Adobe Data Distiller is a query and analysis tool integrated within Adobe Experience Platform (AEP) that enables marketers and data analysts to perform complex data operations and generate insights from customer data. The platform has evolved to support direct querying of live data sources through advanced data sharing mechanisms, significantly expanding its analytical capabilities without requiring data movement or duplication.¹⁾

Adobe Data Distiller operates as a SQL-based query engine within the AEP ecosystem, allowing users to construct custom datasets and perform analytical queries across customer experience data ²⁾.

A significant advancement in Data Distiller's capabilities involves direct integration with Databricks Delta Sharing technology, which enables the tool to query live data from Databricks environments without physically moving or copying underlying records. This approach uses virtual tables—read-only data objects that maintain references to source data while presenting it as queryable tables within the Data Distiller interface ³⁾.

Delta Sharing establishes a secure, controlled connection between Databricks data lakes and AEP's query engine, allowing Data Distiller to access current Databricks datasets in real time. This architecture eliminates traditional ETL (extract, transform, load) overhead and reduces data redundancy, as analysts query live sources directly rather than managing periodic data exports or synchronized copies. The virtual table abstraction provides governance and access control through Databricks' sharing mechanisms while maintaining full compatibility with Data Distiller's SQL query environment.

Data Distiller's integration with Delta Sharing relies on secure authentication and permission protocols that respect both Adobe and Databricks access control policies. The system uses Apache Spark SQL as its underlying query engine, enabling efficient distributed query execution across large datasets. When queries reference Delta Sharing virtual tables, the execution layer communicates with Databricks' Delta Lake metastore to resolve table definitions and push computation to the appropriate data source ⁴⁾.

The solution supports complex analytical operations including multi-table joins, aggregations, window functions, and custom user-defined functions. Query optimization occurs transparently, with the Spark query planner determining whether to execute operations within Data Distiller or push them to the Databricks Delta Lake for parallel execution based on cost and data locality considerations.

Data Distiller with Delta Sharing integration serves several marketing and analytics use cases:

* Real-time Customer Segmentation: Query live customer datasets to identify audience segments meeting specific behavioral or demographic criteria without data replication * Attribution and Journey Analysis: Analyze customer touchpoints and conversion paths by joining AEP event data with Databricks-hosted historical records * Predictive Analytics Data Preparation: Access raw data sources directly to prepare training datasets for machine learning models * Compliance and Data Governance: Maintain single sources of truth for customer data while enforcing consistent access controls across platforms

The direct querying approach offers significant advantages including reduced infrastructure costs, elimination of data synchronization latency, and simplified data governance through single-source-of-truth architectures. Organizations avoid managing multiple copies of the same data across systems, reducing storage costs and ensuring analytical consistency.

However, query performance depends on network latency between AEP and Databricks environments, and complex analytical operations may require optimization to avoid excessive data transfer. Additionally, organizations must maintain compatible Databricks environments and Delta Sharing permissions to utilize this functionality effectively.

As of 2026, the integration of Adobe Data Distiller with Databricks Delta Sharing represents a broader industry trend toward federated analytics architectures ⁵⁾ that enable query-in-place rather than data movement-centric approaches. This pattern reflects increasing organizational emphasis on data minimization, regulatory compliance, and operational efficiency in marketing technology stacks.

• Adobe Experience Platform (AEP)
• Adobe
• Adobe Marketing Agent
• Adobe Journey Optimizer
• Adobe CX Enterprise