data.world not only supports metadata collection, but can also power federated queries of data sources such as Snowflake, BigQuery, SQL Server, and many others. Now in addition to our UI, you can use our API to manage these virtual connections and tables.

Head over to our API documentation to learn more about configuring virtual connections and adding virtual tables to your datasets with the public API.

🚨 Default behavior change coming next week 🚨

We have recently discovered that when executing queries, there are some cases where our DATETIME columns contain timezone information, and other cases where they do not. This is primarily an issue that arises with columns containing date/time information in uploaded files (we do not see this with live tables). We have decided to address this inconsistency. Starting next week, query result columns of type DATETIME will no longer contain timezone information, while columns of type DATETIMESTAMP will always contain timezone information.

The impact of this change shouldn’t be significant, and most users will see no change. However, if you have queries across ingested data which aggregate on DATETIME columns, or do DATE_ADD() style calculations, you may notice differences in your results depending on your current timezone.

If you are impacted by this change, here are some ways to clarify your intent w.r.t. timezones:

1. CAST the resulting column to a DATETIMESTAMP to force timezones, or DATETIME to strip timezones (documentation)
2. Use AT_TIME_ZONE() to explicitly state your timezone (documentation)
3. Ensure that the table column type is set to be of type DATETIMESTAMP or DATETIME (documentation)

Note: If timezone information is desired, but not defined, UTC is assumed. 

Please contact support@data.world with any questions or concerns. As always, we’re happy to help.

data.world is very excited to announce our new deep brain integration.

Now data consumers simply need to think about what data they want, and data.world will return governed, curated data. It also supports cataloging of business terminology straight from subject matter experts.

When we originally envisioned the feature, our design inspiration was to provide an "easy button." However Jon Loyens, co-founder and CPO, famously then said "what if there was no button at all?"

A future release will support agile data governance workflows, such as data access approvals. Integration is quick and relatively painless, though upgrades require a bit of effort and minor outpatient surgery.

Tags now support ampersands, apostrophes, and periods 🎉  

Search is a core feature of data.world and is consistently a focus of our improvement efforts. This month, we've rolled out improvements to result rankings on searches that contain more than one word. This improvement provides better rankings for results with titles that exactly match the submitted search as well as several other ranking and relevance improvements. These changes also provide better support for searches that contain special characters such as ampersands, dashes, underscores, and slashes.

Here is a roundup of some of the key features we released last month, February 2021 -- Looker metadata support, custom metadata field default edit behavior improvements, Gra.fo enhancements, and dataset sync webhook notifications.

Check out recent UX improvements to Gra.fo, our visual knowledge graph modeling tool. 

1. To quickly zoom out to see the full graph, use the keyboard shortcut:
   OPTION (ALT) + SHIFT + 0
2. To zoom to a specific concept:
   OPTION (ALT) + SHIFT + click concept
3. You can now deeplink directly to a concept in the graph using the Copy link to concept option in the concept menu. Watch the video to see how we use this feature to move fluidly between a data.world catalog and Gra.fo.

Gra.fo is a companion product to data.world with a 30 day free trial of premium team features. Visit Gra.fo to learn more, or reach out to help@data.world.

Here is a roundup of some of the key features we released last month, January 2021 -- broader resource support for recently viewed resources and auto suggestions from the search bar, collection search filter/facet, collection management API, and enhanced SQL support for windowed aggregations.

data.world now sends 2 additional webhooks: Dataset Sync Failure and Dataset Sync Recovery Success. 

When a dataset within your org fails to sync, data.world will immediately send a webhook message to the configured URL with details about the failure, including error code and error messages from the downstream source.

With those sync failure details, you could configure a ticket to be created in your ticketing system, trigger an alert for a dev-ops team, or simply just make the team aware of the issue.

Natively, data.world will format the webhook payload to post messages in Slack when it recognizes the configured webhook is Slack, which is shown in the demo video below.

Demo

Screenshots

Technical Details

syncTask.status.success is triggered only after the recovery from a failure. 

Example payload, in JSON:

{
  "datasetid": "sync-demo",
  "event": {
    "type": "SUCCESS",
    "task": "state_table.csv",
    "created": "2021-02-04T06:37:06.936Z"
  },
  "eventType": "syncTask.status.success",
  "resourceUrl": "https://data.world/texas-whiskey/sync-demo/file/state_table.csv",
  "resourceType": "URL",
  "resourceOwner": "texas-whiskey"
}

 syncTask.status.failure is triggered on the first failure for that file. When it recovers, data.world will send one syncTask.status.success

Example payload, in JSON:

{
  "datasetid": "sync-demo",
  "event": {
    "type": "FAILURE",
    "task": "state_table.csv",
    "created": "2021-02-04T06:35:17.945Z",
    "error": "HTTP/1.1 404 Not Found"
  },
  "eventType": "syncTask.status.failure",
  "resourceUrl": "https://data.world/texas-whiskey/sync-demo/file/state_table.csv",
  "resourceType": "URL",
  "resourceOwner": "texas-whiskey"
}

Have you ever need to create a 7-day trailing average in order to smooth out a graph? How about compare a value to the same value in the previous row?

Windowed aggregations are a powerful way to perform complex analysis, such as this, without leaving the comfort of the SQL. These functions operate on a defined group, or "window", of rows to return an aggregated value for each row. Window aggregations are supported for both uploaded tabular data, as well as live tables (where support exists in the connected database).

Note: this feature is currently in Beta. Please contact support with any issues.