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.
Datafold helps you deal with data quality in your pull request. It provides automated regression testing throughout your schema and pipelines so you can address quality issues before they affect production. No more shipping and praying, you can now know exactly what will change in your database ahead of time.
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Hightouch is the leading Reverse ETL platform. Your data warehouse is your source of truth for customer data. Hightouch syncs this data to the tools that your business teams rely on. Hightouch has a catalog of flexible destinations including Salesforce, HubSpot, Zendesk, NetSuite, and ad platforms like Facebook or Google. Hightouch is built for data engineers and is a natural extension to the modern data stack with out-of-the-box integrations with your favorite tools like dbt, Fivetran, Airflow, Slack, PagerDuty, and DataDog.
It’s simple — connect your data warehouse, paste a SQL query, and use our visual mapper to specify how data should appear in downstream tools. No scripts, just SQL. Get started for free at dataengineeringpodcast.com/hightouch
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold.
- 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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- dbt Packages