©2022 Databricks Inc. — All rights reserved
Kafka with Spark Structured
Streaming and Beyond:
Building Real-Time Data
Processing and Analytics
with Databricks
Emma Liu
Staff Product Manager, Databricks
Nitin Saksena
Sr. Director of Omni Channel Architecture, Albertsons
Companies
Ram Dhakne
Staff Solutions Engineer, Confluent

©2022 Databricks Inc. — All rights reserved
Databricks
The Lakehouse Company
Global adoption
Over 7000 customers, from F500 to unicorns
Inventor and pioneer of the
data lakehouse
Gartner recognized leader in both
● Database Management Systems
● Data Science and Machine Learning
Platforms
Creator of highly successful OSS data
projects: Delta Lake, Apache Spark, and
MLflow
Raised over $3B in investment
3000+ employees across the globe

©2022 Databricks Inc. — All rights reserved 3
Simple
Unify your data warehousing and AI
use cases on a single platform
Multicloud
One consistent data platform across clouds
Open
Built on open source and open standards
Databricks
Lakehouse Platform
Lakehouse Platform
Data
Warehousing
Data
Engineering
Data Science
and ML
Data
Streaming
All structured and unstructured data
Cloud Data Lake
Unity Catalog
Fine-grained governance for data and AI
Delta Lake
Data reliability and performance

©2022 Databricks Inc. — All rights reserved
Data Governance Data
Warehousing
Data
Engineering
Data Science
and ML
Data
Streaming
BI and Dashboards Machine Learning Data Science
Consulting &
SI Partners
Databricks thrives within your modern data stack
Data Pipelines
Unity Catalog
Delta Lake
Cloud Data Lake
Data Ingestion

©2022 Databricks Inc. — All rights reserved
Speaker: Emma Liu
Benyue (Emma) Liu
Staff Product Manager – Data Ingestion @ Databricks
Previous Experiences: Product Manager at TigerGraph,
MarkLogic; Software Engineer at Oracle
Domain Focus: Cloud Computing, DBaaS, Multi-Model
& Graph Databases, Developer/DBA Tools,
Containerization
MS in Technology and Policy Program at MIT
BS in General Engineering at Harvey Mudd College

©2022 Databricks Inc. — All rights reserved
Spark Structured Streaming
Project Lightspeed
Topic
All structured and unstructured data
Cloud Data Lake
Unified Governance and Security
Data Reliability and Performance
Lakehouse Platform
Data
Warehousing
Data
Engineering
Data Science
and ML
Data
Streaming
Developer Experience
Kafka and Spark Structured Streaming

©2022 Databricks Inc. — All rights reserved
Continuously generated and unbounded data
Machine &
Application Logs
Clickstreams Mobile &
IoT Data
DB Change
Data Feeds
Application
Events
Streaming Data
The vast majority of the data in the world is streaming data!
7

©2022 Databricks Inc. — All rights reserved
Stream Processing is
continuous and unbounded
Stream Processing
8
Traditional Processing is
one-off and bounded 1
Data Source
2
Processing
Data Source Processing

©2022 Databricks Inc. — All rights reserved
Stream Processing in the Lakehouse
Databases
Streaming
Sources
Cloud Object
Stores
SaaS
Applications
NoSQL
On-premises
Systems
Data Sources
BI
Reporting
Dashboarding
Data Science
Machine
Learning
Operational
Apps
Data
Consumers
Transform and improve quality
Stream/batch processing
Spark, Structured Streaming
(Photon)
Ingest
Auto Loader
Serve /
Share
Orchestrate all processes
Monitor & Govern
Delta Live Tables
Jobs

©2022 Databricks Inc. — All rights reserved
Technical Advantages
A more intuitive way of capturing and processing
continuous and unbounded data
Lower latency for time sensitive applications and use cases
Better fault-tolerance through checkpointing
Higher compute utilization and scalability through
continuous and incremental processing
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©2022 Databricks Inc. — All rights reserved
Business Benefits
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BI and SQL
Analytics
Fresher
and faster
insights
Quicker and
better business
decisions
Data
Engineering
Sooner
availability of
cleaned data
More business
use cases
Data Science
and ML
More frequent
model update
and inference
Better model
efficacy
Event Driven
Application
Faster customized
response
and action
Better and
differentiated
customer
experience

©2022 Databricks Inc. — All rights reserved 12
Project Lightspeed

©2022 Databricks Inc. — All rights reserved
Project Lightspeed
Faster and simpler stream processing
Predictable Low Latency
Target reduction in tail
latency by up to 2x
Enhanced Functionality
Advanced capabilities for
processing data with new
operators and easy to use APIs
Operations & Troubleshooting
Simplifying deployment,
operations, monitoring, and
troubleshooting
Connectors & Ecosystem
Improving ecosystem support for
connectors, authentication &
authorization features
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©2022 Databricks Inc. — All rights reserved 14
Apache Kafka
&
Apache Spark Structured
Streaming

©2022 Databricks Inc. — All rights reserved
Apache Kafka and Apache Spark Structured Streaming
Apache Kafka
Connector for
Spark Structured
Streaming Structured
Streaming
End-to-end Open Source Pipeline
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©2022 Databricks Inc. — All rights reserved
Apache Kafka Connector for Spark Structured Streaming
Apache Kafka
Connector for
Spark Structured
Streaming Structured
Streaming

©2022 Databricks Inc. — All rights reserved
Apache Kafka and Databricks Auto Loader
Auto
Loader
Cloud Storage
(S3, ADLS, GCS)
Structured
Streaming

©2022 Databricks Inc. — All rights reserved
Confluent Cloud - Databricks Delta Lake Sink Connector
Cloud Storage
Delta Lake
Sink Connector

©2022 Databricks Inc. — All rights reserved
Latency
Convenience Cost
Choose the right
latency, convenience,
and cost tradeoff for
each specific use case
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©2022 Databricks Inc. — All rights reserved 20
Supercharge Lakehouse with
Streaming Data
Databricks and Confluent

©2022 Databricks Inc. — All rights reserved 21
Albertsons

Building Real-Time Data
Processing and Real time Analytics
Nitin Saksena
Senior Director of Omni Channel Architecture, Albertsons Companies

ALBERTSONS COMPANIES 23
Albertsons Companies is one of the largest
food and drug retailers in the country.
Locally great, Nationally strong

ALBERTSONS COMPANIES 24
Our Purpose
To bring people together around the joys of
food and to inspire well-being

ALBERTSONS COMPANIES 25
Albertsons – Locally great, Nationally strong
Environment, Social and Governance (ESG) efforts are focused on: Planet, People, Product and Community

ALBERTSONS COMPANIES 26
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.

ALBERTSONS COMPANIES 27
Technology
Transformation and
Strategic Priorities

ALBERTSONS COMPANIES 28
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

ALBERTSONS COMPANIES 29
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

ALBERTSONS COMPANIES 30
Architecture Deep Dive

ALBERTSONS COMPANIES 31
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

ALBERTSONS COMPANIES 32
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

ALBERTSONS COMPANIES 33
Real Time Data Processing using Databricks
Offer Management System
API API
Master Data
Stores
Offer Expansion and Distribution
Clips Distribution to relevant store

ALBERTSONS COMPANIES 34
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

ALBERTSONS COMPANIES 35
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

©2022 Databricks Inc. — All rights reserved 36
Databricks &
Confluent Partnership

©2022 Databricks Inc. — All rights reserved
Speaker: Ram Dhakne
Ram Dhakne
Ram works as a Staff Solutions Engineer at Confluent. He has a
wide array of experience in NoSQL databases, Filesystems,
Distributed Systems and Apache Kafka.
He supports various industry verticals ranging from large
financial services, retail, healthcare, telecom, and utilities
companies towards their digital modernization journey.
His current interests are in helping customers adopt realtime
event streaming technologies using Kafka. As a part-time hobby,
he has authored two children’s books.

©2022 Databricks Inc. — All rights reserved
Modern data platforms are powering multiple real-time
apps across industries
Retail
Drive consumer
analytics & streamline
operations
Healthcare
Provide patients better
choices & doctors
better insight
Banking
Combat fraud &
remain competitive
Automotive
Amplify vehicle
intelligence & safety
Inventory
Management
Personalized
Promotions
Product
Development
& Introduction
Sentiment
Analysis
Supply chain
logistics
Systems of
Scale for High
Traffic Periods
Connected
Health
Records
Data
Confidentiality
& Accessibility
Dynamic Staff
Allocation
Optimization
Integrated
Treatment
Proactive
Patient Care
Real-Time
Monitoring
Capital
Management
Early-On
Fraud
Detection
Market Risk
Recognition &
Investigation
Preventive
Regulatory
Scanning
Real-Time
What-If
Analysis
Trade Flow
Monitoring
Advanced
Navigation
Environmental
Factor
Processing
Fleet
Management
Predictive
Maintenance
Threat
Detection &
Real-Time
Response
Traffic
Distribution
Optimization

©2022 Databricks Inc. — All rights reserved

©2022 Databricks Inc. — All rights reserved
Multiple deployment models
Confluent Platform
The Enterprise Distribution of
Apache Kafka
Confluent Cloud
Apache Kafka Re-engineered
for the Cloud
Self-Managed Software
Fully-Managed Service
VM
Deploy on any platform, on-prem or cloud
Available on the leading public clouds

©2022 Databricks Inc. — All rights reserved
From migration
to modernization
How Confluent and Databricks accelerate
your journey to real-time analytics in the
cloud

©2022 Databricks Inc. — All rights reserved
Modernize your data platform with Confluent + Databricks
Reduce total cost of ownership
Process data in stream to lower DW costs. Lower data
pipeline TCO with fully managed cloud service.
Power new analytics and apps
Link on-prem and cloud for easier data movement
across environments with real-time event streaming
to modernize your analytics and applications
Get more data to and from your DW
Break data silos and easily connect your data
warehouse to popular sources and sinks using
Confluent’s 120+ pre-built connectors
= Real-time connections & streams

©2022 Databricks Inc. — All rights reserved
Managed Connectors
(for near real time data delivery)
● Bring Data from everywhere to everywhere
● Multi-Cloud Native
● Open Source Kafka - hardened for Enterprise
● Single platform for BI, AI, ML on all data
● Multi-Cloud Native
● Open source (Spark, MLFlow, Delta Lake)
Multiple
Integrations
Spark Structured
Streaming
(for real time data delivery)
Best in class solution for real-time analytics
Delivered at scale with the speed, security, and reliability required by enterprises

©2022 Databricks Inc. — All rights reserved
Next steps
Get started
Try Databricks for free
databricks.com/try-databricks
Visit the Databricks booth
Databricks
Get started
Try Confluent for free with $400
in free credits
confluent.io/confluent-cloud
Visit the Confluent booth
Confluent

©2022 Databricks Inc. — All rights reserved
Q&A

©2022 Databricks Inc. — All rights reserved

©2022 Databricks Inc. — All rights reserved
Thank you!
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