How a Real‑Time CDP Solves Data Silos: Architecture, Tech Choices & Lessons

What Is a CDP?

A Customer Data Platform (CDP) unifies fragmented customer data from multiple sources into a persistent, queryable database that can be accessed by downstream systems for marketing and analytics.

Challenges and Goals

Enterprises face diverse data formats, siloed channels, complex audience segmentation, and the need to serve both B2C and B2B2C models while maintaining multi‑tenant isolation and scalability.

Key objectives include flexible data ingestion, real‑time cross‑channel identity resolution, unified customer profiles, low‑latency processing, secure long‑term storage, and support for both SaaS and on‑premise deployments.

Technology Selection

Identity Graph: Nebula Graph (cloud‑native deployment) replaces relational and Spark‑based approaches for real‑time ID mapping.

Stream Processing: Apache Flink with Apache Beam provides true stream processing, low latency, and unified batch‑stream programming.

Storage: Apache Kudu for hot data, Impala for query, and Apache Doris for analytical workloads; Parquet files are used for cold storage tiers.

Schema Management: A flexible Schema model abstracts data structures, enabling rapid onboarding of heterogeneous data sources.

Platform Architecture

The system consists of five logical layers: data sources, ingestion services, real‑time compute, unified profiling, and data output. Core modules include a real‑time ingestion service, Connectors for batch sources, an Identity Service, Flink‑based compute jobs, a unified query service, and a real‑time rule engine.

Key Implementations

Data Definition Model: Schemas describe fields, allow inheritance, and support custom extensions per tenant.

Identity Service: Deployed on Kubernetes with Nebula Operator, offering high‑throughput read/write and tenant isolation.

Real‑Time Compute: Stateless entrance jobs clean, validate, and route data; downstream jobs enrich profiles, persist to Kudu/Doris, and feed downstream systems.

Rule Engine: Custom Flink‑based engine evaluates complex AND/OR rules, supporting windowed and non‑windowed behavior analysis.

Scalability: Elastic cluster sizing, dynamic topic routing, and batch‑write triggers balance throughput and resource cost.

Monitoring: Unified logging, Prometheus metrics, SkyWalking tracing, and latency dashboards provide end‑to‑end observability.

Results and Future Outlook

The platform achieves tens of thousands of QPS for identity queries, millisecond‑level processing latency, and 99.99% stability. Future work includes richer AI models, smarter governance, and integration of lake‑house technologies such as Iceberg or Hudi.