Genesys Cloud data lakehouse

The Genesys Cloud data lakehouse is a next-generation data foundation that brings together event data, analytics, and journey insights into a single, query-ready environment. It enables you to unify, organize, and access customer interaction data at scale, without the need to build or manage custom pipelines. The Data Lakehouse combines the flexibility of a data lake with the performance and governance of a data warehouse, helping you accelerate analytics, improve AI accuracy, and deliver more personalized customer experiences. 

Key benefits

The key benefits of Genesys Cloud data lakehouse are as follows:

• Unified data foundation
  • Centralize all Genesys Cloud event data, including voice, digital, and self-service interactions, into a single, governed data layer.
  • Eliminate data silos and provide a consistent view of customer interactions.
• Faster access to insights
  • Query near-real-time, analysis-ready data using your own Business Intelligence (BI), or analytics tools.
  • Reduce the time between data capture and insight generation and support faster, data-driven decision-making.
• Simplified data management
  • Work with curated, deduplicated datasets instead of raw exports.
  • Avoid the complexity of building and maintaining custom extraction, transformation, and loading (ETL) pipelines.
• Enhanced Artificial Intelligence (AI) and orchestration
  • Feed clean, contextual data into Genesys AI, routing, and automation engines.
  • Improve personalization, prediction accuracy, and operational decision-making across the customer journey.

Two Paths: Export and Lakehouse

The Genesys Cloud Data Lakehouse is built on the following two complementary paths for accessing Genesys Cloud data:

Export path (current availability)

The export path, which is available today, is designed for customers who need raw event data for external use. With this approach, structured conversation data is exported in Parquet format to Amazon S3 approximately every 5–10 minutes, making it ideal for organizations that prefer to manage their own data ingestion, transformation, and analytics pipelines.

With the export path, you can:

• Receive structured data in a clean, flat schema.
• Access refreshed data every five minutes for near-real-time visibility.
• Work with short-term snapshots (up to the latest three days of conversation data).
• Integrate easily with existing BI, analytics, or storage tools.

Lakehouse platform (future availability)

The lakehouse platform, which will be available in the future, focuses on delivering query-ready, governed datasets. In this model, raw exported data is ingested into Apache Iceberg tables with schema validation, deduplication, and access controls. Customers can query this data directly using SQL engines such as Amazon Athena or Amazon Redshift, without the overhead of complex ETL processes.

With the lakehouse platform, you can:

• Access governed, queryable datasets directly within the Genesys Cloud environment.
• Benefit from built-in schema management and deduplication.
• Eliminate the need for complex ETL processes or external data synchronization.

Data sources

The data lakehouse includes event data from the following Genesys Cloud domains:

• Conversation Details
• Presence changes
• Routing status
• Work automation
• Workforce management
• Routing configuration
• Quality Management
• Outbound
• Flow
• External Contact
• Customer Intent
• Conversation Custom Attribute
• Agent Profile Data
• Agent Presence Definitions
• Agent Copilot Summary

End-to-end data processing architecture

The Genesys Cloud data lakehouse transforms raw interaction events into trusted, analytics-ready data through a streamlined, end-to-end process designed for scale, governance, and accessibility. The process is as follows:

Event capture

Genesys Cloud continuously captures every customer and agent interaction across different channels, ensuring that no engagement data is lost across the customer journey.

Transformation and governance

The Event Data Platform (EDP) processes these raw event streams and standardizes the data into consistent, analytics-ready datasets with built-in governance, schema management, and data quality controls, ensuring reliability and consistency at scale.

Access and analysis

Once prepared, the data is available for direct access using your preferred BI or query tools, including Amazon Athena or Amazon Redshift, allowing you to explore, analyze, and visualize data directly from the data lakehouse without extra data preparation.

Use cases

The Genesys Cloud data lakehouse supports a wide range of analytics and data-driven initiatives by making trusted, interaction-level data easy to access and integrate across your organization. It also empowers organizations to use their interaction data in powerful ways through the following key use cases:

Performance reporting

The data lakehouse combines data across queues, channels, and agents to gain a comprehensive view of operational performance. It identifies trends, monitors key metrics, and optimizes workforce efficiency with timely and consistent reporting.

Journey analytics

The data lakehouse enriches customer journey maps with behavioral, operational, and sentiment data to uncover friction points across touchpoints. It uses these insights to improve experiences and drive more seamless customer journeys.

AI model training

The data lakehouse provides clean and contextual datasets to the AI and machine learning models to improve predictive accuracy. It improves outcomes for use cases such as forecasting, routing, personalization, and automation, and helps data engineers to build more accurate models.

Data sharing and integration

The data lakehouse seamlessly integrates Genesys Cloud interaction data with enterprise data platforms or other analytics environments. It enables broader BI initiatives and supports cross-functional insights across the organization.