10 posts tagged with "Snowflake"

Moving the AWS Cost and Usage Report to Snowflake answers a question that Cost Explorer cannot answer alone: does the revenue from each customer justify the cloud cost required to serve them?

The practical architecture is straightforward. Export AWS Cost and Usage Report 2.0 (CUR 2.0) to Amazon S3 as Parquet, load the files into Snowflake, sync revenue data from Stripe, and add customer and segment context from Salesforce. A shared customer identity model then turns three disconnected systems into customer-level cost, revenue, and margin analysis.

AWS Data Exports -> S3 Parquet ---\
                                    \
Stripe ------------------------------> Supaflow -> Snowflake -> dbt -> Margin dashboard
                                    /
Salesforce ------------------------/

This guide explains the pipeline, the allocation decisions that matter, and the Snowflake model needed to calculate gross margin without hiding shared or unattributed costs.

In other words, moving AWS billing data to Snowflake is the data-engineering foundation for cloud unit economics and customer profitability analysis.

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.

Moving Google Drive files into Snowflake can mean several different things: loading a folder of recurring CSV exports, syncing every worksheet in a set of Google Sheets, replacing one named range, or running a one-time import. Those workloads should not share one blanket recommendation.

This guide compares Supaflow, Fivetran, Hevo, Airbyte, manual Snowflake loads, and a custom Google API pipeline. It then walks through a Supaflow setup using CSV files; the same pipeline workflow applies to TSV, Excel (.xlsx), and native Google Sheets sources.

Need MySQL data in Snowflake for analytics, reporting, or a migration? Supaflow handles the initial load and keeps new and updated rows in sync without a custom export job. This guide walks through the setup, the first sync, and the checks that confirm your Snowflake tables match the source.

Before you start, decide how each table records changes and how you want to handle deleted rows. Supaflow uses a date, datetime, or timestamp column to find inserts and updates. It does not read the MySQL binary log, so hard deletes require a separate approach.

For the shorter product and capability overview, see the MySQL to Snowflake connector page.

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.

Need to move Redshift tables into Snowflake? Supaflow can replicate Redshift tables into Snowflake without a custom export script, copy job, scheduler, or table-by-table loader.

This guide walks through the full setup end to end: create the Snowflake destination, create the Redshift source, build the pipeline, run the first sync, and verify the data in Snowflake.

For the shorter product overview before the walkthrough, see the Redshift to Snowflake migration page.

Oracle Transportation Management integration is one of the more deceptive data engineering problems in the logistics stack. OTM exposes a well-documented REST API and sync looks straightforward on paper -- until you run it against real data and discover that the metadata catalog returns 400 on half the tables you need, "empty" timestamps arrive as literal 0, and sync-mode responses silently truncate at 1 MB.

This guide walks through how OTM data integration actually works in production: the objects that matter, the five quirks that break naive pipelines, the right way to handle incremental sync with OTM's server-side clock, and how to move OTM data into Snowflake or any cloud warehouse reliably.

Need to replicate your SQL Server tables into Snowflake? Supaflow's SQL Server connector supports two query modes: Standard for simple cursor-based sync, and Change Tracking for capturing every insert, update, and delete -- including hard deletes that standard mode can never detect.

This guide walks through setting up a SQL Server to Snowflake pipeline with Change Tracking enabled, so your Snowflake tables stay in sync with every change made in SQL Server.

Most ETL tools move your data through a third-party cloud before loading it into Snowflake. Supaflow takes a fundamentally different approach: the pipeline engine runs inside your Snowflake account as a Snowflake Native App. Your data never leaves Snowflake.

We are now live on the Snowflake Marketplace -- install directly from Snowsight and start running pipelines in under 20 minutes.

Introducing Supaflow​

A single platform to replace multiple stitched-together pipeline tools.

Modern data teams are drowning in vendor sprawl. The average company uses four to five different tools just to move data from Salesforce to Snowflake—and back again. Each vendor adds another contract, another integration to maintain, and another potential point of failure.

That's why we built Supaflow: a unified data movement platform that collapses ingestion, transformation, activation, and orchestration into one secure, flexible solution—without your data ever leaving your environment.