explaingit

lightdash/lightdash

5,798TypeScriptAudience · dataComplexity · 4/5Setup · hard

TLDR

An open-source business intelligence tool built on dbt that lets teams explore data, build charts, and share dashboards from a browser without writing SQL.

Mindmap

mindmap
  root((lightdash))
    What it does
      BI dashboards
      Data exploration
      dbt integration
    Features
      No SQL needed
      Scheduled reports
      Drill down view
    Tech
      TypeScript
      dbt SQL
      Kubernetes
    Audience
      Data analysts
      Business users
      Data engineers
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Code map

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Things people build with this

USE CASE 1

Build a self-service dashboard so non-technical teammates can filter and explore metrics without SQL or analyst help.

USE CASE 2

Connect Lightdash to an existing dbt project to expose metric definitions to business users immediately.

USE CASE 3

Schedule weekly email or Slack delivery of a sales dashboard to stakeholders automatically.

USE CASE 4

Preview a new dbt metric definition in Lightdash before merging to production via a CI/CD pipeline.

Tech stack

TypeScriptdbtSQLKubernetesHelm

Getting it running

Difficulty · hard Time to first run · 1h+

Requires an existing dbt project and a supported data warehouse, self-hosted deployment needs Kubernetes or a managed cloud host.

In plain English

Lightdash is an open-source business intelligence tool, meaning it is software for exploring and visualizing data in a company's database and sharing those insights as charts and dashboards. The project describes itself as an open-source alternative to Looker, a well-known commercial BI product. It is built to work with dbt, a popular tool that data teams use to transform and organize data in warehouses. The central idea is that metrics and dimensions (the definitions of what you are measuring and how you slice the data) live inside your dbt project rather than inside the BI tool. This keeps business logic in one place. When a data analyst updates a metric definition in dbt, Lightdash picks up that change automatically rather than requiring a separate update in the dashboard software. For end users, Lightdash provides a browser-based interface for asking data questions without writing SQL. You pick a model, choose dimensions and metrics from a list, apply filters, and the tool runs the query and renders a chart. You can save charts, combine them into dashboards, schedule email or Slack deliveries, and share results via URL. There is also a drill-down feature for viewing the underlying rows behind a number, and a lineage view that shows how a data model relates to others upstream and downstream. For developers and data engineers, the tool offers preview environments for testing changes before publishing, automated content validation through CI/CD pipelines, and version history with rollback for charts. The project can be run locally with a provided install script, deployed to a cloud hosting service, or self-hosted using Kubernetes and provided Helm charts. A hosted cloud version is also available for teams that do not want to manage their own infrastructure. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
Connect Lightdash to my dbt project on BigQuery and expose the orders model as a self-service dashboard for the sales team, what are the steps?
Prompt 2
Set up scheduled Slack delivery of a Lightdash dashboard to my sales channel every Monday morning.
Prompt 3
Use Lightdash preview environments to test a new dbt metric before publishing it to production, walk me through the workflow.
Prompt 4
Build a drill-down chart in Lightdash that lets a user click a bar in a chart to see the underlying rows that make up that number.
Prompt 5
Deploy Lightdash on Kubernetes using the provided Helm charts, which values.yaml fields do I need to configure for a minimal production setup?
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