Your data stack, connected. Every driver, one governed model.

For finance teams, the best ETL approach is one finance can own: direct connections to the warehouse, mapping to the chart of accounts, scheduled refreshes, and output in the tools finance already uses. Cube connects to Snowflake, BigQuery, Redshift, Databricks, and SQL databases over JDBC, so most teams do not need a separate ETL tool for FP&A.

ETL transforms data before loading it into the destination. ELT loads raw data first and transforms it inside the warehouse. Cloud warehouses made ELT common for analytics. Cube reads the transformed tables either approach produces.

No. Your pipeline keeps feeding the warehouse for analytics. Cube reads curated tables from the warehouse and maps them to the finance model, so data teams maintain one source and finance owns its own mappings.

Yes. Snowflake, Google BigQuery, Amazon Redshift, and Databricks connect through Cube's warehouse connectors, and any engine with a JDBC driver connects through direct SQL, with no custom build.

Yes. SQL Server, PostgreSQL, MySQL, Oracle, Azure SQL, and any other JDBC-capable database connect the same way a warehouse does: a read-only role, a schedule, and a mapping into your model.

Setup is finance-led with one ask of your data team: a read-only role scoped to the tables finance needs. From there your team reviews the proposed mappings and approves them, with guided onboarding from Cube.

On a schedule you control. Most teams sync daily, and more frequently for metrics that move during the day. You can also trigger a sync on demand.

Yes. Operational drivers from the warehouse land in the same governed model as your accounting actuals, so revenue per unit, cost per customer, and driver-based forecasts read from one place instead of a stitched spreadsheet.

Cube connects to BI both ways. As a source, it pulls the curated data sources and metrics your team already maintains in platforms like Tableau, Looker, and Power BI. As a destination, those same dashboards read the governed numbers Cube maintains, so finance and BI finally agree.

Yes. Cube's patented bi-directional sync fetches live drivers and actuals into your spreadsheets and publishes plan inputs back to the governed model, so the spreadsheet stays a first-class surface rather than a copy.

The operational drivers your plan actually moves on: product usage, pipeline snapshots, billing events, headcount cuts, and unit economics. Cube maps those tables to the accounts and dimensions finance plans by, and leaves the rest of the warehouse alone.

When schemas change or new tables appear, Cube's Data Manager agents propose the mapping with a confidence score, and finance approves or adjusts it. Every change is logged, so the model stays clean and auditable.

An accounting integration pulls structured financials, the general ledger, from systems like NetSuite or QuickBooks. A warehouse connection pulls operational data, like pipeline or usage, from Snowflake or Databricks. Cube supports both, so financial and operational drivers live in one model.