Author name: John

Lightdash's dbt Write-Back

Keeping Your Semantic Layer Clean with Lightdash’s dbt Write-Back

Lightdash /

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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Exploring Google Colab’s Data Science Agent

Business Intelligence /

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

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Lightdash spotlight

Simplify Metrics Exploration with Lightdash Spotlight

Lightdash /

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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platform scoring and player rankings in fantasy football

Platform Scoring and Player Rankings in Fantasy Football

Analytics /

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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uniting teams

Uniting Teams with Lightdash & Google Sheets Data Integration

Lightdash /

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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Data documentation in Lightdash

Data Documentation in Lightdash

Lightdash /

Organizing a business intelligence stack goes beyond pipelines and dashboards. It requires clear, accessible documentation. Tools like Lightdash, integrated with DBT, make this possible by combining robust ETL processes with a documented semantic layer. This ensures your data is understandable and actionable. In this post, we explore the concept of a semantic layer, its role

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Self-Hosting Lightdash Guide

Self-Hosting Lightdash Guide: Step-by-Step Setup

Lightdash /

Lightdash is an open-source analytics platform licensed under MIT, designed for teams looking to provide self-service reporting to their organization. Positioning itself as an open-source alternative to Looker, Lightdash allows users to explore, visualize, and report on data without advanced technical knowledge. It’s especially popular for modern data environments due to its direct database connectivity

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