Connect Salesforce to Amazon Redshift — Sales Pipeline Data Warehouse

Log into the Supermetrics Hub, create a new data transfer, select Salesforce as your source and Amazon Redshift as the destination. Authorize your Salesforce account, provide your Redshift cluster endpoint and credentials, choose your target schema and table naming conventions, select the fields you need, set a schedule, and start the transfer. Supermetrics loads data using optimized S3 COPY commands — the same high-throughput path AWS recommends for bulk ingestion — so no custom ETL pipelines, AWS Glue jobs, or staging buckets are required.

Supermetrics is SOC 2 Type II certified and GDPR compliant. All Salesforce credentials are encrypted at rest and in transit using TLS 1.2+. Data flows directly from the Salesforce API to your Redshift cluster — Supermetrics never stores your marketing data on its own servers. On the AWS side, your VPC isolation keeps the cluster off the public internet, IAM policies restrict which roles can access the data, and S3 server-side encryption protects any intermediate staging objects. You retain full control of your security perimeter.

Absolutely — cross-source analysis is where Redshift shines in the AWS ecosystem. Once Salesforce data lands in your cluster, you can JOIN it with any other table in the same schema. Need to enrich it with raw event logs or clickstream data sitting in S3? Redshift Spectrum lets you query those external files directly without loading them, so your cluster stays lean while your analysis spans petabytes. Supermetrics supports 170+ data sources that all land in the same Redshift schema, and you can optimize JOIN performance further with DISTKEY and SORTKEY settings on frequently queried columns.

All standard Salesforce reporting fields are available, including Opportunity count, Amount won, Hit rate, Average amount per opportunity, Age (days), Leads count, and many more. You select exactly which metrics and dimensions to transfer during setup, and you can add or remove fields at any time without losing historical data already loaded into Redshift. Because Redshift uses columnar storage, queries that scan only a few selected metrics out of a wide table are extremely fast — the engine skips unneeded columns at the disk level, reducing I/O and keeping your analytics snappy.

Data freshness depends on your transfer schedule. Supermetrics supports hourly, daily, or weekly transfers into Redshift. Most teams schedule daily transfers so yesterday's final numbers are ready each morning. Incremental loading means only new and changed records are written via optimized S3 COPY commands, keeping cluster load minimal. If you define a SORTKEY on your date column, Redshift can skip entire disk blocks during time-range queries, making your morning dashboards in QuickSight or Tableau load in seconds.

The connector supports opportunities, leads, accounts, contacts, campaigns, cases, and custom objects. Standard and custom fields are available.

Yes. Incremental loads capture deal stage changes, so you can build pipeline progression and stage duration analyses with SQL.