docs: add doc for assertions & data contracts

docs-website/sidebars.js

@@ -69,24 +69,38 @@ module.exports = {
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-              id: "docs/managed-datahub/observe/volume-assertions",
-              className: "saasOnly",
-            },
-            {
-              type: "doc",
-              id: "docs/managed-datahub/observe/custom-sql-assertions",
-              className: "saasOnly",
+              label: "Assertions",
+              type: "category",
+              link: {
+                type: "doc",
+                id: "docs/managed-datahub/observe/assertions",
+              },
+              items: [
+                {
+                  type: "doc",
+                  id: "docs/managed-datahub/observe/freshness-assertions",
+                  className: "saasOnly",
+                },
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+                  type: "doc",
+                  id: "docs/managed-datahub/observe/volume-assertions",
+                  className: "saasOnly",
+                },
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+                  type: "doc",
+                  id: "docs/managed-datahub/observe/custom-sql-assertions",
+                  className: "saasOnly",
+                },
+                {
+                  type: "doc",
+                  id: "docs/managed-datahub/observe/column-assertions",
+                  className: "saasOnly",
+                },
+              ],
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               type: "doc",
-              id: "docs/managed-datahub/observe/column-assertions",
-              className: "saasOnly",
+              id: "docs/managed-datahub/observe/data-contract",
             },
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         },

docs/managed-datahub/observe/assertions.md

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+# Assertions
+
+:::note Contract Monitoring Support
+Currently we support Snowflake, Databricks, Redshift, and BigQuery for out-of-the-box contract monitoring as part of Acryl Observe.
+:::
+
+An assertion is **a data quality test that finds data that violates a specified rule.** 
+Assertions serve as the building blocks of [Data Contracts](/docs/managed-datahub/observe/data-contract.md) – this is how we verify the contract is met.
+
+## How to Create and Run Assertions
+
+Data quality tests (a.k.a. assertions) can be created and run by Acryl or ingested from a 3rd party tool.
+
+### Acryl Observe
+
+For Acryl-provided assertion runners, we can deploy an agent in your environment to hit your sources and DataHub. Acryl Observe offers out-of-the-box evaluation of the following kinds of assertions:
+
+- [Freshness](/docs/managed-datahub/observe/freshness-assertions.md) (SLAs)
+- [Volume](/docs/managed-datahub/observe/volume-assertions.md)
+- [Custom SQL](/docs/managed-datahub/observe/custom-sql-assertions.md)
+- [Column](/docs/managed-datahub/observe/column-assertions.md)
+
+These can be defined through the DataHub API or the UI. 
+
+<p align="center">
+  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/assertions/assertion-ui.png"/>
+</p>
+
+### 3rd Party Runners
+
+You can integrate 3rd party tools as follows:
+
+- [DBT Test](/docs/generated/ingestion/sources/dbt.md#integrating-with-dbt-test)
+- [Great Expectations](../../../metadata-ingestion/integration_docs/great-expectations.md)
+
+If you opt for a 3rd party tool, it will be your responsibility to ensure the assertions are run based on the Data Contract spec stored in DataHub. With 3rd party runners, you can get the Assertion Change events by subscribing to our Kafka topic using the [DataHub Actions Framework](/docs/actions/README.md).
+
+
+## Alerts
+
+Beyond the ability to see the results of the assertion checks (and history of the results) both on the physical asset’s page in the DataHub UI and as the result of DataHub API calls, you can also get notified via [slack messages](/docs/managed-datahub/saas-slack-setup.md) (DMs or to a team channel) based on your [subscription](https://youtu.be/VNNZpkjHG_I?t=79) to an assertion change event. In the future, we’ll also provide the ability to subscribe directly to contracts.
+
+With Acryl Observe, you can get the Assertion Change event by getting API events via [AWS EventBridge](/docs/managed-datahub/operator-guide/setting-up-events-api-on-aws-eventbridge.md) (the availability and simplicity of setup of each solution dependent on your current Acryl setup – chat with your Acryl representative to learn more).
+
+
+## Cost
+
+We provide a plethora of ways to run your assertions, aiming to allow you to use the cheapest possible means to do so and/or the most accurate means to do so, depending on your use case. For example, for Freshness (SLA) assertions, it is relatively cheap to use either their Audit Log or Information Schema as a means to run freshness checks, and we support both of those as well as Last Modified Column, High Watermark Column, and DataHub Operation ([see the docs for more details](/docs/managed-datahub/observe/freshness-assertions.md#3-change-source)).

docs/managed-datahub/observe/data-contract.md

@@ -0,0 +1,119 @@
+# Data Contracts
+
+## What Is a Data Contract
+
+A Data Contract is **an agreement between a data asset's producer and consumer**, serving as a promise about the quality of the data.
+It often includes [assertions](assertions.md) about the data’s schema, freshness, and data quality.
+
+Some of the key characteristics of a Data Contract are:
+
+- **Verifiable** : based on the actual physical data asset, not its metadata (e.g., schema checks, column-level data checks, and operational SLA-s but not documentation, ownership, and tags).
+- **A set of assertions** : The actual checks against the physical asset to determine a contract’s status (schema, freshness, volume, custom, and column)
+- **Producer oriented** : One contract per physical data asset, owned by the producer.
+
+
+<details>
+<summary>Consumer Oriented Data contracts</summary>
+We’ve gone with producer-oriented contracts to keep the number of contracts manageable and because we expect consumers to desire a lot of overlap in a given physical asset’s contract. Although, we've heard feedback that consumer-oriented data contracts meet certain needs that producer-oriented contracts do not. For example, having one contract per consumer all on the same physical data asset would allow each consumer to get alerts only when the assertions they care about are violated.We welcome feedback on this in slack!
+</details>
+
+Below is a screenshot of the Data Contracts UI in DataHub.
+
+<p align="center">
+  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/data_contracts/validated-data-contracts-ui.png"/>
+</p>
+
+## Data Contract and Assertions
+
+Another way to word our vision of data contracts is **A bundle of verifiable assertions on physical data assets representing a public producer commitment.** 
+These can be all the assertions on an asset or only the subset you want publicly promised to consumers. Data Contracts allow you to **promote a selected group of your assertions** as a public promise: if this subset of assertions is not met, the Data Contract is failing.
+
+See docs on [assertions](/docs/managed-datahub/observe/assertions.md) for more details on the types of assertions and how to create and run them.
+
+:::note Ownership 
+The owner of the physical data asset is also the owner of the contract and can accept proposed changes and make changes themselves to the contract.
+:::
+
+
+## How to Create Data Contracts
+
+Data Contracts can be created via DataHub CLI (YAML), API, or UI.
+
+### DataHub CLI using YAML
+
+For creation via CLI, it’s a simple CLI upsert command that you can integrate into your CI/CD system to publish your Data Contracts and any change to them.
+
+1. Define your data contract.
+
+```yaml
+{{ inline /metadata-ingestion/examples/library/create_data_contract.yml show_path_as_comment }}
+```
+
+2. Use the CLI to create the contract by running the below command.
+
+```shell
+datahub datacontract upsert -f contract_definition.yml
+```
+
+3. Now you can see your contract on the UI.
+
+<p align="center">
+  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/data_contracts/data-contracts-ui.png"/>
+</p>
+
+
+### UI
+
+1. Navigate to the Dataset Profile for the dataset you wish to create a contract for
+2. Under the **Validations** > **Data Contracts** tab, click **Create**.
+
+<p align="center">
+  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/data_contracts/create-data-contract-ui.png"/>
+</p>
+
+
+3. Select the assertions you wish to be included in the Data Contract.
+
+<p align="center">
+  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/data_contracts/select-assertions.png"/>
+</p>
+
+
+:::note Create Data Contracts via UI
+When creating a Data Contract via UI, the Freshness, Schema, and Data Quality assertions must be created first.
+:::
+4. Now you can see it in the UI.
+
+<p align="center">
+  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/data_contracts/contracts-created.png"/>
+</p>
+
+
+### API
+
+_API guide on creating data contract is coming soon!_
+
+
+## How to Run Data Contracts
+
+Running Data Contracts is dependent on running the contract’s assertions and getting the results on Datahub. Using Acryl Observe (available on SAAS), you can schedule assertions on Datahub itself. Otherwise, you can run your assertions outside of Datahub and have the results published back to Datahub. 
+
+Datahub integrates nicely with DBT Test and Great Expectations, as described below. For other 3rd party assertion runners, you’ll need to use our APIs to publish the assertion results back to our platform.
+
+### DBT Test
+
+During DBT Ingestion, we pick up the dbt `run_results` file, which contains the dbt test run results, and translate it into assertion runs. [See details here.](/docs/generated/ingestion/sources/dbt.md#module-dbt)
+
+<p align="center">
+  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/data_contracts/dbt-test.png"/>
+</p>
+
+
+
+### Great Expectations
+
+For Great Expectations, you can integrate the **DataHubValidationAction** directly into your Great Expectations Checkpoint in order to have the assertion (aka. expectation) results to Datahub. [See the guide here](../../../metadata-ingestion/integration_docs/great-expectations.md).
+
+<p align="center">
+  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/observe/data_contracts/gx-test.png"/>
+</p>

metadata-ingestion/examples/library/create_data_contract.yml

@@ -0,0 +1,39 @@
+# id: sample_data_contract # Optional: if not provided, an id will be generated
+entity: urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)
+version: 1
+freshness:
+  type: cron
+  cron: "4 8 * * 1-5"
+data_quality:
+  - type: unique
+    column: field_foo
+## here's an example of how you'd define the schema
+# schema:
+#   type: json-schema
+#   json-schema:
+#     type: object
+#     properties:
+#       field_foo:
+#         type: string
+#         native_type: VARCHAR(100)
+#       field_bar:
+#         type: boolean
+#         native_type: boolean
+#       field_documents:
+#         type: array
+#         items:
+#           type: object
+#           properties:
+#             docId:
+#               type: object
+#               properties:
+#                 docPolicy:
+#                   type: object
+#                   properties:
+#                     policyId:
+#                       type: integer
+#                     fileId:
+#                       type: integer
+#     required:
+#       - field_bar
+#       - field_documents