Real Estate

Real Estate & Facilities Analytics

Cloud data analytics for a NASDAQ-listed real estate & facilities leader.

Client: US NASDAQ-listed company owning, maintaining and renting out 10,000+ properties

The Problem

The client owns, maintains and rents out over 10,000 properties, running operations on Yardi, Salesforce and custom applications. Their on-premises BI suite was too rigid for ad-hoc analytics — finance, operations, marketing, FPQA and other teams couldn't mix and merge data themselves for quick analysis.

Challenges

  • Bulky admin overhead maintaining Windows, file and SQL servers
  • Expensive licensing for multiple on-premise servers with HA & DR copies
  • Concurrency issues with parallel read/write processes
  • An ETL-built warehouse that was hard to change, with a steep learning curve
  • Clients forced to learn MDX to analyze data in SSAS

Solution Design & Implementation

01

Data Lake Architecture

A central data lake serving as the repository of all data in its native form — from users, vendors, clients and partners.

02

ELT & Data Processing

Extract-Load-Transform architecture allowing data to be transformed into the form each team needs for their use case.

03

Cloud-Based Easy Access

Browser-based querying from Snowflake and uploads to AWS S3 — far easier than the server-based on-prem technologies that demanded more training.

Architecture

Sources

Yardi

Salesforce

Custom Applications

Uploads

Vendor & Partner Files

Ingestion & Integration

Browser-based Uploads to S3

Cloud Data Lake & ELT

AWS + Snowflake Stack

Transformations

Snowflake

ELT Transformations

Data Zones

Native Form (S3)
Data Lake
Team-Ready Marts

Consumption

Self-serve

Finance & Operations

Ad-hoc Analytics

Marketing & FPQA

Orchestration & Monitoring

Self-serve Query Layer

Results

  • Significant reduction in processing time
  • Savings on human capital
  • High up-time and availability
  • Infrastructure costs saved

Future Plans

More Migrations

All remaining on-prem reporting migrated to the data-lake-based cloud analytics framework.

Central Analytics

All new reporting flows through the data lake, enabling cross-platform analytics across business units.

AI & ML Initiatives

The lake now supports AI and ML models to identify risks and potential opportunities beyond reporting.

Retire Old Servers

Unused on-premise servers decommissioned, saving infrastructure and management costs.