Business context

A key aspect of data compliance is knowing where sensitive data lives and applying classifications that relate to policies that inform business processes for proper tracking and management. Identifying sensitive data, applying these policies, and reporting on this information can be an extremely time consuming and error-prone task if attempted manually.

data.world’s Sensitive Data Discovery automates discovery and classification, making it easier for enterprise customers to identify sensitive data and take action on it within the catalog.

Capabilities

Scan – Use advanced machine learning to identify sensitive data types like email addresses, names, ID numbers, locations, protected health information, and 40+ additional data types identifiable out of the box.

Classify – Apply policy classifications, tags, and statuses such as Restricted, Personal Information, US Only, etc. These classifications help maintain the integrity and confidentiality of your data. They are driven by your scan results and other metadata, as dictated by your unique business logic and terminology.

Take Action – Report and audit sensitive data types and policy classifications across your data landscape, understand how it changes over time, and drive better compliance and governance in your organization.

Integrate – Leverage Sensitive Data Discovery metadata as part of your broader metadata orchestration strategy with APIs and bulk export. Our open and extensible platform makes it easy to plug in your broader ecosystem of additional Sensitive Data Discovery tools and platforms for even greater governance capabilities.

Screenshots

Resource page example

Search results example

If you are an existing data.world customer and would like to be included in the private beta, reach out to your Client Success Director for more information.

Business context

Querying data in its current state is the most common data catalog use case, but there are times when it is necessary to compare previous versions of datasets, metadata, and lineage. data.world SQL and SPARQL Time Travel allows customers to view changes across metadata and data and even query historical data sources. 

Capabilities

The new feature provides granular insight into audit trails and analysis of data that is snapshotted across time. You can search both ingested data sources and Snowflake virtual tables for previous states of data. Being able to analyze previous versions of a dataset, even simultaneously with the current version of a dataset, enables flexible analysis across various time scales – review data month-over-month, year-over-year, etc.

In data.world, your metadata is also data and therefore fully queryable and reportable. You can compare previous versions of your metadata with current versions in order to understand how your systems and schemas are changing. See new columns, new column names, sensitive data that recently appeared in a field that wasn't there previously, and much more.

Supported operations include previous version, number of versions back (tip-N), specific timestamp, and offset.

Example: SQL Time Travel Query

Example: SPARQL Time Travel Query

Business context

Most analysts trying to find answers to business questions aren’t searching for tables and columns directly. What they are actually looking for is contextual information that accelerates time to business impact for data. data.world Concept Cards will change the way data consumers access data by providing a unique search experience no other catalog provider does or can do without the backing of a knowledge graph.

Capabilities

Concept Cards are a feature on data.world’s near-term roadmap to help users discover related people, resources, and other supporting information we can obtain from the knowledge graph about a given search topic. If there are suggested actions that can be taken for the topic itself or for related resources, access to those actions is surfaced directly in the search results.

These cards become a jumping off point to browse and discover new things on the platform that share something in common with the search topic of interest. We see these Concept Cards as the first of many intelligent recommendations we can make by harnessing the power of the knowledge graph.

For customers leveraging data artifact lineage, the resource items are now interactive. You can click through the icons to view the respective resource pages.

Updated metrics tables/reports have arrived on September 16, 2021! Some reports may take 24-48 hours to reflect the new data after deploy due to sync timing.

New Tables (multi tenant & single tenant)

• Resources - Dataset Files - A detailed listing of all (currently existing) files residing in datasets.
• Tops - Engagement - A list of users ranked by key engagement metrics.
• Resources - Live Metadata Assets Created - By Day - A long form series of counts of metadata assets created by date.
• Events - Dataset Activity - By Day - A fact table containing dataset activity measurements, aggregated by UTC-based calendar day.
• Events - Metadata Assets Activity - By Day - A fact table containing metadata assets activity measurements aggregated by UTC-based calendar day.

Updated Tables (multi tenant & single tenant)

• Membership - Current - By Org - Added new columns for email address, user display name, org-level authorization settings, org-level visibility settings and date of most recent update to authorization settings.
• Events - Searches - Last 90 Days - Fixed a bug that caused the counts of search results to be capped at 10.
• Events - Downloads (previously Events - Downloads - Last 90 Days) - Extended the timeframe to all-time; added new columns for file labels and user displayname. 

Base platform data updates (single tenant only):

• DOWNLOADS - new columns: (type, filename, filelabels) providing information about file downloads.
• DAILY_DWEC_ASSET_FACTS - new fact table providing measurements of metadata asset activity by date.
• FILES_DATASET_DIM - new dimension table providing information about files residing in Datasets.

The data.world public API supports several options for programmatically making updates to resources on the platform. PATCH is a method for making partial updates to individual records, such as adding tags, changing a description, or modifying a title.

In the next two weeks, we will be making a change to the way PATCH endpoints modify list values. We outline these changes below.

Existing Merge Behavior

Lists are merged with existing values on PATCH requests

1. A dataset has tags: [tag A , tag B] 
2. A PATCH request is sent to /datasets/democorp/my-example-dataset with body: { "tags": [ "tag C", "tag D" ]  }
3. The dataset is updated to reflect tags: [ tag A, tag B, tag C ,tag D]
4. A PATCH request is sent to /datasets/democorp/my-example-dataset with payload: { "tags": []  }
5. No change is applied and the tags remain: [ tag A, tag B, tag C ,tag D]

New Replace Behavior

Lists replace existing values on PATCH requests

1. A dataset has tags: [tag A , tag B] 
2. A PATCH request is sent to /datasets/democorp/my-example-dataset with body: { "tags": [ "tag C", "tag D" ]  }
3. The dataset is now updated to have tags: [ tag C ,tag D].  tag A and tag B have been removed.
4. I send a PATCH request to /datasets/democorp/my-example-dataset with body: { "tags": []  }
5. The dataset has been updated to remove all tags.

Why we are making this change

Today, PATCH can be used to add, modify, or remove fields for all non-list values. With the current merge logic, items can only be appended to list values using PATCH. As a consequence, if you want to remove or reorder the items in a list, you must use the PUT method, which does not support partial updates and requires a full overwrite of the existing record. The new logic to overwrite list values will allow users to make partial updates to records that remove or modify the order of items in the list without needing to modify the entire record.

This new logic primarily impacts tags, file labels, collections, and multi-select custom metadata fields.

This week, our search page got a face lift and we unveiled new tools for searching on data.world.

Explore data.world's rich advanced search syntax with the Search Builder tool on our main search page. This friendly form helps you construct more complex searches with multiple filters, logical operators, categories, and custom metadata fields. The Search Builder can be accessed by selecting the "Advanced" option above the filters list on the main search page.

This release also includes changes to our main search page. You'll notice a new layout on the All Results tab that shows the top 3 search hits by type for your term. This tab now shows more results per page and gives users a high level overview of the types of resources they can find on the platform. Hover over the circular "i" icon for more details about the result. More targeted results can be viewed on the Resources tab. You'll also notice changes to the category tabs at the top of the page. Resources, Organizations & People, Comments, and Columns each have their own tailored search experience.

The new search experience is available today for select users and will be available for all users early next week.

We're happy to announce the beta release of our new Postgres Proxy support. Our goal is to allow any BI and data science tools which support PostgreSQL, to connect to directly with data.world. No 3rd party integration support required!

data.world provides a federated engine to query data from multiple data systems simultaneously, at the source. By using Postgres Proxy, it’s now easier than ever to extend these capabilities to your favorite analysis tools for quickly accessing and creating value from data.

To connect to data.world using the proxy, simply create a new PostgreSQL connection, configured as follows:

host: postgres.data.world
port: 5432
user: {your data.world user id}
pass: {read/write token}
db:   agentid/datasetid

You can find your read/write token in the user settings. If you have any issues or questions, don't hesitate to reach out.

Note: for single tenant customers, set host to postgres.{site}.data.world.

For enterprise customers, updated metrics have been released today to your Usage and Governance Reporting (ddw-metrics-*) dataset to address some minor bugs and performance improvements.

Potential observable changes:

• For both single-tenant and multi-tenant customers, some reports were not reflecting de-provisioned user accounts. With this fix, multi-tenant customers may find a slight increase in counts in Visits - Return Visitors - Daily, Visits - Return Visitor Days, Visits - Unique Visitor Days and Visits - Unique Visitors - Monthly now properly reflecting de-provisioned user accounts in addition to active user accounts. Single-tenant customers may find a slight increase in Visits - Unique Visitors Daily and Visits - Unique Visitor Days.
• For multi-tenant customers, under certain circumstances some reports could duplicate-count users that were members of multiple sub-organizations. With this fix, you may find a slight decrease in counts in Membership - By Date, Visits - Adoption Daily and Visits - Avg Visits Weekly.

If you have any questions or concerns, please let us know at support@data.world or via your customer success representative.

In August, we plan to release a series of improvements to our search page including: 

• Category tabs to replace the result type dropdown
• Collapsible filter groups for the left sidebar
• A new discovery-driven All Results Page to highlight a more comprehensive set of result types for broad searches
• A new Advanced Search Builder utility for more complex searches that take advantage of boolean operators and our advanced search syntax