4 posts tagged with "Salesforce"

A Salesforce Google Sheets integration can send CRM data to a spreadsheet for reporting and return reviewed changes to Salesforce. The safest design does not let both systems overwrite the same fields whenever they want. It keeps a stable Salesforce ID on every row, limits which fields Sheets can change, and orders the two sync directions so an outbound refresh cannot erase an unprocessed edit.

This guide compares the main connector, automation, and API approaches. It then shows how to build a scheduled two-way Salesforce and Google Sheets workflow with Supaflow using separate outbound and writeback pipelines.

Salesforce Marketing Cloud Engagement stores useful analytics data across Data Extensions, subscribers, sends, journeys, and system Data Views such as _Sent, _Open, and _Click. Moving that data into Snowflake gives marketing, revenue, and data teams one place to analyze engagement alongside CRM, product, and transaction data.

The timing matters. Salesforce keeps only the most recent 180 days in Automation Studio Data Views. If you need a durable history, start exporting before older engagement records age out.

This guide shows how to export Salesforce Marketing Cloud data with Supaflow's SFMC-to-Snowflake connector, including the Installed Package permissions, the connector settings visible in the app, the objects to select, and the checks to run after the first load.

A lead score trapped in a warehouse is just a number. The useful version appears on the Salesforce Lead record while a sales rep is deciding who to call next.

Supaflow Python tasks close that loop. You can ingest CRM and behavioral data into Snowflake, apply transparent scoring rules with Python and pandas, write the results to an activation table, and then sync those fields back to Salesforce. The same output can also feed a Salesforce Marketing Cloud Data Extension for a high-intent nurture journey.

A Supaflow orchestration turns those pipelines and tasks into one dependency-aware workflow, so activation cannot start before ingestion and scoring finish. This guide builds that end-to-end data pipeline.

Export Salesforce to S3 in 10 Minutes​

Export Salesforce to S3 and query it in Athena — no ETL tools, no long‑running servers.

This open‑source, serverless pipeline uses AWS Lambda, Step Functions, and the Salesforce Bulk API 2.0 to export every Salesforce object to S3. After each run, it automatically updates the AWS Glue Data Catalog, so your data is immediately queryable in Athena.

After a single deployment, you can query your Salesforce data directly in Athena:

SELECT * FROM salesforce_export.account_raw LIMIT 10;