FP&A software for Snowflake, BigQuery and Databricks (2026)

If your metrics already live in Snowflake, BigQuery, Databricks or Redshift, pick an FP&A tool with a native connector to that warehouse. Operational drivers then flow into the plan on a schedule instead of through CSV exports, and the warehouse stays the source of truth.

Pigment, Cube and Drivetrain list connectors for all four, and Aleph connects natively to Snowflake, BigQuery and Databricks. Anaplan covers Snowflake, BigQuery and Databricks through its Data Orchestrator. Adaptive from Workday and Planful have native Snowflake connectors. Datarails, Abacum and Vena need a closer look before you assume the connection is plug-and-play.

Bottom line: Shortlist only tools that document a native connector for the warehouse you actually run, then ask how often it refreshes and who owns the mapping. Aleph is our pick if your team wants warehouse data inside the Excel and Google Sheets models it already uses.

Which FP&A tools connect to Snowflake, BigQuery and Databricks?

Every tool we checked except Vena lists a connector for at least one cloud warehouse, but Snowflake is the only one most of them cover. Coverage of BigQuery, Databricks and Redshift is thinner than most logo walls suggest. Here's what each vendor documents on its own site and help center as of October 2026:

Pricing and connector coverage are indicative as of October 2026. Vendors add connectors often, so confirm with each one before you buy. Oracle's NetSuite Planning and Budgeting and EPM Cloud reach warehouses through the EPM Integration Agent, which runs a SQL query over JDBC and loads the result, so treat them as an integration project rather than a connector. We couldn't confirm current warehouse support for cfo.ai (formerly Runway) or Bob Finance (formerly Mosaic) from their current sites.

Native connector, ETL or file loads: what's the difference?

There are three ways warehouse data gets into a plan, and they differ mostly in who does the maintenance.

1. Native connector. The FP&A tool authenticates to the warehouse with a service account, runs a query or reads a table, and refreshes on a schedule. Finance can change what it pulls without filing a ticket. This is the setup to aim for.
2. ETL, reverse ETL or an integration platform. A tool like Fivetran, Hightouch, Boomi or the vendor's own integration agent moves the data. It works, but the data team now owns a pipeline whose only customer is finance, and schema changes break it quietly.
3. File loads. Someone exports a CSV from the warehouse and uploads it. It's fine for a one-off. As a monthly process it's where version errors come from, because nobody can tell which export the forecast ran on.

A logo on an integrations page doesn't tell you which of the three you're getting. Ask the vendor to show the connection setup screen in the demo, not a slide.

Does the planning tool keep a copy of my warehouse data?

Yes, in almost every case, and that's not a flaw. A planning tool has to calculate scenarios quickly, keep versions, and store assumptions and inputs the warehouse never holds. So it keeps a synced copy of the tables it needs. The question to ask is what that copy means for refresh timing and governance.

Refresh timing. Your plan is only as current as the last sync. A daily schedule is plenty for monthly actuals. Weekly pipeline or usage drivers usually need an on-demand refresh before a forecast review. Check whether a refresh is a button the analyst can press or a job only an admin can rerun.

Governance. The warehouse should stay the source of truth, with the planning tool reading from modeled tables (your dbt marts, say) rather than raw ingestion tables. Use a read-only service account scoped to just those tables. Then check that the planning tool re-applies permissions on its side, since a synced copy doesn't inherit the warehouse's row-level security. Our guide to role-based access controls in FP&A covers what to test.

Lineage. Every number in the plan should trace back to a table and a sync time. If an analyst can't tell when a figure last refreshed, the board pack will eventually carry a stale one.

Zero-copy sharing is starting to change this. Snowflake Secure Data Sharing, BigQuery sharing and Databricks' Delta Sharing (built on the open Delta Sharing protocol) let a consumer read live tables without an extract. Pigment already uses Delta Sharing for Databricks. For now, though, most planning tools still sync.

How to choose FP&A software for a finance data warehouse

Six checks separate a real warehouse integration from a logo:

• Your warehouse and where it's hosted. Snowflake coverage is common. BigQuery, Databricks and Redshift coverage is not, and some connectors only support one cloud (Workday's Databricks connector, for example, is AWS-only).
• Direction. Most connectors only read. If you want plan and forecast data written back so BI can show plan vs actual, ask how, because write-back is often an API or cloud-storage export rather than a connector.
• Query or table. Tools that import from a SQL query are flexible but need someone who writes SQL. Tools that browse tables are easier for finance but depend on clean, modeled tables.
• Refresh controls. Scheduled, on demand, or both, and who can trigger it.
• Limits. Some connectors cap table sizes or row counts on certain warehouses. Ask what happens when a table grows past the cap.
• Where finance works. If your team builds models in Excel or Google Sheets, a web-only tool means rebuilding them. If it already works in a web planner, that's less of a concern.

For the bigger picture of where planning sits next to BI, see FP&A software vs BI tools.

The tools, with a fair best-for for each

1. Aleph

Best for finance teams that want warehouse data next to ERP, CRM and HRIS actuals, inside the Excel and Google Sheets models they already use.

Aleph connects to Snowflake, BigQuery and Databricks as part of 150+ no-code connectors. You can pull warehouse tables without code or query them with SQL, then bring the data into a sheet in one click or on a schedule. Plans, budgets and scenarios push back into Aleph, where they're versioned, with access controls, fine-grained permissions and audit logs on top.

• Strengths: warehouse and ERP data in one model; no model rebuild; analysts control the mapping without waiting on the data team.
• Considerations: built for teams that plan in spreadsheets. If you want a locked-down web planning interface run by a central admin, an enterprise suite fits better.

2. Pigment

Best for larger companies building enterprise-wide models on a warehouse. Pigment documents connectors for all four warehouses. Snowflake imports run from a SQL query with key-pair authentication, and its Databricks connector uses Delta Sharing. Its docs note that Databricks imports are read-only and capped (tables at 1 GiB, views at 100,000 rows), and Snowflake export goes through an API and cloud storage rather than a direct write.

3. Anaplan

Best for enterprises with a dedicated model-builder team. Anaplan Data Orchestrator connects to Snowflake, BigQuery and Databricks, with full, append and incremental loads. Redshift has been announced on Anaplan's community but isn't in its connector documentation yet.

4. Adaptive from Workday

Best for Workday HCM customers whose data sits in Snowflake. Workday added Snowflake to Adaptive Planning's Cloud Data Connect in 2025. Databricks support has been announced in limited availability for Databricks on AWS. We found no native BigQuery or Redshift connector in Workday's docs.

5. Planful

Best for mid-market teams that want structured CPM workflows. Planful's Snowflake connector, announced in June 2026, loads SQL-defined data on a daily, weekly or custom schedule. It's inbound only, and we found no BigQuery, Databricks or Redshift connector.

6. Cube

Best for spreadsheet-first teams that want a lighter planning layer. Cube lists Snowflake, BigQuery, Databricks and Redshift on its integrations page and describes the syncs as bidirectional, refreshing on a schedule the customer sets. We didn't find technical docs behind the integrations page, so ask for a walkthrough.

7. Drivetrain

Best for teams with many source systems who are happy to plan in a web app. Drivetrain's integration docs cover all four warehouses.

8. Datarails

Best for Excel-heavy teams automating an existing consolidation process. Datarails documents a Snowflake connector that syncs query results in, available on its Integrations plan. BigQuery, Databricks and Redshift appear as logos on its integrations page, but we didn't find docs for them.

9. Abacum

Best for growth-stage teams that want a collaborative web planner. Abacum has integration pages for Snowflake, BigQuery and Databricks, though it doesn't publish how the sync works.

10. Vena, Jedox, OneStream and Oracle

• Vena: best for Excel-based CPM where the ERP, not the warehouse, is the main source. We found no warehouse connector on its site; data comes in through flat files or its API.
• Jedox: best for multi-entity planning with heavy ETL. It has a read-and-write Snowflake connection on a premium license and reaches other warehouses through its APIs.
• OneStream: best for enterprise close and consolidation. Its June 2026 Snowflake connector mainly sends governed financial data into Snowflake for analytics.
• NetSuite Planning and Budgeting / Oracle EPM: best for NetSuite shops that want one vendor. Warehouse data comes in through Oracle's EPM Integration Agent, which is a configuration project, not a connector.

For the broader market beyond warehouse support, see our roundup of the top FP&A software for 2026.

Do you need a finance data warehouse for FP&A?

Usually not. If your actuals live in the ERP, CRM, billing system and HRIS, an FP&A tool with native connectors to those systems covers planning without a warehouse. Building one just for finance means maintaining pipelines you didn't need, which we cover in the modern finance tech stack for SaaS companies.

The warehouse earns its place once product, usage or operational data drives the forecast and the data team already runs Snowflake, BigQuery or Databricks. Then connect the planning tool to it rather than exporting from it. Billing data is a common example: Stripe can deliver into the warehouse too, as covered in our guide to FP&A tools that integrate with Stripe.

Plan on your warehouse data with Aleph

Aleph connects to Snowflake, BigQuery and Databricks alongside your ERP, CRM and HRIS, and puts that data into the Excel and Google Sheets models your team already runs. Drivers update on a schedule, the warehouse stays the source of truth, and finance owns the mapping. Book a demo to see it on your own warehouse.