Sigma Computing is a cloud-native analytics platform that provides spreadsheet-like data exploration capabilities but currently does not support direct connections to AWS Athena which powers Tallyfy Analytics.
Connection limitations and alternatives
Sigma Computing can’t connect directly to Tallyfy Analytics. The root cause is an architectural mismatch - Sigma requires persistent data warehouse connections, while Tallyfy Analytics runs on AWS Athena’s serverless model. These two approaches are fundamentally incompatible.
Sigma expects an always-on database that maintains open connections, provides dedicated compute, and exposes standard JDBC/ODBC endpoints with persistent session management. That’s how traditional data warehouses like Snowflake, Redshift, and BigQuery work - and those are exactly what Sigma supports.
AWS Athena works differently. It’s a serverless query engine1 with no persistent database servers. Compute resources spin up per query, data lives as Parquet files in S3 rather than in database tables, and metadata sits in a separate AWS Glue Data Catalog. There’s no always-on connection to maintain.
The incompatibility shows up in several places:
- Connection persistence - Sigma keeps database connections alive throughout your analysis session. Athena creates and drops connections per query.
- Driver compatibility - Sigma’s connectors expect traditional warehouse JDBC drivers. Athena has JDBC drivers, but they follow completely different usage patterns.
- Metadata discovery - Sigma looks for standard database metadata APIs. Athena uses AWS Glue Data Catalog instead - a separate service with different interfaces.
- Query optimization - Sigma optimizes queries for traditional databases. Athena’s query planner works against S3-based data with different optimization rules.
Snowflake reads your Tallyfy Analytics Parquet files directly from S3 - no ETL or data duplication needed. Then you connect Sigma to Snowflake using its native connector. See our dedicated Snowflake setup guide for the complete configuration.
- Create a Snowflake warehouse - Ideally in us-west-2 for best performance, since that’s where Tallyfy Analytics data is stored
- Create an external stage - Point Snowflake to your Tallyfy Analytics S3 location using your existing AWS credentials
- Query directly - Snowflake reads the Parquet files natively
- Connect Sigma - Use Sigma’s native Snowflake connector
This approach reads your existing S3 data without duplication, works alongside your existing Athena/Power BI/Tableau connections, and scales automatically. The tradeoff is Snowflake licensing costs (usage-based) and the fact that all fields are stored as STRING, so you’ll need to cast types in queries.
If you already have Amazon Redshift, use Redshift Spectrum to query your S3 data directly, then connect Sigma to Redshift using its native connector.
- Configure Spectrum on your existing Redshift cluster to read S3 data
- Connect Sigma to Redshift using the native connector
- Query Tallyfy data through Redshift
No data duplication needed, and query performance is strong for complex analytics. You will need to manage a Redshift cluster, which adds AWS costs.
If you’d rather stick with Athena directly, these tools support it natively:
| BI tool | Connection method | Strength |
|---|---|---|
| AWS QuickSight | Native (no drivers needed) | Built for AWS, serverless, pay-per-use |
| Tableau | Amazon Athena JDBC driver | Advanced visualizations, enterprise governance |
| Power BI | Amazon Athena ODBC driver | Excel-like experience, Microsoft tools |
All three have dedicated Tallyfy Analytics setup guides available.
If you have development resources, you can build a bridge using AWS SDKs to query Athena programmatically, transform results, cache them in a Sigma-supported database, and schedule syncs. This gives you full control but requires significant development and ongoing maintenance investment.
| Your priority | Best option |
|---|---|
| Keep using Sigma | Snowflake or Redshift Spectrum bridge |
| Lowest cost | Power BI or Tableau with Athena directly |
| All-AWS stack | QuickSight with Athena |
| Full customization | Custom integration |
Possibly. Sigma has been expanding its connector list - they recently added Azure SQL Database and SQL Server 2022. Monitor their product roadmap and submit feature requests if Athena support matters to your team. In the meantime, Power BI and Tableau connect to Tallyfy Analytics without any workarounds.
Sigma > Alternatives for spreadsheet-style analytics
Since Sigma Computing requires persistent database connections that are incompatible with Tallyfy Analytics’ serverless Athena architecture you can achieve similar spreadsheet-style data exploration using Power BI with Athena ODBC drivers or AWS QuickSight with native Athena integration or Tableau for advanced visualization or by bridging through Snowflake or Redshift to use Sigma itself.
Connect Snowflake to your Tallyfy Analytics data stored in Amazon S3 by creating a storage integration and external stage using the AWS credentials provided during your analytics setup to query process and member data directly alongside your existing Athena access.
Snowflake can directly query Tallyfy Analytics data stored as Parquet files in Amazon S3 by creating an external stage with your AWS credentials and then running SQL queries against process tasks members and form field data without any ETL pipeline or data duplication.
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A serverless query engine runs SQL queries without provisioning servers - you pay per query rather than for always-on infrastructure ↩
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