Comparison: CDPs and Lakehouses in AI Applications
Lakehouses vs. Customer Data Platforms – An Overview
While lakehouses store data and make it available for analysis, CDPs make this data usable for marketing, CRM, and AI.
Particularly exciting: the use of Conversational AI and Next Best Action engines.
This article compares both approaches and shows how Acceleraid is the ideal complement.
1. Lakehouse: The Data Foundation
A lakehouse combines a data lake and a data warehouse into a single platform.
Typical representatives include Snowflake, Databricks, Google BigLake, or Microsoft Fabric.
Strengths:
- Storage of all data at scale
- AI and machine learning workflows at enterprise level
- Governance, security, and scalability
Value for AI:
Lakehouses provide the ideal foundation for training models – from fraud detection to churn prediction.
2. CDP: The Activation Layer
A Customer Data Platform pursues a different goal:
making data usable for customer engagement.
Strengths:
- 360° customer view in real time
- Self-service without programming knowledge
- Integrated consent and GDPR handling
- Next Best Action engines that leverage AI to make the right decision at the moment of interaction
- Direct activation in email, app, ads, chat, or call center
Value for AI:
CDPs not only provide models but also bring them into customer engagement –
fast, GDPR-compliant, and understandable for business teams.
3. Comparison: Lakehouse vs. CDP
| Feature | Lakehouse (Snowflake, Databricks, etc.) | CDP (e.g., Acceleraid) |
|---|---|---|
| Data Storage | Raw data, structured & unstructured | Customer profiles in real time, aggregated |
| Users | Data engineers, BI teams | Marketing, CRM, product teams |
| AI Focus | Training & modeling | Application in customer engagement (Next Best Action) |
| Complexity | High, requires engineering | No-code, self-service |
| Activation | Indirect, via exports/APIs | Direct, omnichannel integration |
| Consent | Storage & audit | Visible customer consent “out of the box” |
| Conversational AI | Data basis for training | Real-time delivery of relevant information via MCP |
4. Bridge to Conversational AI & MCP
AI chatbots are becoming one of the most important channels in customer engagement.
Through the Model Context Protocol (MCP), bots can access company data directly.
However: Bots don’t need all raw data – they need the right Next Best Action at the right moment.
Acceleraid delivers exactly that:
- Integration with data sources such as DWH, data lakes, or CRM lakehouses as the data foundation
- Real-time customer profiles & consent status
- Next Best Action engine for AI-driven recommendations
- Utilization of large volumes of transaction data
- Connection to chatbots via MCP – GDPR-compliant and ready to use immediately
5. Conclusion
Lakehouses are ideal for training AI models and managing enterprise data centrally.
CDPs are indispensable for applying these models in customer engagement – without programming, in real time, and across channels.
Acceleraid combines both and extends them with conversational AI: the ideal solution for companies that want to put AI into practical use in customer interactions.