Integrating Stigg into your existing data stack just got significantly faster and more flexible. We’re excited to introduce our new, upgraded data export pipeline - expanding support far beyond Snowflake and BigQuery to nearly 20 new destinations, paired with granular control over sync schedules, entity selection, and monitoring!

At Stigg, we believe you should always have full, seamless ownership of your data. This update makes it easier than ever to bring Stigg data directly into your warehouse, data lake, operational database, or storage layer without complex custom ETLs.

🆕 What’s new?

1. 22 New Supported Destinations

You are no longer limited to Snowflake and BigQuery. Stigg now supports native exports to:

• Data Warehouses & Lakes: Databricks, Amazon Redshift, Amazon Redshift Serverless, ClickHouse, Amazon Athena, SingleStore, Delta Lake
• Relational & NoSQL Databases: PostgreSQL, MySQL, Microsoft SQL Server, Oracle, Amazon Aurora (PostgreSQL & MySQL), MongoDB, MotherDuck
• Cloud Storage & Files: Amazon S3, S3-compatible storage, Google Cloud Storage (GCS), Azure Blob Storage, Apache Iceberg, SFTP, Google Sheets

2. Flexible Sync Scheduling & Custom Frequencies

Take control over when your data updates. Choose your sync frequency (every 1-24 hours), or trigger an immediate manual sync at any time with a single click in the Stigg app.

3. Entity Customization

Choose exactly which Stigg entity groups sync to your destination (Customer Data, Product Catalog, and Usage Tracking). Keep your schemas clean and export only the data your team needs.

4. Real-Time Sync Monitoring & Visibility

Track the status, progress, and history of every sync run in real-time without refreshing the page:

• Determine exact timestamps and status (Successful vs. Failed) for the last sync.
• Drill down into error logs, sync durations, total rows transferred per entity, and whether the run was automated or triggered manually.

💡 Why it matters

• Seamless Stack Integration: Connect Stigg directly to your existing data infrastructure without needing intermediate data pipelines.
• Operational Control: Tailor export workloads to fit your warehouse costs and reporting cadences by selecting specific entities and scheduling custom sync frequencies.
• Troubleshooting Transparency: Instantly verify sync health and diagnose errors directly within the Stigg dashboard without digging through external system logs.

⚒️ Get started

1. In the Stigg app, navigate to the Integrations > Apps section and click on + Add.
2. Under the Data export section, select your desired destination, and follow the setup instructions.
3. Select the Stigg entities you want to export and set your preferred sync schedule.
4. Click Sync Now to run an initial sync immediately and view real-time progress in the history tab!

For detailed setup instructions and schema details, check out our Data Export Documentation.

📦 Availability

• New Customers: Available immediately to all new customers on the Scale and BYOC plans.
• Existing Customers: Our team will be reaching out and working closely with existing Snowflake and BigQuery integration users to ensure a smooth migration to the new pipeline.

🍿 See it in action