37 docs tagged with "Semantic Layer"

Learn more about MetricFlow and its key concepts

Discover the diverse range of partners that seamlessly integrate with the powerful dbt Semantic Layer, allowing you to query and unlock valuable insights from your data ecosystem.

Learn about MetricFlow and build your metrics with semantic models

Cache common queries to speed up performance and reduce query computation.

Learn how to query and consume metrics from your deployed dbt Semantic Layer using various tools and APIs.

Use Conversion metrics to measure conversion events.

Metrics can be defined in the same or separate YAML files from semantic models within the same dbt project repo.

Use Cumulative metrics to aggregate a measure over a given window.

Learn how the dbt Semantic Layer enables data teams to centrally define and query metrics.

dbt Semantic Layer product architecture and related questions.

These cookbook recipes are a collection of scenario-based, real-world examples for building with the dbt Semantic Layer.

Read the FAQs to learn more about the dbt Semantic Layer, how it works, how to build metrics, integrations, and more.

Deploy the dbt Semantic Layer in dbt Cloud by running a job to materialize your metrics.

Derived metrics is defined as an expression of other metrics..

Dimensions determine the level of aggregation for a metric, and are non-aggregatable expressions.

Entities are real-world concepts that correspond to key parts of your business, such as customers, transactions, and ad campaigns.

Integrate with Google Sheets to query your metrics in a spreadsheet.

Integrate and use the GraphQL API to query your metrics.

Learn about partner integration guidelines, roadmap, and connectivity.

Integrate and use the JDBC API to query your metrics.

Joins allow you to combine data from different tables and create new metrics

Learn how to migrate from the legacy dbt Semantic Layer to the latest one.

Measures are aggregations performed on columns in your model.

Query metrics and metadata in your dbt project with the MetricFlow commands.

MetricFlow expects a default time spine table called metricflow_time_spine

Integrate with Excel to query your metrics in a spreadsheet.

Learn how to use the dbt Semantic Layer Python SDK library to interact with the dbt Semantic Layer.

Use this guide to build and define metrics, set up the dbt Cloud Semantic Layer, and query them using Google Sheets.

Use ratio metrics to create a ratio out of two measures.

Saved queries are a way to save commonly used queries in MetricFlow. They can be used to save time and avoid writing the same query over and over again.

Integrate and query metrics and dimensions in downstream tools using the Semantic Layer APIs

Learn about the semantic manifest.json file and how you can use artifacts to gain insights about your dbt Semantic Layer.

Semantic models are yml abstractions on top of a dbt mode, connected via joining keys as edges

Seamlessly set up the dbt Semantic Layer in dbt Cloud using intuitive navigation.

Use simple metrics to directly reference a single measure.

Use Tableau worksheets to query the dbt Semantic Layer and produce dashboards with trusted date.

The Semantic Layer, powered by MetricFlow, has three types of built-in validations, including Parsing Validation, Semantic Validation, and Data Warehouse validation, which are performed in a sequential and blocking manner.