---
title: Feature comparison
slug: swisscom-b2b-lab/feature-comparison
docTags: 
createdAt: 2025-10-13T08:12:59.000Z
---

:::hint{type="info"}
This section is not an exercise but an **explanatory chapter** designed to clarify common misconceptions about Query Service and BI Extension. It provides context, comparisons, and practical examples to help you understand their roles within Adobe Experience Platform and Customer Journey Analytics.
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When working with Adobe Experience Platform (AEP) and Customer Journey Analytics (CJA), two features often raise questions: **Query Service** and **BI Extension**. Both allow SQL-based access using PostgreSQL and integrate with external tools by a PSQL connector, which makes them look similar at first glance. However, they serve different purposes in the data lifecycle.

### Similarities

- Both use **PostgreSQL syntax**, so analysts can write queries in a familiar language.
- Both enable **integration with exteranl tools **through a PSQL connector.

### Key Differences

| Aspect                | Query Service                                                                                                                     | BI Extension                                                                                                                                               |
| --------------------- | --------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Data Location**     | AEP **Data Lake**: Raw, ingested datasets from multiple sources (Adobe and non-Adobe).                                            | CJA **Data Views**: Pre-processed, curated datasets optimised for journey analysis.                                                                        |
| **Purpose**           | Data engineering and transformation: stitch, join, validate, and prepare data for downstream apps.                                | Democratise access to CJA metrics and dimensions in BI tools without recreating logic.                                                                     |
| **Scope of Analysis** | Broad, multi-domain queries across raw data (CRM, POS, web, etc.).                                                                | Focused on **customer journey insights** already modelled in CJA.                                                                                          |
| **Performance**       | Handles large-scale queries but requires SQL expertise and governance.                                                            | Optimised for reporting consistency and speed; inherits CJA’s aggregation logic.                                                                           |
| **Use Cases**         | • Validate ingestion pipelines<br />• Create derived datasets<br />• Data quality checks<br />• Advanced joins across raw sources | • Build dashboards in Power BI/Tableau using CJA metrics<br />• Share consistent KPIs across teams<br />• Avoid duplicating CJA logic in external BI tools |

### Summary

Think of **Query Service** as the **data engineering layer**—it works on raw, harmonised data in AEP’s Data Lake, enabling transformations and joins before data flows into CJA. On the other hand, **BI Extension** is the **analytics distribution layer**, giving analysts direct access to curated CJA data views for consistent reporting without reinventing the wheel.