Question 45

A retail analytics team must ingest data from a **Salesforce** SaaS application and a **SQL Server** transactional database into Unity Catalog managed tables. The requirements are: - The Salesforce ingestion can run on a schedule (every 4 hours is acceptable), and the team wants Databricks to handle incremental reads, schema evolution, and SCD Type 2 history automatically. - The SQL Server data must be ingested **continuously** using change data capture so that row-level inserts, updates, and deletes are reflected in near real time. - Both pipelines must run on **serverless compute**, store credentials as Unity Catalog securable objects, and be governed end-to-end by Unity Catalog (including source lineage). - The team does not want to write or maintain custom Spark code for either source. Which approach satisfies all requirements with the least custom code?

• ABuild two custom Structured Streaming notebooks that use the Salesforce REST API and the SQL Server JDBC driver, and schedule them as Lakeflow Jobs on classic all-purpose compute.
• BUse **Lakeflow Connect managed connectors** — a SaaS connector for Salesforce and a database (CDC) connector for SQL Server — each creating a managed ingestion pipeline governed by Unity Catalog on serverless compute.
• CUse `COPY INTO` for both sources, pointing at staging files exported nightly by an external ETL tool into a Unity Catalog volume.
• DUse Auto Loader (`cloudFiles`) to read Salesforce and SQL Server data directly, since Auto Loader supports incremental reads from any source.