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We're consuming the Genesys Cloud Data Extraction API and wanted to ask the community how you approach consuming and processing this data, given the volume it generates.

For context on volume: we poll the API every 10 minutes, which generates roughly 260 GB per month spread across up to 1,440 Parquet files a day (individual files can be up to 1 GB), with 26 active schemas. Event latency is around 5 to 10 minutes, with a 72-hour retention window on the API side.

Do you apply any transformation to this raw data before using it for reporting (aggregations, joins across schemas, deduplication), or do you query it directly as it lands? If you do transform it, what tools or approach do you use (Spark, scheduled SQL, something serverless), and how often do those transformations run?

On the architecture side, does your pipeline stop at extraction and raw storage, or do you have an additional processing/curation layer before data reaches dashboards? What would you recommend for someone who currently only has extraction plus raw storage and wants to move toward data that's more "ready to consume"?

I have a scenario very similar to the one you described; I'll follow the discussion to pick up some tips!

You may also want to check the Genesys Developer Center. While it doesn't prescribe a specific analytics architecture, it includes the Data Extraction API documentation, guides, and developer resources that can help when designing your ingestion pipeline. https://www.genesys.com/capabilities/development 

Hi Danna,

This is a useful question, particularly at the volumes you are processing.

The Lakehouse Extraction API is designed to provide scalable access to detailed Genesys Cloud data, but it does not prescribe a single downstream analytics architecture. In general, we would recommend treating the extracted Parquet files as a foundational data source rather than as reporting-ready datasets. We say they are data warehouse friendly but ingestion still needs to be carefully planned for end consumption. 

Most mature implementations introduce some form of processing and curation between extraction and consumption. This allows customers to apply their own business logic, manage duplicate or late-arriving records, combine related datasets and create structures optimized for their reporting and analytics requirements.

The precise architecture will depend on the customer's existing data platform and operational needs. Some organizations may use a lakehouse or data warehouse transformation layer, while others may use managed data engineering or serverless services. The important principle is to separate:

• Reliable extraction and retention of the source data
• Transformation and governance of that data
• Consumption through reports, dashboards and analytical applications

Querying the extracted files directly can be appropriate for exploration, validation or lower-volume use cases. At larger scale, however, a curated layer will generally provide better performance, consistency and governance than placing transformation logic independently within each dashboard or reporting tool.

Customers should also ensure that their ingestion process accounts for the API's retention period through appropriate monitoring, retry and reconciliation controls.

Looking further ahead, Genesys is also working toward a Lakehouse Query capability on the roadmap for later this year, subject to the normal roadmap and release-planning process. This is intended to provide a more directly queryable path to governed Lakehouse data and reduce the amount of extraction and data-platform infrastructure customers need to operate themselves. 

Key points: The Lakehouse Extraction API remains the appropriate option today for customers that need detailed data delivered into their own environment. The future Lakehouse Query capability is expected to complement that model by supporting customers who would prefer a managed query experience over the Lakehouse platform.