This document discusses building real-time data processing and analytics with Databricks and Kafka. It describes how Databricks' lakehouse platform and Spark Structured Streaming can be used with Apache Kafka to ingest streaming data and perform real-time analytics. It also provides an example of how a large retailer, Albertsons, uses Databricks to distribute offers in real-time, power dashboards with streaming data, and enable hyper-personalization with real-time data models. The partnership between Databricks and Confluent is also discussed as a way to modernize data platforms and power new real-time applications and analytics.
22. Building Real-Time Data
Processing and Real time Analytics
Nitin Saksena
Senior Director of Omni Channel Architecture, Albertsons Companies
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Albertsons Companies is one of the largest
food and drug retailers in the country.
Locally great, Nationally strong
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Our Purpose
To bring people together around the joys of
food and to inspire well-being
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Albertsons – Locally great, Nationally strong
Environment, Social and Governance (ESG) efforts are focused on: Planet, People, Product and Community
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Nitin Saksena
Senior Director of Omni Channel Architecture, Albertsons
Nitin is leading the Enterprise Architecture Team across eCommerce, Digital Shopping Experience,
Marketing and Media Collective, Merchandizing and Pharmacy. He has 19+ years of experience in
industry predominantly in Retail. Nitin has architected several key initiatives in Loyalty, Offer
Execution and Redemption, Order Management and Customer Support. His areas of interest is to
solve complex business problems with simple solutions.
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Technology Transformation - Key Tenets
Strengthen talent with an agile and high energy Global Team
High Performance Team
Build easy frictionless &
differentiated Omni-
Channel Experiences
Omni-Channel
Experiences
Create agile, scalable and
reliable technology with
Platform Modernization
Modern Technology
Platform
Maximize value for our
customers and employees
with intelligent data
Intelligent Data
Leverage technology to
drive productivity in the
enterprise
Productivity
Strong Technology Foundation to Support Accelerated Business Growth
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Technology Strategic Priorities
DRIVERS
Intelligent Agility Cognitive Realtime Connected Autonomous
Foundation
Modernization
& Migration to Public
Cloud
Enterprise
Data Platform
Network
Modernization
InfoSecurity
Global People
& Process Optimization
Competitive Digital &
eCommerce
Optimized
Promotions
Forecasting &
Replenishment
Run Great Stores
Smart Stores
Supply Chain of
the Future
Scaled &
Differentiated
eCommerce
Strategic Data
Health &
Wellness
Expansion
Loyalty
Programs
Business
Priorities
EASY & FRICTIONLESS EXCITING & INNOVATIVE DIGITAL PENETRATION LONG TERM CUSTOMER
RELATIONSHIPS
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Real Time Data Processing using Databricks
Distributing Offers and
Customer Clips to each store
in Near Real Time
Distribution of Offers to the relevant
Stores
Distribution of Clips in near real time to
the frequently shopped store.
An improved Store checkout
experience.
Real Time Business
Reporting and Dashboard
Supply Chain Order Forecast
Warehouse Order Management and
Delivery
Labor Forecasting and Management
Demand planning
Inventory management
Inventory updates from 2200
stores to the cloud services
in near real time
Maintain a visibility in the health of the
Stores
Well Managed Out of Stock and
Substitution recommendations for
Digital Customers
Ingesting Transactions in
Near Real Time to Data Hub
Generate Near Real Time Dashboards for
Associates and Business
Feed Data in Near Real time to Data Models
for in-session Hyper Personalization
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Real Time Data Processing Using Databricks
Real Time Data Processing using
Databricks in an Event Driven
Architecture
Problem Statement
• Merchandising Team creates Offers and Promotions.
• The Offers are created at Banner, Division or Individual Store Levels
• These Offers are to be presented to our customers.
• More importantly these offers are sent to the stores across the country.
• Customers view these offers on digital channel and clip them.
• These Clips are sent to frequently shopped stores in real time.
• Clipped Offers are applied on the transactions online and in stores
• Stores and Online system are notified of the redeemed offers
Stores
Offers Clips
Offers
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Real Time Data Processing using Databricks
Offer Management System
API API
Master Data
Stores
Offer Expansion and Distribution
Clips Distribution to relevant store
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Real Time Analytics Using Databricks
API
Operational
Database
Event
Database
Extract Metadata out of Business Events
Cross Reference the Metadata with other Event
Metadata
Drive the Metrics out of Event Cross References
Derived Metrics
Dashboard
Application
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Future Use Case with Real Time Data Processing
Hyper Personalization of
Content (Offers, Recipes)
In session
recommendation using
runtime data models
Next Best Action
Reduced Out of Stock
Pick and Improve
Substitution By using real
time inventory signals