From 428e15ae56e670e09f0dc4cf9c7ba10c42fec909 Mon Sep 17 00:00:00 2001 From: Doug Beatty <44704949+dbeatty10@users.noreply.github.com> Date: Fri, 6 Sep 2024 10:13:49 -0600 Subject: [PATCH 01/39] Package dependencies may be included in either `packages.yml`or `dependencies.yml` --- website/snippets/_packages_or_dependencies.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/snippets/_packages_or_dependencies.md b/website/snippets/_packages_or_dependencies.md index 8d21768b0bf..87a6842c271 100644 --- a/website/snippets/_packages_or_dependencies.md +++ b/website/snippets/_packages_or_dependencies.md @@ -7,7 +7,7 @@ Starting from dbt v1.6, we added a new configuration file called `dependencies.y If your dbt project doesn't require the use of Jinja within the package specifications, you can simply rename your existing `packages.yml` to `dependencies.yml`. However, something to note is if your project's package specifications use Jinja, particularly for scenarios like adding an environment variable or a [Git token method](/docs/build/packages#git-token-method) in a private Git package specification, you should continue using the `packages.yml` file name. -Examine the following tabs to understand the differences and determine when should use to `dependencies.yml` or `packages.yml`. +Examine the following tabs to understand the differences and determine when should use to `dependencies.yml` or `packages.yml` (or both at the same time). From ab2202f43cc5bb27231b93b321716ac5a29d305b Mon Sep 17 00:00:00 2001 From: mirnawong1 Date: Tue, 17 Sep 2024 13:42:23 +0100 Subject: [PATCH 02/39] update mf commands --- .../docs/docs/build/metricflow-commands.md | 38 ++++--------------- 1 file changed, 7 insertions(+), 31 deletions(-) diff --git a/website/docs/docs/build/metricflow-commands.md b/website/docs/docs/build/metricflow-commands.md index 1f50e501261..e389a3c8a91 100644 --- a/website/docs/docs/build/metricflow-commands.md +++ b/website/docs/docs/build/metricflow-commands.md @@ -18,34 +18,14 @@ MetricFlow is a dbt package that allows you to define and query metrics in your Using MetricFlow with dbt Cloud means you won't need to manage versioning — your dbt Cloud account will automatically manage the versioning. -**dbt Cloud jobs** — MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs. +dbt Cloud jobs support the `dbt sl validate` command to [automatically test your semantic nodes](/docs/deploy/ci-jobs#semantic-validations-in-ci). You can also add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs. - - - - -- MetricFlow [commands](#metricflow-commands) are embedded in the dbt Cloud CLI. This means you can immediately run them once you install the dbt Cloud CLI and don't need to install MetricFlow separately. -- You don't need to manage versioning — your dbt Cloud account will automatically manage the versioning for you. - - - - - -:::info -You can create metrics using MetricFlow in the dbt Cloud IDE and run the [dbt sl validate](/docs/build/validation#validations-command) command. Support for running more MetricFlow commands in the IDE will be available soon. -::: - - - - - -:::tip Use dbt Cloud CLI for semantic layer development +#### dbt Cloud +In dbt Cloud, run MetricFlow commands directly in the dbt Cloud IDE or in the dbt Cloud CLI. -You can use the dbt Cloud CLI for the experience in defining and querying metrics in your dbt project. - -A benefit to using the dbt Cloud is that you won't need to manage versioning — your dbt Cloud account will automatically manage the versioning. -::: +For dbt Cloud CLI users, MetricFlow commands are embedded in the dbt Cloud CLI, which means you can immediately run them once you install the dbt Cloud CLI and don't need to install MetricFlow separately. You don't need to manage versioning because your dbt Cloud account will automatically manage the versioning for you. +#### dbt Core You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). You need to use `pip` to install MetricFlow on Windows or Linux operating systems: 1. Create or activate your virtual environment `python -m venv venv` @@ -54,10 +34,6 @@ You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-star **Note**, you'll need to manage versioning between dbt Core, your adapter, and MetricFlow. - - - - Something to note, MetricFlow `mf` commands return an error if you have a Metafont latex package installed. To run `mf` commands, uninstall the package. ## MetricFlow commands @@ -65,9 +41,9 @@ Something to note, MetricFlow `mf` commands return an error if you have a Metafo MetricFlow provides the following commands to retrieve metadata and query metrics. - + -You can use the `dbt sl` prefix before the command name to execute them in the dbt Cloud CLI. For example, to list all metrics, run `dbt sl list metrics`. For a complete list of the MetricFlow commands and flags, run the `dbt sl --help` command in your terminal. +You can use the `dbt sl` prefix before the command name to execute them in the dbt Cloud IDE or dbt Cloud CLI. For example, to list all metrics, run `dbt sl list metrics`. For a complete list of the MetricFlow commands and flags, run the `dbt sl --help` command in your terminal. - [`list`](#list) — Retrieves metadata values. - [`list metrics`](#list-metrics) — Lists metrics with dimensions. From a7cd168963d492301595c7c1747b734251426df4 Mon Sep 17 00:00:00 2001 From: mirnawong1 Date: Tue, 17 Sep 2024 14:25:57 +0100 Subject: [PATCH 03/39] add ide support --- .../docs/docs/build/metricflow-commands.md | 70 +++++++++++-------- .../dbt-cloud-ide/develop-in-the-cloud.md | 1 + .../docs/use-dbt-semantic-layer/exports.md | 2 +- website/docs/guides/sl-snowflake-qs.md | 4 +- .../snippets/_sl-test-and-query-metrics.md | 6 +- 5 files changed, 47 insertions(+), 36 deletions(-) diff --git a/website/docs/docs/build/metricflow-commands.md b/website/docs/docs/build/metricflow-commands.md index e389a3c8a91..9a6b9a70054 100644 --- a/website/docs/docs/build/metricflow-commands.md +++ b/website/docs/docs/build/metricflow-commands.md @@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer] Once you define metrics in your dbt project, you can query metrics, dimensions, and dimension values, and validate your configs using the MetricFlow commands. -MetricFlow allows you to define and query metrics in your dbt project in the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation), [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud), or [dbt Core](/docs/core/installation-overview). To experience the power of the universal [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and dynamically query those metrics in downstream tools, you'll need a dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) account. +MetricFlow allows you to define and query metrics in your dbt project in the [dbt Cloud](/docs/cloud/about-develop-dbt) or [dbt Core](/docs/core/installation-overview). To experience the power of the universal [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and dynamically query those metrics in downstream tools, you'll need a dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) account. MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, and 3.11. @@ -20,12 +20,17 @@ Using MetricFlow with dbt Cloud means you won't need to manage versioning &mdash dbt Cloud jobs support the `dbt sl validate` command to [automatically test your semantic nodes](/docs/deploy/ci-jobs#semantic-validations-in-ci). You can also add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs. -#### dbt Cloud -In dbt Cloud, run MetricFlow commands directly in the dbt Cloud IDE or in the dbt Cloud CLI. + + + + +In dbt Cloud, run MetricFlow commands directly in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or in the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation). For dbt Cloud CLI users, MetricFlow commands are embedded in the dbt Cloud CLI, which means you can immediately run them once you install the dbt Cloud CLI and don't need to install MetricFlow separately. You don't need to manage versioning because your dbt Cloud account will automatically manage the versioning for you. + + + -#### dbt Core You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). You need to use `pip` to install MetricFlow on Windows or Linux operating systems: 1. Create or activate your virtual environment `python -m venv venv` @@ -36,25 +41,32 @@ You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-star Something to note, MetricFlow `mf` commands return an error if you have a Metafont latex package installed. To run `mf` commands, uninstall the package. + + + ## MetricFlow commands MetricFlow provides the following commands to retrieve metadata and query metrics. - + -You can use the `dbt sl` prefix before the command name to execute them in the dbt Cloud IDE or dbt Cloud CLI. For example, to list all metrics, run `dbt sl list metrics`. For a complete list of the MetricFlow commands and flags, run the `dbt sl --help` command in your terminal. +You can use the `dbt sl` prefix before the command name to execute them in the dbt Cloud IDE or dbt Cloud CLI. For example, to list all metrics, run `dbt sl list metrics`. For a complete list of the MetricFlow commands and flags, run the `dbt sl --help` command in your terminal. -- [`list`](#list) — Retrieves metadata values. -- [`list metrics`](#list-metrics) — Lists metrics with dimensions. -- [`list dimensions`](#list) — Lists unique dimensions for metrics. -- [`list dimension-values`](#list-dimension-values) — List dimensions with metrics. -- [`list entities`](#list-entities) — Lists all unique entities. -- [`list saved-queries`](#list-saved-queries) — Lists available saved queries. Use the `--show-exports` flag to display each export listed under a saved query. -- [`query`](#query) — Query metrics, saved queries, and dimensions you want to see in the command line interface. Refer to [query examples](#query-examples) to help you get started. -- [`export`](#export) — Runs exports for a singular saved query for testing and generating exports in your development environment. You can also use the `--select` flag to specify particular exports from a saved query. -- [`export-all`](#export-all) — Runs exports for multiple saved queries at once, saving time and effort. -- [`validate`](#validate) — Validates semantic model configurations. +Note: Only the `list`, `query`, and `validate` commands are available in the dbt Cloud IDE. + +|
Command
|
Description
| dbt Cloud IDE | dbt Cloud CLI | +|---------|-------------|---------------|---------------| +| [`list`](#list) | Retrieves metadata values. | ✅ | ✅ | +| [`list metrics`](#list-metrics) | Lists metrics with dimensions. | ✅ | ✅ | +| [`list saved-queries`](#list-saved-queries) | Lists available saved queries. Use the `--show-exports` flag to display each export listed under a saved query. | ✅ | ✅ | +| [`query`](#query) | Query metrics, saved queries, and dimensions you want to see in the command line interface. Refer to [query examples](#query-examples) to help you get started. | ✅ | ✅ | +| [`validate`](#validate) | Validates semantic model configurations. | ✅ | ✅ | +| [`export`](#export) | Runs exports for a singular saved query for testing and generating exports in your development environment. You can also use the `--select` flag to specify particular exports from a saved query. | ❌ | ✅ | +| [`export-all`](#export-all) | Runs exports for multiple saved queries at once, saving time and effort. | ❌ | ✅ | +| [`list dimensions`](#list) | Lists unique dimensions for metrics. | ❌ | ✅ | +| [`list dimension-values`](#list-dimension-values) | List dimensions with metrics. | ❌ | ✅ | +| [`list entities`](#list-entities) | Lists all unique entities. | ❌ | ✅ | - ## View query history in Explorer To enhance your discovery, you can view your model query history in various locations within dbt Explorer: -- [View from Performance charts](#view-from-performance-charts) -* [View from Project lineage](#view-from-project-lineage) -- [View from Model list](#view-from-model-list) +- [View from Performance charts](#view-from-performance-charts) +* [View from Project lineage](#view-from-project-lineage) +- [View from Model list](#view-from-model-list) ### View from Performance charts @@ -92,7 +95,7 @@ To enhance your discovery, you can view your model query history in various loca 4. Click on a model for more details and go to the **Performance** tab. 5. On the **Performance** tab, scroll down to the **Model performance** section. 6. Select the **Consumption queries** tab to view the consumption queries over a given time for that model. - + ### View from Project lineage diff --git a/website/static/img/docs/collaborate/dbt-explorer/enable-query-history-success.jpg b/website/static/img/docs/collaborate/dbt-explorer/enable-query-history-success.jpg new file mode 100644 index 0000000000000000000000000000000000000000..5b25372ab59990aa58096711f23df57e08085220 GIT binary patch literal 60135 zcmeEug?w?yl2S^*nV>n1Y-*G9oS_1Ox=Kq{Jsh2ngsQ2#7Zn@UY-BDNSXX-~(j~ zQBeg+QBh(AM>|ssYZC~F4@oYu{L;M&7{Ti^$y;!w3!m+W6og4I!(1G!cqM+3%uLec zTi`0?5zT-6>^wEDx3P14tSS!It$_gpg5yFId2iX2z5K%hb8^?7PcB3C@t<@g?W*WKm&x7&R- zd^hv!nBJeKnSb~wMB|OQzM+;YJ86|+A~)nf{tO^pxVV?G@7|ASFr!W(>7XEFk@AeY z%T}j$K2U{I8*$mTf@Oa8)6mhyNOHum7&8i9!?<|8i?`8(WoN?g8epJH+z=59#|UPT z*`h09VnPT~FSMnJ-|FZ*!!q}(o3zG)SAn$Af@09?*YxKo`bovvHA^vV^Sqy~$a;J% zQz~%F={H2ZZ&xbh40%q-ZO6`aJK>*A`LO&1T?A*~b2ayP2Oefs6Lm>bSy>1g@IE}m zn-B{KXz<<}@CO(Cfq;OD4~BpPf1`mvpK>Ap=PLA2F4X_*zoGbDP*_P+QWE^FWaMaK 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zVX{&fLwCRh#txlBaaLO8+Hr7#jmR}}YnBO5-3rLZ^iZg!MT2T5+3~pb-aBwD3WChs zD3rgpKTKEKr#cd#k&~_7b*!xj+P=_!1b0{g{USf8ZKV{tKEDMkOa|t!#TbDB83f+t(v}(&G#rIGGMWkXx@{m-8eEVfr=JSwM zlyZfXM@-4~;=OGYnBd__bPY-|V{UcaB@b$>2<=@fxapDr)nM#+HlA{soBFz@M4nsc5 Date: Wed, 25 Sep 2024 10:54:16 +0100 Subject: [PATCH 14/39] add rn --- website/docs/docs/dbt-versions/release-notes.md | 1 + 1 file changed, 1 insertion(+) diff --git a/website/docs/docs/dbt-versions/release-notes.md b/website/docs/docs/dbt-versions/release-notes.md index f3a0c53844e..5b139098279 100644 --- a/website/docs/docs/dbt-versions/release-notes.md +++ b/website/docs/docs/dbt-versions/release-notes.md @@ -20,6 +20,7 @@ Release notes are grouped by month for both multi-tenant and virtual private clo ## September 2024 +- **New**: dbt Explorer's Model query history feature is now in Preview for dbt Cloud Enterprise customers. Model query history allows you to view the count of consumption queries for a model based on the data warehouse's query logs. This feature provides data teams insight, so they can focus their time and infrastructure spend on the worthwhile used data products. To learn more, refer to [Model query history](/docs/collaborate/model-query-history). - **New:** There are two new [environment variable defaults](/docs/build/environment-variables#dbt-cloud-context) — `DBT_CLOUD_ENVIRONMENT_NAME` and `DBT_CLOUD_ENVIRONMENT_TYPE`. - **New:** The [Amazon Athena warehouse connection](/docs/cloud/connect-data-platform/connect-amazon-athena) is available as a public preview for dbt Cloud accounts that have upgraded to [`versionless`](/docs/dbt-versions/versionless-cloud). From 78643b8a49b0db3a5beb56294fe1c361baf025f0 Mon Sep 17 00:00:00 2001 From: mirnawong1 Date: Wed, 25 Sep 2024 11:20:09 +0100 Subject: [PATCH 15/39] update to ga --- website/docs/docs/collaborate/data-tile.md | 20 +++++-------------- .../docs/docs/dbt-versions/release-notes.md | 1 + 2 files changed, 6 insertions(+), 15 deletions(-) diff --git a/website/docs/docs/collaborate/data-tile.md b/website/docs/docs/collaborate/data-tile.md index efd6a0d59aa..39fe058670f 100644 --- a/website/docs/docs/collaborate/data-tile.md +++ b/website/docs/docs/collaborate/data-tile.md @@ -6,14 +6,8 @@ description: "Embed data health tiles in your dashboards to distill trust signal image: /img/docs/collaborate/dbt-explorer/data-tile-pass.jpg --- -# Embed data health tile in dashboards - With data health tiles, stakeholders will get an at-a-glance confirmation on whether the data they’re looking at is stale or degraded. This trust signal allows teams to immediately go back into Explorer to see more details and investigate issues. -:::info Available in beta -Data health tile is currently available in open beta. -::: - The data health tile: - Distills trust signals for data consumers. @@ -122,17 +116,13 @@ Follow these steps to embed the data health tile in Tableau: 1. Create a dashboard in Tableau and connect to your database to pull in the data. -2. Ensure you've copied the iFrame snippet available in dbt Explorer's **Data health** section, under the **Embed data health into your dashboard** toggle. -3. Embed the snippet in your dashboard. - - `