Scalable Offline Identity Graph for Marketing Intelligence

Designed and implemented a large-scale offline identity management system that enables organizations to unify fragmented consumer data and derive actionable insights for targeted marketing campaigns.

The platform processes high-volume datasets such as purchase history, website activity, and consumer interests, and links them to a unique consumer identity using advanced matching techniques.

This unified identity layer powers segmentation, targeting, and campaign analytics at scale.

Offline identity management system for Marketing Analytics

Implementation Process

Data Processing Layer

Batch data received from multiple clients:

Purchase transactions
Behavioral and interest data

Supported ingestion of large-scale structured and semi-structured datasets

Data Cleansing & Standardization

Data processed through cleansing pipelines:

Attribute standardization
Deduplication
Format normalization

Identity Matching & Resolution Engine

Core system to link consumer data across sources Built logic to:

Create a unique consumer identity (Identity Graph)
Identify unique consumer attributes
Connect fragmented records

Big Data Processing Layer

Built on Hadoop Distributed File System (HDFS) Used Apache Spark for:

Data aggregation and segmentation
Distributed data processing
Large-scale high speed data processing.

Data Storage & Access

Processed data stored in:

HDFS (data storage)
Optimized for bulk storage and large-scale querying

Structured datasets prepared for:

Consumer segmentation
Campaign targeting

Analytics & Reporting Layer

Generated insights for:

Consumer segmentation for marketing
Interest-based targeting
Campaign audience selection

Delivered outputs for:

Data-driven decision making
Marketing campaign execution

Technology Stack

Java, Scala
Python
Spark & Hadoop
AI & ML

Offline Identity Management & Data Analytics