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
Analytics teams often struggle to keep everyone aligned. Insights get stuck in dashboards, emails go unanswered, and decisions stall while people wait for data. Lightdash, an open-source analytics tool built for the modern data stack, changes that. Its Slack integration brings insights directly into your team’s conversations. Key Features of the Lightdash Slack Integration 1.
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Maintaining a clean and standardized semantic layer is essential in modern analytics. Lightdash’s dbt Write-Back streamlines this process by enabling analysts to develop models, metrics, and dimensions directly within Lightdash and write them back to their dbt project. With Git-based source control, teams can ensure their business logic remains consistent, reusable, and version-controlled. Achieving a
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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
Self-serve analytics tools empower non-technical users by giving them direct access to data, enabling operational directors, sales leaders, and others to assess performance and make informed decisions. Traditional centralized data teams often face bottlenecks due to numerous analytics requests for reports and automation. To address this, modern data teams are adopting a decentralized model, where
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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
Not all fantasy football leagues are equal. Scoring system differences across ESPN, Yahoo, and Sleeper impact player rankings and strategy. This post breaks down key scoring variations and analyzes how they affect player performance. I explored these differences in the dbt Fantasy Football Data Modeling Challenge, hosted by Paradime.io and Lightdash. This submission placed second
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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
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Data silos are the silent killers of collaboration. They slow down decisions, frustrate teams, and leave stakeholders in the dark. Marketing can’t find campaign performance data, finance is stuck chasing budget figures, and product managers are left guessing about user engagement. Lightdash’s Google Sheets data integration flips the script. It connects your teams with a
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