De-indent headers

Starting at h1 everywhere

src/cookbooks/data_monitoring/collections.md

@@ -1,24 +1,24 @@
-## Collections in Bigeye
+# Collections in Bigeye
 
 Collections help you organize and focus on specific areas of interest, making it simpler to track and address data quality across different segments of your data landscape. This feature enhances efficiency by allowing users to monitor grouped entities in a cohesive manner.
 
 ![](../../assets/Bigeye/Bigeye-Collections.png)
 
-### Creating a new collection
+## Creating a new collection
 
 If you don’t find a collection that meets your product or requirements, admins can create a new collection.
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/YTOOTFw5MLw?si=X3VybaWasts-sdjw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
 If you're not an admin, please [submit a Jira ticket](https://mozilla-hub.atlassian.net/browse/DENG-4726) with the necessary details.
 
-### Adding metrics to collection
+## Adding metrics to a collection
 
 To add metrics to a collection in Bigeye, navigate to the collection you want to update and click "Add Metrics." You can search or filter for specific metrics that align with your monitoring goals.
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/ZFRAaeX6z8w?si=ba3jYHTQNZPDi9ua" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
-### Adding notifications to collection
+## Adding notifications to a collection
 
 One useful feature of collections is the ability to add notifications. To set this up, click the "Edit" button, then navigate to the "Notifications" tab in the modal that appears.
 

src/cookbooks/data_monitoring/cost_considerations.md

@@ -1,4 +1,4 @@
-### Cost considerations
+# Cost considerations
 
 The Freshness and Volume metrics, which represent Pipeline Reliability, are included in the free tier. There is no charge when these metrics are added to a table.
 

src/cookbooks/data_monitoring/deploying_metrics.md

@@ -1,4 +1,4 @@
-## Deploying Metrics
+# Deploying Metrics
 
 To deploy metrics in Bigeye, navigate to a schema, table, or column and click "Add Monitoring."
 You can choose to add metrics via 4 options
@@ -25,7 +25,7 @@ For more details, please refer to the Bigeye documentation on [how to deploy met
 
 Watch the Bigeye tutorial on [how to use the metrics page](https://www.youtube.com/watch?v=jNzSki59AWQ)
 
-### Freshness and Volume (Pipeline Reliability)
+## Freshness and Volume (Pipeline Reliability)
 
 Bigeye tracks data quality by monitoring the timeliness (freshness) and completeness (volume) of your data and checks them hourly.
 Initially, it looks back 28 days, then 2 days for subsequent runs. For volume, it aggregates row counts into hourly buckets, using the same lookback periods. We have an option to select [`manual`](https://docs.bigeye.com/docs/manual-thresholds) thresholds vs [`Autothresholds`](https://docs.bigeye.com/docs/autothresholds) that learn typical patterns and alert on anomalies.
@@ -39,7 +39,7 @@ Only one Freshness and one Volume metric can be deployed per table.
 
 Please refer to Bigeye documentation for more details on [Freshness and Volume metrics](https://docs.bigeye.com/docs/freshness-and-volume-pipeline-reliability-copy).
 
-### List of available metrics
+## List of available metrics
 
 Bigeye offers a range of available metrics to monitor data quality and reliability across your data pipelines.
 These metrics cover areas such as data freshness, volume, distribution, schema changes, and anomalies. You can deploy these metrics to track key performance indicators and ensure your data meets expected standards.
@@ -48,19 +48,19 @@ Please refer to the Bigeye documentation for [list of available metrics](https:/
 
 Watch the Bigeye tutorial on the [metrics types](https://www.youtube.com/watch?v=jNzSki59AWQ)
 
-### Autometrics
+## Autometrics
 
 Autometrics are suggested metrics that monitor anomalies in column-level data, automatically generated for all new datasets in Bigeye. They can be found under the Autometrics tab in the Catalog when viewing a source, schema, table, or column page.
 
-> :warning: **Try to avoid this option!**: On tables with many columns a large number of monitors might get deployed. This increases noise and cost. Instead, it is recommended to [choose relevant metrics from the list of available metrics](#list-of-available-metrics) manually.
+> **Try to avoid this option!**: On tables with many columns a large number of monitors might get deployed. This increases noise and cost. Instead, it is recommended to [choose relevant metrics from the list of available metrics](#list-of-available-metrics) manually.
 
 ![](../../assets/Bigeye/Bigeye-Autometrics.png)
 
-### Custom SQL
+## Custom SQL
 
 Custom rules are useful for addressing unique data quality requirements that standard metrics may not cover. Once set, these rules integrate into your monitoring workflow.
 
-### Recommendations / Best Practices to deploying metrics
+## Recommendations / Best Practices to deploying metrics
 
 - It's recommended to avoid deploying Autometrics extensively, as this could result in a high signal-to-noise ratio, leading to unnecessary alerts and potential distraction.
 

src/cookbooks/data_monitoring/further_reading.md

@@ -1,4 +1,4 @@
-## Further reading
+# Bigeye - Further reading
 
 - Kinds of [alert thresholds](https://www.youtube.com/watch?v=8DWyZuU-w1c&t=9s) that Bigeye supports
 

src/cookbooks/data_monitoring/interface.md

@@ -1,6 +1,6 @@
-## Interface
+# Bigeye Interface
 
-### Catalog
+## Catalog
 
 The Catalog tab in the left-hand menu offers a comprehensive view of all data sources connected to Bigeye, making it simple to navigate and manage your entire data ecosystem.
 
@@ -14,7 +14,7 @@ For more detailed information about the Catalog, please refer to the [Catalog do
 
 Watch the [Bigeye tutorial](https://www.youtube.com/watch?v=8DWyZuU-w1c&t=9s) on how to navigate Bigeye Catalog.
 
-### Workspaces
+## Workspaces
 
 Workspaces in Bigeye allow multiple teams to collaborate simultaneously, with each team managing and monitoring their own data independently.
 Each Bigeye workspace includes its own Catalog, BI tools, and ETL tools, Metrics and issues, Templates and schedules, Collections, Deltas.
@@ -25,7 +25,7 @@ If you do not find a suitable workspace, please submit a [Jira ticket](https://m
 
 ![](../../assets/Bigeye/Bigeye-Workspace.png)
 
-### Collections
+## Collections
 
 Collections in Bigeye allow you to group related metrics, making it easier to manage and monitor them together.
 
@@ -34,14 +34,14 @@ If you're not an admin, please submit a [Jira ticket](https://mozilla-hub.atlass
 
 Watch the [Bigeye tutorial](https://www.youtube.com/watch?v=4H5AM0a71bs&list=PLUmsPWeo8j4U9SpGCnAe9syilD4_jBgSI&index=8) on how to navigate Bigeye Catalog.
 
-### Issues
+## Issues
 
 Bigeye's Issues feature helps you track and manage data quality issues detected by the platform.
 You can assign, prioritize, and resolve issues within the platform, ensuring that your data quality remains high. Issues can be categorized and filtered to streamline the resolution process across teams.
 
 For more details, refer to the Bigeye documentation on [Issues page](https://docs.bigeye.com/docs/issues)
 
-### Dashboard
+## Dashboard
 
 Users can monitor the data quality metrics and issues in a centralized view. It highlights key features such as customizable widgets, real-time metric tracking, and the ability to visualize data health at a glance. Users can configure dashboards to focus on specific metrics or tables and receive immediate insights into their data pipelines' performance.
 

src/cookbooks/data_monitoring/issues_management.md

@@ -1,10 +1,10 @@
-## Issues management
+# Issues management
 
 The `Issues` tab allows filtering issues by parameters like severity or date, and reviewing details such as impacted metrics and tables.
 
 ![The Issues overview on BigEye, allowing to filter issues](../../assets/Bigeye/issues-tab.png)
 
-### View issue details
+## View issue details
 
 Once we click on the issue we can view a metric chart that displays a time series visualization of the alerting metric.
 
@@ -14,7 +14,7 @@ The metric chart in Bigeye displays a time series visualization of the alerting
 
 ![](../../assets/Bigeye/Issues-2.png)
 
-### Status of Issue
+## Status of Issue
 
 Users can change the status to "Active," "Resolved," "Muted," or "Dismissed," depending on the issue's current state. This allows for better tracking and management of issues across data pipelines. Status updates are reflected in the timeline, providing a clear history of the issue's progression.
 
@@ -26,21 +26,21 @@ Use the Mute button above the timeline to mute an issue and stop being notified
 
 For more details, refer to the [Bigeye documentation](https://docs.bigeye.com/docs/change-the-issue-status).
 
-### Debug
+## Debug
 
 Use the queries in the Debug tab to troubleshoot your issue.
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/rSWAl7f8vcc?si=YA2HgEcRIyL0FKMC" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
-### Jira Integration
+## Jira Integration
 
 The Bigeye integration with Jira enables teams to track critical data quality issues seamlessly through Jira's flexible interface. The bi-directional integration ensures that updates made in either Jira or Bigeye are synced across both systems. Once a Jira ticket is created in Bigeye, any status changes or comments are automatically reflected in both platforms, keeping all team members informed.
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/5UtAkwvjt5U?si=wnpb6fqhQMPvS6wO" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
-### Slack Integration [Coming Soon]
+## Slack Integration [Coming Soon]
 
 Bigeye enables users to take direct actions on issues on Slack messages without needing to navigate to the Bigeye web interface.
 Users can resolve, mute, or dismiss alerts directly from Slack messages, ensuring efficient workflows and quick responses to data quality issues.
 
-#### Recommendation / Best practices [Coming Soon]
+## Recommendation / Best practices [Coming Soon]