Data Exploration For Business Users Powered By Analytics Engineering With Lightdash
Data Engineering Podcast
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What's the Difference Between the Semantic Layer and the Metric Layer?
Light dash is using a new data base called d b t. The semantic layer, or what's being termed now the metric layer,. There have been projects that are coming out focusing on providing that sort of metric business. And i'm wondering what your thoughts are on the approach that light dash is taking to provide some of that metric mededata.
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Summary
The market for business intelligence has been going through an evolutionary shift in recent years. One of the driving forces for that change has been the rise of analytics engineering powered by dbt. Lightdash has fully embraced that shift by building an entire open source business intelligence framework that is powered by dbt models. In this episode Oliver Laslett describes why dashboards aren’t sufficient for business analytics, how Lightdash promotes the work that you are already doing in your data warehouse modeling with dbt, and how they are focusing on bridging the divide between data teams and business teams and the requirements that they have for data workflows.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Oliver Laslett about Lightdash, an open source business intelligence system powered by your dbt models
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Lightdash is and the story behind it?
- What are the main goals of the project?
- Who are the target users, and how has that profile informed your feature priorities?
- Business intelligence is a market that has gone through several generational shifts, with products targeting numerous personas and purposes. What are the capabilities that make Lightdash stand out from the other options?
- Can you describe how Lightdash is architected?
- How have the design and goals of the system changed or evolved since you first began working on it?
- What have been the most challenging engineering problems that you have dealt with?
- How does the approach that you are taking with Lightdash compare to systems such as Transform and Metriql that aim to provide a dedicated metrics layer?
- Can you describe the workflow for someone building an analysis in Lightdash?
- What are the points of collaboration around Lightdash for different roles in the organization?
- What are the methods that you use to expose information about the state of the underlying dbt models to the end users?
- How do they use that information in their exploration and decision making?
- What was your motivation for releasing Lightdash as open source?
- How are you handling the governance and long-term viability of the project?
- What are the most interesting, innovative, or unexpected ways that you have seen Lightdash used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Lightdash?
- When is Lightdash the wrong choice?
- What do you have planned for the future of Lightdash?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Lightdash
- Looker
- PowerBI
- Redash
- Metabase
- dbt
- Superset
- Streamlit
- Kubernetes
- JDBC
- SQLAlchemy
- SQLPad
- Singer
- Airbyte
- Meltano
- Transform
- Metriql
- Cube.js
- OpenLineage
- dbt Packages
- Rudderstack
- PostHog
- Firebolt
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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