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Exploring the DBT Semantic Layer

Sep 26, 2024

Hands-on with the DBT Semantic Layer

Introduction

  • Presenters: Cameron Afzal (PM for the Semantic Layer) & Cal McCann (Developer Experience Advocate)
  • Purpose: Introduction to the DBT Semantic Layer, its use, and future directions.
  • Audience: Those familiar with DBT and newcomers curious about the Semantic Layer.

Background and Motivation

  • Origins: Came from different paths; Cameron through product management, Cal through analytics engineering.
  • Problem: Traditional metrics logic was hard to manage, often embedded in SQL behind dashboards.
  • Solution: Adopted DBT to improve metrics understanding and customer success.
  • Integration: Built product around DBT, leading to joining DBT Labs.

Opportunities with the DBT Semantic Layer

  1. Centralized Metric Definitions:
    • Define metrics centrally to avoid redundant custom queries.
  2. Knowledge Loop Contribution:
    • Centralize information to enhance data preparation and analytics.

DBT Semantic Layer Value

  • Consistency in Metrics:
    • Ensures uniform calculation methods and definitions across tools and teams.
  • Integration with Tools:
    • Works with various BI and data tools to maintain a consistent source of truth.

Semantic Layer Components

  1. DBT Metrics:
    • Define metrics in DBT projects.
  2. Proxy Server:
    • Compiles DBT SQL queries, executes them, and integrates with tools.
  3. Metadata API:
    • Provides metric definitions and model metadata to tools.

Workflow Extensions

  • Ubiquity:
    • Works seamlessly with existing tools to extend DBT workflows.
  • Utilities:
    • Equip developers and users with APIs and meaningful integrations.

Example User Flow

  • From Data Engineer to Business User:
    • Load, define, discover, and analyze metrics using integrated tools like Atlan, Mode, and Hex.

Importance for the Community

  • Consistency and Trust:
    • Ensures consistent metrics usage, reducing discrepancies and rebuilding trust.
  • Empowers Analytics Engineers:
    • Focus on high-leverage tasks rather than resolving metric inconsistencies.

Partner Integrations

  • Current Partners:
    • Dot, Spot, Hex, Mode, DeepNote, Atlan, Houseware, Lightdash.
  • Future Expansion:
    • Encourage engagement with vendors for further integrations.

Product Roadmap

  1. Modeling Efficiency:
    • Enhance metric and model modeling.
  2. DBT Cloud Experience:
    • Improve reliability and access.
  3. Ecosystem Development:
    • Facilitate integration development.

Future Semantic Layer Features

  • Entities and Relationships:
    • Higher-level model abstractions.
  • Improved Developer Experience:
    • Versioning, testing, and development environment support.

Availability and Setup

  • Current Support:
    • Available in DBT Cloud, requires Snowflake platform.
  • Instructions for Use:
    • Setup guidance available in documentation.

Q&A Highlights

  • Open Source Availability:
    • DBT server will be source available soon.
  • Commercial Model:
    • Currently exploring feedback before finalizing pricing.
  • Performance and Execution:
    • Proxy compiles SQL, sends to warehouse, and returns results.

Conclusion

  • Participation Encouraged:
    • Users urged to try the semantic layer, provide feedback, and engage with community.

This summary captures the key points and details from the lecture on the DBT Semantic Layer, providing an overview of its purpose, functionality, integration, and future roadmap.

Full transcript