For data analysts, pivot tables are a staple tool for transforming raw data into actionable insights. They enable quick summaries, flexible filtering, and detailed breakdowns, all without complex code. But when it comes to large datasets in Snowflake, using spreadsheets for pivot tables can be a challenge. Snowflake users often deal with hundreds of millions of rows, far beyond the typical limits of Excel or Google Sheets. In this post, we’ll explore some common approaches for working with Snowflake data in spreadsheets and the obstacles that users face along the way.
The Challenges of Bringing Snowflake Data into Spreadsheets
Spreadsheets are incredibly flexible, allowing users to build pivot tables, filter data, and create calculations all within a familiar interface. However, traditional spreadsheet tools like Excel or Google Sheets are not optimized for massive datasets. Here are some challenges users often face when trying to handle Snowflake pivot tables in a spreadsheet:
- Row Limits and Data Size Constraints
- Excel and Google Sheets have row limits (roughly 1 million in Excel and around 10 million cells in Google Sheets), which make it nearly impossible to analyze large Snowflake datasets directly within these tools.
- Even if the dataset fits within these limits, performance can be slow, with calculations lagging and loading times increasing significantly as the spreadsheet grows.
- Data Export and Refresh Issues
- Since Snowflake is a live data warehouse, its data changes frequently. To analyze it in a spreadsheet, users often need to export a snapshot. This process can lead to stale data and requires re-exports whenever updates occur, which can be cumbersome for ongoing analysis.
- Additionally, exporting large datasets manually can be time-consuming, and handling large CSV files can lead to file corruption or data inconsistencies.
- Manual Pivots and Aggregations
- Creating pivot tables on large datasets often requires breaking down data into smaller chunks or creating multiple pivot tables. For instance, if a sales dataset has several million records, users may need to filter by region or product category and export these smaller groups into separate sheets.
- This workaround not only takes time but also risks errors during data manipulation, as each subset must be correctly filtered and organized.
- Limited Drill-Down Capabilities
- While pivot tables in Excel or Google Sheets offer row-level views, managing drill-downs across large, fragmented datasets can be tedious. Users often need to work with multiple sheets or cross-reference with other data sources, which reduces the speed and ease of analysis.
SQL Complexity and Manual Aggregations in Snowflake
For those working directly in Snowflake, pivot table functionality requires custom SQL queries to achieve the same grouped and summarized views that come naturally in a spreadsheet. SQL-based pivoting and aggregations in Snowflake can involve nested queries, CASE statements, and multiple joins to simulate the flexibility of pivot tables. For instance, analyzing a sales dataset by region, product category, and time period would require writing and managing complex SQL code, often involving temporary tables for intermediate results.
These manual SQL processes not only add to the workload of data teams but also slow down the speed of analysis, especially for teams that need quick ad hoc insights. Any adjustments, such as changing dimensions or adding filters, require rewriting or modifying the queries—limiting the flexibility of analysis and creating a dependency on technical resources.
Common Spreadsheet Workarounds for Snowflake Pivot Tables
Despite the challenges, many users still rely on spreadsheets for analyzing Snowflake data. Here are some approaches users often take, along with the pros and cons of each.
- Exporting Data in Chunks
- By exporting data in manageable chunks (e.g., filtering by a specific date range or product line), users can work with smaller datasets that fit within spreadsheet constraints.
- Pros: Makes large datasets more manageable and allows for focused analysis.
- Cons: Requires multiple exports and re-imports, which can be time-consuming and error-prone. Maintaining consistency across these chunks can also be challenging.
- Using External Tools for Data Aggregation, then Importing into Spreadsheets
- Some users set up SQL queries to aggregate data in Snowflake first, summarizing by dimensions (like month or region) before exporting the data to a spreadsheet. This approach can reduce the data size and allow for simpler pivot tables in Excel or Google Sheets.
- Pros: Reduces data volume, enabling the use of pivot tables in spreadsheets for summarized data.
- Cons: Limits flexibility, as each aggregation is predefined and static. Adjusting dimensions or drilling further requires repeating the export process.
- Creating Linked Sheets for Distributed Analysis
- Another approach is to use multiple linked sheets within Excel or Google Sheets to split the data across multiple files. Users can then create pivot tables on each smaller sheet and link the results to a master sheet for consolidated reporting.
- Pros: Allows users to break data into smaller parts for easier analysis.
- Cons: Managing links across sheets can be complex and slow. Changes in one sheet may not immediately reflect in others, increasing the risk of outdated or mismatched data.
- Using Add-Ons for Real-Time Data Pulls
- Some users leverage add-ons like Google Sheets’ Snowflake connectors or Excel’s Power Query to pull Snowflake data directly into spreadsheets, setting up automated refresh schedules.
- Pros: Ensures data stays up to date without manual exports and imports.
- Cons: Row and cell limits still apply, and performance can be an issue. Automated pulls of large datasets can be slow and may still hit performance ceilings.
When Spreadsheets Fall Short: Alternatives for Real-Time, Large-Scale Pivot Tables
While these spreadsheet workarounds offer temporary solutions, they can limit the speed, scalability, and depth of analysis. For teams relying on pivot tables to explore data ad hoc, test hypotheses, or drill down to specifics, spreadsheets lack the ability to scale effectively with Snowflake’s data volume and are often ill-equipped to handle robust governance requirements. Here’s where platforms like Gigasheet stand out, offering a more powerful and compliant solution for pivoting and exploring Snowflake data.
Gigasheet connects live to Snowflake, enabling users to create dynamic pivot tables directly on hundreds of millions of rows. Unlike spreadsheets, which require data replication or exports, Gigasheet accesses Snowflake data in real time, maintaining all established governance and Role-Based Access Control (RBAC) protocols. This live connection ensures that analytics teams don’t need to create or manage secondary data copies, reducing redundancy and mitigating the risks of outdated or mismanaged data.
With an interface tailored for spreadsheet users, Gigasheet combines the familiar flexibility of pivot tables with scalable drill-down functionality, all without requiring SQL or advanced configurations. Gigasheet also integrates seamlessly with Snowflake’s access controls, letting data teams configure user permissions directly within Snowflake or via SSO authentication. This means that only authorized users can view, pivot, or drill down on data as per organizational data policies, aligning with the strictest governance practices.
For analytics and data engineering leaders, Gigasheet provides a solution that preserves data integrity, minimizes the risk of uncontrolled data duplication, and supports real-time analysis at scale. This functionality not only improves the analytical depth but also ensures data compliance, allowing teams to perform ad hoc exploration without sacrificing speed, security, or control.
Final Thoughts
Using spreadsheets to create pivot tables on large datasets from Snowflake is certainly possible, but the process is far from ideal. Workarounds like exporting chunks, aggregating data, and linking sheets can help users tackle Snowflake data, but they come with limitations in data freshness, flexibility, and performance. As Snowflake’s popularity grows, so does the need for tools that bridge the gap between scalable data storage and easy, on-the-fly analysis.
For users ready to go beyond traditional spreadsheets, platforms like Gigasheet offer an efficient way to pivot, filter, and drill down into massive Snowflake datasets in real-time, without manual exports or row limits. So while spreadsheets will always have a place in the data analysis toolkit, there are now more powerful options available for handling big data.