From a Data Validation perspective, having access to validate data using DBeaver can enable your data quality team to succeed while validating - this is a must have!
Discover how DBeaver can allows your data validation team to succeed while implementing Data Cloud
Data Cloud can be an extremely powerful asset to compliment just about any resource, and especially so if you're already integrating into other aspects or products within the Salesforce ecosystem, such as Marketing Cloud or Health Cloud.
The beauty of Data Cloud is its flexibility. Developers can be enabled to integrate data into Data Cloud and can gain a better understanding of new systems.
We can validate, slice & dice and review what's in Data Cloud on the fly with the flexibility of DBeaver and the Data Cloud JDBC driver.

What's DBeaver?
DBeaver is a standalone open source application and uses a specific driver (think plug-in) that can interface with Data Cloud, which can then be queried from using ANSI SQL.
DBeaver interfaces with Salesforce Data Cloud via a JDBC driver. DBeaver isn't a Salesforce product - it's standalone.
DBeaver access to validate sure makes life easier on an implementation. It's recommended to become familiar with DBeaver, as Salesforce documentation is referencing and supporting this solution.
Other tools are able to reference and use the same JDBC driver to interface as well if supported.
DBeaver can help uncover Salesforce Data Cloud data issues quickly
DBeaver enables query access to all of the Salesforce Data Cloud objects, such as DMO (Data Model Objects), CI (Calculated Insights) as well as any DSO (Data Source Objects).
In short, DBeaver becomes the key to unlocking verified, quality data right in Data Cloud.
Data Validation
DBeaver allows you to query the Data Model from within Data Cloud rather nicely.
Not quite comfortable with SQL? You staff can still browse thru mapped objects using Data Explorer in Data Cloud, even sorting.
Troubleshooting
Get a granular look at your data. The JDBC driver that DBeaver uses allows you to inspect your data from within Data Cloud via ANSI-SQL.
DBeaver really shines when you use it to perform complex joins to further dial your data in.
Quality Assurance
Data quality is paramount, especially in Data Cloud. You want your instance to be considered the "source of truth".
For your QA team that's validating your Data Cloud quality, DBeaver will be an invaluable tool.
Quickly identify data ingested via datastreams and DMO (Data Model Objects).
Calculated Insights
Calculated Insights aren't typically one-and-done. Getting a CI (Calculated Insight) right in Salesforce Data Cloud will take some work, and often a few takes to get it just right.
To support this, you can create sample queries in DBeaver. These will allow you to better validate the logic & relationships before creating the actual Calculated Insight from within Data Cloud.
Segmentation
Segment validation takes time!
With a little bit of ANSI SQL, you can validate segment counts by recreating segment logic in DBeaver.
View record details, including the Unified ID to see which records made the segment.
Bulk export
I can't say that there's an easy way to export data out of Data Cloud via the browser for validation.
DBeaver can help, however as you validate your data by allowing you to export data from a DMO or even the results of a complex query. Output the results to a csv file.
JDBC Driver for Salesforce Data Cloud - a zoomed out view
Business value is unlocked in Data Cloud when you can access the data from Data Cloud from just about anywhere, really quickly.

Using DBeaver to access Data Cloud via the JDBC Driver is one method to access this data, especially while in the data validation stage.
The JDBC driver works with more than just DBeaver
Did you know? Tableau and DBeaver can both "reach in" and query Data Lake Objects in Data Cloud with the JDBC driver. Connecting via Tableau? The JDBC driver can be your friend too!
How do we make the safe choice?
With great data comes great responsibility.
Customers increasingly have expectations that businesses managing their data are respecting their consent with care.
In order to align to that expectation, a thorough view of your core systems is paramount to project success.
Gaining a view of current data quality challenges for your business and efforts to improve data quality is required in order to accurately & successfully align and report on the data that will be needed to be ingested & understood by Data Cloud.

Steps required
Download the latest version of DBeaver here
Create a new Database Connection in DBeaver using the Salesforce CDP driver. Confirm the version of the latest Salesforce JDBC driver for Data Cloud here.
Salesforce Connected App - With Salesforce Admin access from within Data Cloud, set up a Connected App. Refer to Set Up Connected App for DBeaver. Note your consumer key & consumer secret. You'll use these credentials in a future step
Make a connection in DBeaver using your Connected App credentials. Note: your password in DBeaver must consist of: (password+security token). Need to reset your Security Token?
In DBeaver, head to Driver Settings > Driver properties. Take the consumer key & consumer secret from your Salesforce Connected App and edit those here:
clientId = consumer key
clientSecret = consumer secret
Test Connection
Reference: https://help.salesforce.com/s/articleView?id=sf.c360_a_dbeaver_to_cdp.htm&type=5
What you'll need to get started:
The ability to create a Connected App from with Salesforce (Admin)
The ability to download and run DBeaver
The ability to download and link the latest JDBC Driver
How can the entire team help?
Combine technical folks ("I can write and interpret SQL") with non-technical staff ("I'm scared of SQL") to help support validation efforts. As you progress, you'll help upskill your staff, while become more comfortaable with how Data Cloud works under the hood.
Validation Method: Data Cloud & DBeaver
I find it helpful viewing the same data from at least 2 different angles. It helps connect the dots a bit more later on.
Try this:
Leverage the browser experience and use Data Explorer, and then compare while browsing for the most recent data for a DLO within DBeaver.
Compare data ingested into a Data Lake Object from within Data Cloud
Compare the same data now by navigating thru the DLO tree within DBeaver
You can enable your team!
Think about the team resources you already have that might be able to help in Data Cloud, with a little support.
What team members do you have that might really like using DBeaver and can help? Think about your staff that might be already writing complex SQL queries. Can they help with validating?
From your marketing team, the folks who might be writing SQL automations will better understand the "why" from Data Cloud.
"I want to refine with SQL" == use DBeaver

Access the same object detail below from DBeaver. Supplement by combining with other objects from within Data Cloud.
Use ANSI-SQL to refine your data with ease.
To doublecheck, use Data Explorer from withing Data Cloud to confirm what you're looking at.
Export queried data to csv for further review & evaluation.
"I'm scared of SQL" == start with Data Explorer

A limitation: You can only view 1 object at a time with Data Explorer. Columns are limited to 10 at any given time.
Use the filter criteria to narrow down the data that you're trying to validate.
Note: Copy SOQL will provide sample code for you, but it's not 100%. You'll need to likely tweak the SQL before pasting it into DBeaver.
What's the business value in having access to DBeaver for Data Cloud?
Your Data Cloud data should be validated & considered a source of truth for your organization
Sample Uses for the JDBC Driver with Salesforce Data Cloud
Calculated Insights - Test & Validate CI queries before deploying. Write your query with DBeaver, confirm results with Business. A Business Data Quality Analyst can help in this task.
Better understand the data you're working with - Salesforce is doing a wonderful effort sharing more information on developing with Data Cloud. The JDBC driver helps to connect with the underlying data that drives Data Cloud. This includes access to mapped data within as Data Lake Objects (DLO) and the underlying data model objects (DMO).
Timing is everything in Data Cloud
It's critical that you can validate when data arrived in Data Cloud to better understand why the data looks like it does while validating, or why the segment count isn't quite what you expected.
How can we confirm from the beginning that we have the data we need to support the Business, and can report that with confidence? How can Data Cloud resources, such as the JDBC Driver be leveraged to better access core Data Cloud capabilities?
It's important during implementation that staff can quickly and easily confirm granular data as it gets mapped into Data Cloud. Accessing the Data Model via DBeaver, or any other tool that can use the JDBC driver will be immensely useful.
How confident are you with your Data Cloud plan?
How can you have better confidence in the underlying data that's qualifying an individual into a Segment, or a Calculated Insight metric?
Our goal: The entire team can gain confidence in the implementation by better understanding the "behind the scenes" view.
Ideal State:
- "We confirm from the beginning that we have 100% of the data we expect in Data Cloud."
- "My team is empowered to understand Data Cloud. I can see how this can fit in to the big picture."
- "We can accurately report our metrics from Data Cloud with confidence. I know where to go to access what I need."
Let's dig in!
DBeaver Reference
Connecting DBeaver to Data Cloud (Salesforce)
JDBC Driver Overview (Salesforce)
Unlock Salesforce CDP with DBeaver (Gina Nichols)
How Isaac can help:
I've worked with all sorts of team structures, in a variety of verticals to help each find success with Salesforce Marketing Cloud & Data Cloud.
In-depth Data Cloud Training
I can help your team become Data Cloud power users with in-depth training on how to use DBeaver & Data Cloud.
Will it work?
I can help your team to validate use case fit and guide to a Proof of Concept.