Aviato to Databricks integration — real-time data sync

Yes. Stacksync provides a managed, real-time integration between Aviato and Databricks — Aviato is a read-only source, so data flows from it into the other system: authenticate both systems, choose the objects to sync, map fields visually, and changes propagate in milliseconds — no code required.

On the Aviato side: Acquisition / Exit Event, Company, Person, Funding Round, plus custom fields where Aviato exposes them. On the Databricks side: Volumes, SQL Warehouses, Change Data Feed, Catalogs. Stacksync auto-detects both schemas and converts types between the two systems.

Aviato is a read-only source, so this integration runs one-way: Stacksync reads from Aviato in real time and delivers into Databricks. Field mapping and monitoring work the same as for two-way pairs.

Common patterns for Aviato and Databricks: Where Aviato accepts updates: operational write-back; History that outlives the tool; Analytics on Aviato's data. Segments, scores, or reference values computed in Databricks sync back onto records in Aviato, putting analysis where the work happens.

Aviato: REST API returning JSON, with search/filter endpoints for querying company and people records. Authentication: API key passed on each request. Databricks: SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution. Authentication: Personal access tokens or OAuth machine-to-machine credentials for service principals. Stacksync manages authentication, retries, and rate limits on both sides.

Aviato: Aviato's data model centers on private-market entities — companies, the people behind them, and the funding events connecting them — rather than user-created business records. Databricks: Delta Lake's Change Data Feed records row-level inserts, updates, and deletes, enabling incremental sync without full scans. Stacksync's field mapping accounts for these differences between Aviato and Databricks without custom code.