Data sourced from Dakota Marketplace, the global LP and GP intelligence platform trusted by thousands of investment professionals. Learn More | Book a Demo
Snowflake started as a data warehouse built for structured data, fast SQL, and BI dashboards, and it’s still where most investment firms run their analytics. It’s also the platform we hear about most from the firms we work with. But a warehouse is only as good as what goes into it, and a Snowflake account without clear rules gets expensive and hard to trust.
We work with investment firms connecting Dakota data to Snowflake every day, and the same patterns keep showing up. The teams that get the most from the platform keep their data clean, control who sees what, and stop building pipelines for data they could simply receive.
In this article, we’ll cover eight Snowflake best practices for investment firms, from data quality and access control to bringing in external data and keeping costs in check.
8 Snowflake Best Practices for Investment Firms
1. Use Snowflake for What It Does Best
Snowflake is strongest in the hands of analytics teams. Our shorthand: Snowflake is for the analytics team, and Databricks is for the data science and engineering team.
That makes Snowflake a natural home for:
- Fundraising dashboards and pipeline reporting built on clean, structured data
- Regulatory reporting and compliance, where several teams need to work from one governed dataset
- SQL-based research teams that want fast, reliable queries without heavy engineering
Many of the largest firms run both platforms, with analytics in Snowflake and engineering in Databricks. If that’s your setup, decide which team owns which data so the two environments don’t drift apart.
2. Clean Data Before It Lands
Snowflake follows a schema-on-write model: data is cleaned and organized before it goes in. That’s what makes queries fast and reports reliable, and it’s also why skipping that step causes problems later.
Our rule of thumb: data that is already structured and ready to use can go straight to a warehouse, while data that needs more cleaning and structuring goes to a lake first. Loading messy data into Snowflake and fixing it in every downstream query creates conflicting numbers across reports.
Set naming conventions and data standards early, and agree on which tables are the source of truth for each team.
3. Govern by Access: Control Who Sees What
Snowflake’s governance is built around what each user has access to. Used well, that lets portfolio managers, risk, and compliance teams work from the same data without stepping on each other’s toes.
The best practice is to define roles by team and function, then grant access to roles rather than individuals. When a new dataset arrives, it should map to existing roles on day one, not trigger a round of one-off permission requests.
4. Bring External Data In Through Private Sharing, Not Pipelines
Third-party data usually arrives the hard way: file transfers, ETL jobs, and custom code that someone has to maintain. Snowflake’s Private Sharing skips all of that by delivering a provider’s data directly into your account, ready to query.
When a provider offers a private share, ask for one. A standard marketplace listing comes with a predefined set of records that may or may not fit your firm. A private share is scoped to what you actually need. For example, a firm that only cares about RIAs can receive just RIA accounts and contacts, and nothing else. That’s how our Snowflake integration works.
An API still makes sense when you’re pushing data into an application your team is building. But if your reporting already runs in Snowflake, a share drops the data into the environment you’ve built instead of asking you to build around the data.
Want to see what a tailored Dakota share would look like in your Snowflake account? Book a demo to scope it.
5. Test in a Demo Environment Before Production
Before connecting a new data source to your production account, see how it behaves first. Check how the data appears, what the tables look like, and whether your existing queries and joins work against it.
Ask providers whether they offer a demo environment. We can set up demo users in our own Snowflake instance, so your team can walk through the installation, explore the data, and test queries before connecting production.
6. Keep Shared Data View-Only and Build in Your Own Databases
Shared data from a provider should stay exactly as the provider delivers it. With Private Sharing, you get view access to the shared data, which protects your team from accidentally changing the source.
The best practice is to query the shared database, join it to your own tables, and write any enriched or transformed results to databases your firm owns. Your team can join allocator data directly to your CRM records and fundraising history, while keeping a clean line between what came from the provider and what your team built on top of it.
7. Keep Your Data Portable With Apache Iceberg
The longtime criticism of Snowflake was lock-in: data that was hard to pull out and use with other tools. Snowflake answered that by adopting Apache Iceberg, an open format that gives firms more flexibility to use their data across other platforms.
If your firm runs both Snowflake and Databricks, or expects to add tools later, consider where an open format makes sense. It keeps your options open without forcing a migration down the road.
8. Watch the Simplicity Premium
Cost comparisons between Snowflake and Databricks are everywhere, and we’d be cautious about taking either vendor’s numbers at face value. What drives the bill is how much you use the platform and how well the deployment is optimized.
Snowflake tends to look simpler and cheaper for straightforward BI at smaller scale, but that ease can carry a simplicity premium as usage grows. In both platforms, an unoptimized deployment gets expensive quickly. Review which queries and jobs run, how often, and on what compute, and retire the ones no one uses.
Frequently Asked Questions
Is Snowflake or Databricks better for investment firms?
It depends on who will use it. Snowflake fits analytics teams that want clean, structured data for reporting. Databricks fits data science and engineering teams running machine learning on large datasets. Many of the largest firms use both.
How does Snowflake handle data governance?
Snowflake’s governance centers on what each user has access to. Firms define roles by team or function so portfolio managers, risk, and compliance can all work from the same data with the right level of access.
What’s the best way to get third-party data into Snowflake?
Through Private Sharing, when the provider supports it. A private share delivers the provider’s data directly into your account, with no ETL pipeline or custom code.
Is Dakota Marketplace data available in Snowflake?
Yes. We deliver Dakota’s allocator account and contact data to Snowflake as a private share tailored to your firm. Send us your Data Sharing Account Identifier, then install the share through Private Sharing.
Bring Dakota Data Into Your Snowflake Account
Dakota Marketplace’s 250,000+ allocator accounts and 386,000+ verified contacts can be live in your Snowflake account as a private share, ready to query with the SQL your team already writes. Scope it to the allocator types, geographies, and contacts your team targets, then build on it with the dashboards and reports you already use.
Want to see Dakota data running in Snowflake? Book a demo!
