Databricks CustomerLake vs Amperity for identity resolution and Customer 360

Summary

• Databricks CustomerLake is an agentic customer data platform (CDP) embedded natively within Databricks. Databricks announced it on June 16, 2026 in Private Preview, so confirm access with your Databricks account team.
• CustomerLake's Agentic Identity Resolution (AIR) combines deterministic, probabilistic and agentic matching, with LLM and human review workflows for edge cases.
• Amperity describes itself as "the AI-Powered Customer Context Platform". Its Stitch identity resolution uses deterministic, probabilistic and transitive connections and assigns an Amperity ID to each customer cluster.
• Amperity Bridge shares tables with Databricks in both directions through OpenSharing (formerly Delta Sharing), without copying data.
• We believe the better fit depends on where your customer data lives today and which team will own identity rules.

Databricks CustomerLake vs Amperity for identity resolution and Customer 360

Both products turn fragmented customer records into unified profiles, and both document deterministic and probabilistic matching. CustomerLake is embedded in Databricks, so identity resolution runs where your customer data already lives and is governed by Unity Catalog. Amperity is a separate platform whose Stitch process resolves identity. It connects to Databricks through Amperity Bridge and says supported batch workloads, including identity resolution, execute in your compute. We believe the better fit depends on where your customer data lives today and who will own identity rules. CustomerLake launched in Private Preview, so confirm its availability first.

What is identity resolution?

Identity resolution recognizes when records from different systems describe the same person and merges them into one profile. Databricks calls CustomerLake's output golden Customer 360 records, and Amperity assigns an Amperity ID to each cluster of customer records.

Databricks CustomerLake vs Amperity: how they compare

Capability	Databricks CustomerLake (Private Preview)	Amperity
Category	Agentic CDP embedded natively within Databricks	Describes itself as the AI-Powered Customer Context Platform
Matching methods	Deterministic, probabilistic and agentic matching	Deterministic, probabilistic and transitive connections
Edge cases	LLM and human review workflows, plus an agentic learning loop that recommends improvements to matching logic	Hierarchical comparison looks for conflicts, such as married couples with overlapping profile data
Your existing logic	Teams can bring existing identity rules, models and third-party enrichment partners	Rules run from the top of the list down; a setting selects Amperity AI scoring or no matching model (purely deterministic)
Profile output	Golden Customer 360 records, built directly in Databricks	An Amperity ID for each cluster of customer records
Where it runs	Embedded in Databricks, so CDP capabilities run where your customer data already lives	Amperity says supported batch workloads, including identity resolution, execute in your compute, while real time and activation stay Amperity-managed
Governance	Unity Catalog: centralized control over access, lineage and auditability	Amperity says every access point carries lineage showing where customer data is stored, processed, and accessed
Access without copying	Lakehouse Federation gives governed, read-only access to external data through Unity Catalog foreign catalogs	Amperity Bridge enables two-way access between Amperity and Databricks without copying data or scheduling ETL workloads
Activation	Native managed reverse ETL connectors sync audiences to martech and adtech tools	Activations send audiences downstream through orchestrations, single-touch campaigns or multi-stage journeys

How Databricks approaches identity resolution

In its launch blog, Databricks describes Profile Agents that prepare data, identify quality issues and support third-party enrichment to unify disconnected records into golden profiles. Agentic Identity Resolution lets teams bring existing identity rules, models and enrichment partners while the agents help identify edge cases. Profiles are built directly in Databricks and governed through Unity Catalog. The product page adds that CustomerLake does not move or copy customer data, Lakeflow handles ingestion, and marketers can build audiences in natural language through a workspace powered by Genie.

How Amperity approaches identity resolution

Amperity's Stitch documentation describes seven stages: semantic tagging, preprocessing, union of tables, blocking, initial scoring, pairwise comparison and stable ID assignment. Deterministic matching uses rules that define exact matching, probabilistic matching uses rules that define approximate matching, and transitive connections link two records that each share a strong match with a third. Amperity says it resolves identity at scale, including profiles, households and persistent IDs, and describes Pér as its AI agent for customer data. For Databricks teams, the Amperity Bridge guide has you enable OpenSharing on the Unity Catalog metastore and create a share and a recipient to share Databricks tables with Amperity. To read Amperity data in Databricks, you add Amperity as a provider and create a catalog from the share.

Which questions should drive the choice?

• Where does customer data live today? If it is already governed in Unity Catalog, CustomerLake keeps identity work there, and Amperity Bridge offers zero-copy sharing if you also run Amperity.
• Who will own identity rules? CustomerLake lets teams bring their own rules and models; Amperity evaluates ordered rules.

FAQs

Can I use CustomerLake and Amperity together?

Amperity documents two-way table sharing with Databricks through Amperity Bridge. The CustomerLake materials cited here do not describe how it would consume Amperity's shared tables, so ask your Databricks account team.

Is CustomerLake generally available?

Databricks announced CustomerLake on June 16, 2026 as available in Private Preview. This page does not assume a later stage, so confirm the current status with your account team.

Does CustomerLake copy my customer data out of Databricks?

Databricks says no. CustomerLake is embedded directly in Databricks, so CDP capabilities run where your customer data already lives.

Which product matches records more accurately?

No accuracy comparison is cited here. Results depend on your data and rules, so test both on a sample of your own records.