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Overview of DBT Semantic Layer

Sep 26, 2024

Lecture Notes: DBT Semantic Layer

Introduction

  • Hosts: Cameron Afsal (Product Manager) and Callum McCann (Developer Experience Advocate)
  • Objective: Understanding DBT Semantic Layer and its applications
  • Audience: People familiar with or curious about the semantic layer
  • Interaction: Use Slack channel for questions and feedback

Background

  • Cameron and Callum's journey started at another company where metrics logic was complex and embedded in SQL dashboards
  • Problem: Difficulty in understanding metrics logic and managing data preparation
  • Solution: Implementing DBT led to successful customer experiences

Opportunities with DBT Semantic Layer

  1. Centralized Metric Definition: Define metrics once, reducing redundancy and errors
  2. Knowledge Loop Contribution: Centralize information for better data governance

Extending DBT Value

  • Problem: Inconsistent metric calculations across teams and tools
  • Solution: Using a semantic layer for consistent metric definitions and calculations
  • Benefits:
    • Unified metric views across organizations
    • Better governance and faster access to data

Product Architecture

  1. DBT Metrics: Central place for metric definitions
  2. Proxy Server: Compiles SQL queries for execution
  3. Metadata API: Allows metric and model metadata import to tools

Integration with Tools

  • Ubiquity: Integration across various data tools like ThoughtSpot, Hex, Mode, etc.
  • Utilities: Meaningful integrations to improve workflows

User Flow Example

  1. Data Engineer: Loads metrics from CDP data sources
  2. Analytics Engineer: Defines metrics in DBT Cloud
  3. Business User: Uses data catalog to choose metrics
  4. Data Analyst: Deep dives using analytical tools
  5. Data Scientist: Monitors metrics for anomalies

Why It Matters

  • Consistency: Solves inconsistency issues in metrics reporting
  • Efficiency: Frees up analytics engineers for high-value work

Demonstrations

  1. Defining Metrics: In DBT Cloud using YAML files
  2. Discovering Metrics: Using Atlan for metric metadata and lineage
  3. Reporting: Creating reports in Mode
  4. Analyzing: Advanced analysis in Hex

Product Roadmap

  • Metric Modeling: Introduce entities, relationships, and hierarchies
  • DBT Cloud Improvements: Better performance and availability
  • Ecosystem Growth: More integrations and support for additional data platforms

Getting Started

  • Requirements: DBT Cloud account, Snowflake platform, DBT Core 1.2+
  • Slack Channels: For ongoing support and community engagement

Q&A Highlights

  • Open Source: DBT server component to be source available
  • Integration Concerns: Discussion on LookML and caching data
  • Community Input: Encouraged for feature development and roadmap planning

Closing

  • Next Steps: Trying out the semantic layer, engaging in community discussions, and providing feedback
  • Acknowledgements: Thanks to the engineering and documentation teams

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