the curve, startups need to be agile, adaptable, and responsive to market changes. Self-service business intelligence (BI) tools, such as Looker and Lightdash, are essential to unlocking the power of data and driving rapid, informed decision-making. Here are five reasons why self-service BI tools are essential for agile, data-driven startups: 1.
