)
Written by
Table of Contents
“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
NetSuite transaction data involves complex relationships between headers, line items, subsidiaries, departments, classes, and custom fields, making it challenging to design efficient Snowflake table structures that maintain data integrity while supporting analytics queries.
Here’s how to simplify NetSuite transaction data handling with flattened extraction methods that work better with Snowflake’s columnar structure.
Flatten complex transaction relationships for optimal Snowflake design using Coefficient
Coefficient simplifies NetSuite transaction data handling for Snowflake integration. Instead of dealing with NetSuite ‘s normalized transaction structure with separate header and line tables, you can extract flattened transaction data that works better with Snowflake’s columnar architecture.
How to make it work
Step 1. Extract flattened transaction data.
Instead of dealing with NetSuite’s normalized transaction structure with separate header and line tables, Coefficient can extract flattened transaction data that includes both header and line item information in a single dataset, ideal for Snowflake’s columnar structure.
Step 2. Pull multi-level relationships in single extracts.
Coefficient’s Records & Lists imports can pull transaction data with related customer, item, and subsidiary information in a single extract, eliminating the need for complex joins in your Snowflake table design.
Step 3. Handle custom transaction fields automatically.
NetSuite transactions often include extensive custom fields at both header and line levels. Coefficient automatically includes these custom fields in extracts, ensuring your Snowflake tables capture all business-critical transaction data.
Step 4. Use SuiteQL for complex transaction queries.
Use Coefficient’s SuiteQL Query feature to create sophisticated transaction extracts that join multiple NetSuite tables like Transaction, TransactionLine, Customer, and Item with custom aggregations and filtering, producing denormalized datasets optimized for Snowflake analytics.
Step 5. Handle varying transaction types consistently.
Whether extracting sales orders, invoices, purchase orders, or journal entries, Coefficient handles the varying field structures across different NetSuite transaction types, allowing consistent Snowflake table designs.
Step 6. Structure exports to match Snowflake schemas.
Use the drag-and-drop column ordering feature to structure transaction data exports to match your Snowflake table schemas exactly, reducing transformation requirements and improving data loading performance.
Optimize your transaction data architecture
Coefficient’s flattened extraction methods eliminate the complexity of NetSuite transaction relationships, making Snowflake table design straightforward and analytics-ready. Start optimizing your transaction data today.
