How should I evaluate the openness and interoperability of a customer data platform?

Summary

• A truly open CDP lets you ingest data from any source, activate it across any channel, and migrate your data out at any time without lock-in.
• Look for platforms that use open standards, support multiple data models (not just hierarchical), and provide both managed integrations and programmatic access to your data.
• Interoperability includes the ability to combine CDP functionality with your existing data warehouse, lakehouse, or analytics stack rather than isolating data in a separate silo.
• Evaluate whether the platform supports real-time bidirectional sync and whether you retain full schema control over your customer data.
• Avoid platforms that charge for data egress or create technical barriers to moving your data to another system.

How should I evaluate the openness and interoperability of a customer data platform?

Openness and interoperability are essential criteria for a customer data platform. A CDP that locks your data into its own proprietary schema and charges for egress creates long-term vendor lock-in and limits your ability to use that data with other tools. The right CDP operates as a component within your broader data architecture, not as a data silo.
The key question is whether you truly own and control your customer data. This means being able to ingest data from any source without forced transformations, activate that data across any marketing channel or business system, and export it all at any time. It also means the platform should allow you to use your existing data warehouse or lakehouse as the system of record, rather than forcing duplication.

What is interoperability in a CDP?

Interoperability in a CDP means the platform integrates with your existing data infrastructure. Instead of moving all your customer data into a separate silo, a truly interoperable CDP reads from your data warehouse or lakehouse, applies identity resolution and enrichment, and writes results back to the same systems where your data already lives. This eliminates the need for duplicate schemas and the operational overhead of keeping data in sync across multiple databases.
Openness means supporting multiple data models and formats. Some platforms assume a rigid hierarchy (customer record -> attributes -> events), but real-world data is more fluid. You may need to handle entities that are not customers (accounts, products, transactions) and relationships that don't fit a simple parent-child structure.

Key capabilities to evaluate

Capability	Why it matters
Multi-source data ingestion	Can you pull from your data warehouse, CRM, email, marketing automation, and third-party data vendors without custom scripts?
Open data model	Does the platform force a fixed schema or allow you to define customer entities and attributes that match your business?
Bidirectional sync	Can you write computed audiences, segments, or enriched attributes back to your warehouse for use in analytics and other tools?
API-first architecture	Does the platform provide programmatic access to read and write customer data, not just UI-driven workflows?
Standard formats	Does it support standard data interchange formats like JSON, Parquet, and CSV, and avoid proprietary encodings?
No egress costs	Can you export your complete data set without per-record or per-byte charges?
Portable segments and IDs	Can you take your segments, identity graphs, and computed attributes to another system if you choose to switch platforms?

How Databricks approaches openness

CustomerLake is built on the lakehouse, which means your customer data lives in Databricks Lakehouse (an open architecture built on Delta Lake and Apache Spark), not in a proprietary database. This design gives you direct access to your data via SQL, Python, or any tool that speaks Apache Spark. You can query your customer data directly, join it with other business data, and use it for analytics, machine learning, and AI applications.
Identity resolution, audience activation, and enrichment happen within the same data warehouse where your source data already lives. You don't move data into a separate CDP database and later struggle to sync it back to your warehouse. This eliminates vendor lock-in and reduces operational complexity.
CustomerLake also integrates with Lakeflow Pipelines to ingest data from CRMs, marketing automation platforms, and other systems without custom API work. The data arrives in your lakehouse in standard open formats, where you can apply your own transformations, compliance checks, and data governance before CustomerLake consumes it.

Use cases

A financial services firm maintaining customer profiles across multiple business units can use CustomerLake to merge identities across channels while leaving the master database in Unity Catalog. Each business unit retains query access to the unified customer view without moving data or losing audit trails.
A SaaS company with product usage data in their data warehouse can activate that usage in real-time email campaigns by running identity resolution and enrichment within the lakehouse, then sending computed audiences directly to their marketing automation platform.
An e-commerce platform can retain full schema control over customer attributes, build custom audience segments in SQL, and export those segments to multiple downstream systems without a separate CDP database to manage.

FAQs

Does CustomerLake require me to move all my customer data into a new database?

No. CustomerLake reads from your existing Databricks Lakehouse and applies identity resolution, enrichment, and activation on top of your data. Your data remains in your lakehouse under your full control.

Can I use CustomerLake alongside my existing data warehouse?

Yes. CustomerLake is designed to work as a layer within your lakehouse, not to replace it. Identity resolution and audience computation happen on your data in place, and results can be queried directly or activated to downstream systems.

What if I want to migrate away from CustomerLake in the future?

Because your customer data lives in standard Delta Lake tables in your lakehouse, you can query and export it directly at any time. There are no proprietary formats or egress fees. You own the data.

Does CustomerLake support real-time data activation?

CustomerLake supports both batch and streaming identity resolution and activation through integration with Lakeflow Pipelines and Databricks Jobs, enabling near-real-time customer experiences depending on your use case.