Have you ever hit “merge” on a dbt pull request and just hope it doesn’t break everything downstream? Yeah, us too. That slight internal panic—“please don’t wreck any dashboards”—is all too familiar. Now there’s a much better way to avoid dashboard chaos: Preview Projects from GitHub Branches. This capability fundamentally changes how you can develop […]
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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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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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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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
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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Lightdash and Looker are two popular business intelligence (BI) platforms designed to empower analytics teams and small to medium-sized businesses to extract valuable insights from their data. Both platforms emphasize self-service capabilities, allowing non-technical users to build reports, create dashboards, and schedule the sending of CSVs, charts, and tables. This self-service approach is particularly beneficial
