Data sourced from Dakota Marketplace, the global LP and GP intelligence platform trusted by thousands of investment professionals. Learn More | Book a Demo
Getting third-party data into a Databricks workspace usually starts with an ETL pipeline, custom code, and weeks of developer time before anyone runs a single query. For investment firms whose data science and engineering teams already work in Databricks, that build is the bottleneck between buying data and using it.
Dakota Marketplace’s Databricks integration removes that step. Dakota’s 250,000+ allocator accounts and 386,000+ verified contacts arrive in your workspace as a private share, mounted as a catalog and ready to query next to your own data.
In this article, we’ll explain what the integration is, how a private share compares with a marketplace listing and the Dakota API, which teams it fits best, and how to connect in two steps.
What Is a Databricks Integration With Dakota Marketplace?
A Databricks integration with Dakota Marketplace is a private data share that delivers Dakota’s allocator account and contact data directly into your firm’s Databricks workspace. It runs on Delta Sharing, Databricks’ native protocol for sharing data across organizations, so there is no file transfer, no pipeline to maintain, and no code to write.
Once the share is live, Dakota data appears in your workspace as its own catalog. Your team queries it like any other table:
- Allocator account and contact data, scoped to the segments your firm cares about
- Mounted as a catalog in your workspace, alongside your CRM and internal data
- Read-only and ready to query in SQL, Python, or notebooks
- Governed in Unity Catalog, so your administrators control who sees it
The practical result: your data scientists can join Dakota’s allocator data to your own pipeline, fundraising, and CRM records without waiting on an engineering sprint.
Private Share vs. Marketplace Listing vs. the Dakota API
Firms can get Dakota data into Databricks three ways, and the private share is the only one that is both tailored and build-free.
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Private Share
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Marketplace Listing
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Dakota API
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What You Receive
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A dataset scoped to your firm under an agreed scope
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A predefined set of accounts, contacts, and records
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Whatever your developers request and build for
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Setup
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Send a Sharing ID, mount a catalog
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Install from the marketplace
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Custom code built end to end
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Developers Needed
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No
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No
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Yes
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Fits Your Use Case
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Yes, by design
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Maybe
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Yes, if you build it
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The tailoring matters. As Dakota’s team explained during our Databricks vs. Snowflake webinar, a firm that only cares about RIAs can receive just RIA accounts and contacts, and nothing else. The same private share model works on Snowflake.
The API remains the right tool when you need to push Dakota data into an application your team is building. But if your workflows already run in Databricks, a share drops the data into the environment you’ve built instead of asking you to build around the data.
Curious what a tailored Dakota share would include for your team? Book a demo to scope it.
Who the Databricks Integration Is Built For
The Databricks integration fits investment firms whose data science, engineering, or ML teams already work in Databricks. In Dakota’s webinar, CTO Priya Stanley put the split simply: Snowflake is for the analytics team, and Databricks is for the data science and engineering team.
Databricks started as a lakehouse built on Apache Spark, designed for large, messy datasets with machine learning running directly on top. That makes it a natural home for work like:
- Allocator scoring and targeting models that combine Dakota data with your own fundraising history
- Risk and model governance, where Unity Catalog traces exactly what data fed a model and what came out
- Quant and research teams running heavier ML workloads rather than SQL-only analysis
Many of the largest firms run both platforms, with engineering in Databricks and analytics in Snowflake. Dakota supports both, so the data can follow whichever team needs it.
How to Connect Dakota to Databricks in Two Steps
Setup takes two steps on your side, with no pipelines to build. Your workspace needs Unity Catalog enabled before you start.
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Share your Sharing ID. In Databricks, open Catalog, click the gear icon, and select OpenSharing. Click Sharing ID in the top-right corner to copy it, then send it to Dakota along with the users or groups who should have access. Your Sharing ID identifies your Unity Catalog metastore. It is not a username, password, or account ID.
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Mount the Dakota catalog. Once Dakota confirms your share is ready, open Catalog, click Shares received, and select the DAKOTA share. Click Create catalog, give it any name, and confirm. Your Account and Contact data is now ready to query, read-only, in your workspace.
From there, your Databricks administrator can grant access to the new catalog for the right internal users or groups.
Frequently Asked Questions
What data does the Databricks integration include?
Dakota’s allocator account and contact data, scoped to your firm through a private share. Dakota Marketplace covers 250,000+ allocator accounts and 386,000+ verified contacts, and your share can include all of it or only the segments you need.
Do I need developers to set it up?
No. You copy your Sharing ID from Databricks, send it to Dakota, and mount the share as a catalog once it’s ready. There is no ETL pipeline or custom code involved.
What do I need in Databricks before I start?
Your workspace needs Unity Catalog enabled. Delta Sharing runs through Unity Catalog, and your administrator uses it to control who can access the Dakota catalog.
Can I edit the Dakota data in my workspace?
The shared data is read-only. Your team can query it, join it to your own tables, and write results to your own catalogs.
How is this different from the Dakota API?
The API requires developers to build and maintain custom code end to end. A Databricks share places Dakota’s data directly inside the platform your team already uses.
Is Dakota data also available in Snowflake?
Yes. Dakota offers the same private share model in Snowflake, and many firms use both platforms.
Put Dakota Marketplace Data to Work in Databricks
Dakota Marketplace’s 250,000+ allocator accounts and 386,000+ verified contacts can be live in your Databricks workspace in two steps. Scope your share to the allocator types, geographies, and contacts your team targets, then query it next to your own fundraising data.
Want to see Dakota data running in Databricks? Talk to an expert.
