Release - October 27th, 2021

✨ New ✨

Mode Integration through JDBC

Details

Product Component

Interfaces - SQL

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We've released the production version of our integration with Mode analytics. The integration will enable Mode users to have the flexibility of reporting and BI of Mode, with the assurance of accurate metrics from Transform.

Our integration allows you to express MQL requests through SQL using a JDBC interface.

Example:

SELECT * FROM MQL_QUERY(<    bookings   BY     user__country>);

Verification Steps

To verify this, you need a Mode analytics instance.

Documentation for setup with Mode can be found here .

Documentation for using our JDBC interface can be found here.

Documentation Site

Details

Product Component

General

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We are excited to announce that we are officially live with a documentation site at docs.transform.co which you can access from our product. Above the setting link on the left navigation, you'll find a new link called "Documentation".

Note some of our documentation is a work in progress. Please contact us if there is specific content you'd like to see!

Verification Steps

Use the new left nav "Documentation" link to access Transform's Documentation Site.

Quarterly Granularity

Details

Product Component

Metrics Catalog

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We have now released Quarterly granularity as an option in our chart menu, which will aggregate values by the calendar quarter.

Verification Steps

Open up the granularity menu in Edit Chart and confirm that Quarterly is an option!

Surfacing Local Dimensions

Details

Product Component

Metrics Catalog

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Certain data sources do not have primary identifiers and so those dimensions cannot be joined onto other data sources. However, those dimensions can still be accessed locally to that data source. Previously, our UI hid these dimensions, but now we list these dimensions at the bottom of the menu under "Local Dimensions". You can optionally search "Local" to find all these dimensions

Verification Steps

For a metric that's based on a data source without a primary identifier and with dimensions, check that you can see all the local dimensions at the bottom.

🐛 Bug Fixes 🐛

Cumulative Metric Bug Fix

Details

Product Component

Metrics Framework

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We fixed a bug where cumulative metrics were returning incorrect results for some situations

The issue was a spurious groupby when Transform renders the time range join for cumulative metrics. Cumulative metrics are implemented by aggregating the metric's measure across the window specified in the metric. For example:

select  sum(bookings) as bookingsfrom bookings_table ajoin bookings_table bon b.ds <= a.ds and b.ds > date_sub(a.ds, 7, days)group by b.ds, bookings

In this case, the time range (b.ds) was non-varying, Transform would incorrectly group all the bookings values across those 7 days (removing duplicated values). Grouping by a.ds, bookings would work but would be a no-operative, so the fix removed the group by entirely.

Verification Steps

Create a cumulative metric such that the data being accumulated contains repeated values.

ie "weekly bookings"

metric:  name: weekly_bookings  description: weekly bookings  owners:    - bobby@transformdata.io  type: cumulative  type_params:    measures:      - bookings    window: 7 days

measure_bookings, ds1000 2020/01/012000 2020/01/021000 2020/01/033000 2020/01/04

Previously calculating weekly_bookings for 2020-01-05 would yield 6000 (omitting the repeated 1k value). It would now yield the correct value (7000)

No longer formatting dimension values

Details

Product Component

Metrics Catalog

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Previously, we would autoformat your dimension values by removing special characters and adding spaces. This could be confusing because what we displayed in the UI is different than the source database. For any net new Transform deployments, dimension values will not be formatted. Note: Dimension names will still be formatted.

If you would like, you can request to go back to the dimension value auto-formatting behavior. Please reach out to us at support@transformdata.io or over Slack.

Verification Steps

To verify, you can confirm your dimension values in the filter menu match the values in your database.