Understanding where your product is used around the world is an important step towards developing a more complete understanding of your customers, your product's global reach, and related location insights.
Currently, the majority of GitLab's customers choose to download, install, and host a GitLab self-managed instance, which is why we are focused heavily on delivering a great self-managed customer experience.
To make the right data-driven decisions on the self-managed product lifecycle and what features to invest in, our self-managed customers sends GitLab a weekly usage ping at an instance-level by enabling usage ping on their self-managed instance or by sharing the values with our Customer Success team.
This instance-level data allows GitLab to understand country-level statistics and trends in instance adoption, version adoption rate, and instance life cycle.
The goal of this page:
Some data supporting Product Geolocation Analysis is classified as Orange or Yellow. This includes ORANGE customer metadata from the account, contact data from Salesforce and Zuora and GitLab's Non public financial information, all of which shouldn't be publicly available. Care should be taken when sharing data from this dashboard to ensure that the detail stays within GitLab as an organization and that appropriate approvals are given for any external sharing. In addition, when working with row or record level customer metadata care should always be taken to avoid saving any data on personal devices or laptops. This data should remain in Snowflake and Sisense and should ideally be shared only through those applications unless otherwise approved. ORANGE
is_last_ping_in_month. This ensures usage metrics are deduplicated across instances.
usage_ping_mart. Examples on how to work with this data can be found on the Customer Segmentation handbook page.
To receive a Certificate, you will need to earn 100% on the Self-Service SQL Developer Knowledge Assessment. Upon completion of the Knowledge Assessment, you will be emailed your responses and this email will serve as your Certificate.
Snippets are used to create a string of SQL code that can be reused in different charts. For more information, visit the Sisense SQL Snippets page.
This snippet is currently present in Sisense with the name of usage_pings_mart.
WITH pings AS ( SELECT * FROM analytics.usage_ping_mart WHERE ping_source = 'Self-Managed' AND is_last_ping_in_month = TRUE AND date_id >= 20191101 AND [ping_product_tier=product_tier] AND [ping_country_name=Usage_Ping_Country] )
[usage_pings_mart] SELECT ping_month, COUNT(DISTINCT account_id) AS total_accounts FROM pings GROUP BY 1
[usage_pings_mart] SELECT ping_month, ping_country_name AS country_name, COUNT(DISTINCT uuid) AS instances_reporting FROM pings GROUP BY 1,2
If you are not familiar with SQL, there is the Data Discovery function in Sisense wherein you can create charts through a drag-and-drop interface and no SQL query is needed.
More information here on the Data Discovery page in Sisense.
|Usage Ping Mart||usage_ping_id||Mart for exploring usage ping and related customer segmentation metrics|
In order to avoid large joins between tables and the IP-address-to-geolocation mapping consisting of less-than/greater-than join clauses, IP addresses are incrementally mapped to geolocations separate from other models as implemented originally in this merge request.
This approach also gives us the ability to obscure IP addresses in Sisense but still preserving the ability to match IP addresses across different database tables.