iTech webinar presented to cross-industry audience, focus on ElasticSearch, SQLServer Big Data Cluster, and Dell AI Strategy

1. 1
Digital Transformation Through
Data and Analytics
Dell AI and Data Analytics Roadshow
Bill Wong – Artificial Intelligence and Data Analytics Practice Leader
Craig Allen – Microsoft Specialist
Keith Quebodeaux - Advisory Systems Engineer
Ziad Najjar - Infrastructure Solutions Group Director
Dell Technologies

2. 2
COVID-19 and the New Reality
Supply Chain Disruptions
 COVID-19 is forcing companies to question
the ability of their supply chains to effective
and reliably respond to rapidly evolving risk
factors.
Labour
 The unemployment rate rose 5.2
percentage points in April to 13.0%.
 2.1 million people worked reduced hours.
Revenue Challenges
 Across the country, over half of businesses in
Alberta (57.7%), Ontario (56.3%), B.C.
(54.8%), Newfoundland and Labrador (53.5%),
and Saskatchewan (52.8%) saw declines of
20% or more in revenue.
(Conference Board of Canada - March, 2020)
Transportation Disruptions
 Over 80 countries have issued travel
restrictions related to COVID-19.

3. 3
Today’s Business Trends
Trend #1: Remote working will be a prevalent way of working
Trend #2: Organizations across industries will increasingly rely on digital
platforms/channels to increase future resilience and growth
Trend #3: Data and analytics become essential in assisting faster and better
decision-making
According to IDC, By 2025, at least 90% of new enterprise apps will embed
artificial intelligence. Most of these will be AI-enabled apps, delivering
incremental improvements to make applications "smarter" and more dynamic.

4. 4
• Extensive Portfolio of Offerings
Workstations, servers, networking, storage, software and
services — to create end-to-end solutions that underpin
successful AI, machine and deep learning
implementations
• AI Expertise
Dell EMC experts to help users adapt as AI, machine and
deep learning evolve over time – and work with customers
on systems designs and POCs (HPC and AI Innovations
Lab, Solution Centers)
• AI Partner Solutions
Simplified deployment on an optimized IT infrastructure
Dell Technologies Value Proposition for Data Analytics

5. 5
Agenda
 Key Business Challenges and Trends
 Streaming Analytics
 Machine and Time Series Data
 Use Cases
 Advanced Analytics Platform
 Data Virtualization
 Big Data
 Artificial Intelligence / Machine Learning
 Industry Trends
 Partner Ecosystem
 Summary
 Next Steps

6. 6
IoT / Streaming Analytics

7. 7
Data Analytics Ecosystems
• Big Data and Analytics is a Collection of Ecosystems
not an Application
• Big Data Involves Data Sets too Big (Volume), Fast
(Velocity), or Complex (Variety) that “Traditional” Data-
Processing Application Software (RDBMS) are
Inadequate
• Prevalent Ecosystems to Dell EMC
– Splunk
– Hadoop
– Elastic
– SAS
– SAP
– TensorFlow
• For Many Customers Big Data was Synonymous with
the Hadoop Ecosystem

8. 8
What are Machine and Time Series Data?
Machine Data
• Machine data is information
generated by a computer,
sensor, device, process, or
application
• Machine data is not limited to
data about devices
• Machine data crosses all
industry sectors
Time Series Data
• A time series is a sequence of
data points indexed in time order
• Commonly a time series is a
sequence taken at successive
equally spaced points in time

9. 9
Turning Machine Data into Insights
Machine Data Complexities Example
• Large Variety of Sources and
Structure
• Ability to Effectively Analyze and
Make Decisions
• Managing the Rapid Growth of Data
• Building the Right Infrastructure

10. 10
How Are We Typically Dealing with Machine Data? (and Why?)
• Hadoop
– Kafka
– NiFi
– Flume
– Hbase
• Elastic Stack (ELK)
– Elasticsearch
– Kibana
– Logstash
– Beats
Build Modular Build
• Splunk
– Splunk Enterprise
› Splunk Enterprise Security
› Splunk IT Service Intelligence
› Splunk User Behavior
Analytics
Buy

11. 11
Stream Processing Analytics - Logical Model
Centralized analytics
(processing all data,
external data
integration, model
development and
refinement)
Entity-level analytics (event
driven, reactive model
execution with real-time
data compression and
analysis)
Distributed analytics
(localized prediction and
decision making using
edge and neighborhood
data)
Platform Tier Enterprise TierEdge Tier
CAMERAS /
COMPUTER VISION
PARKING /
OTHER
SENSORS
IN VEHICLE
SYSTEMS
BUILDING
MANAGEMENT
LOCATION
AR/VR
ROBOTS
/ ZDT
GPS
MACHINE
CONTROLS Private / Public Cloud Private / Public Cloud

12. 12
Who is Elastic?
• Elastic is a search company
• Creators of the Elastic Stack
– Elasticsearch (Data Store, Search, and
Analysis)
– Kibana (Visualization and Management)
– Beats (Ingest-Endpoints)
– Logstash (Ingest-Data Pipeline)
• Elastic builds offerings that make
data usable in real time and at scale
for:
– Search
– Logging
– Application Performance Monitoring (APM)
– Security
– Business Analytics
• Customers Include:
– Dell Technologies
– Barclays
– eBay
– Goldman Sachs
– Microsoft
– The Mayo Clinic
– NASA
– The New York Times
– Wikipedia
– Verizon

13. 13
What is the Elastic Stack?
https://www.elastic.co/webinars/elasticsearch-sizing-and-capacity-planning

14. 14
Elasticsearch - Dell Stack
Search. Observe. Protect. - At Elasticsearch we believe that search is a the solution to any problem,
whatever the data set may be when you are looking at logs, metrics, security events, infrastructure and
application performance, you are searching through data. With the power of Elasticsearch running on Dell
hardware we can provide a plug and play scalable data analytics solution.
• Search - we bring the power of enterprise and
workplace search allowing you to query petabytes
of data in milliseconds.
• Observe - whether it is logs, metrics, or APM we
are bringing a holistic view of how to monitor your
enterprise infrastructure from the cloud to bare
metal.
• Protect - through our SIEM and end point
protection tools we are eliminating by the end point
pricing.
Use Cases
• Security and IT Ops
• Customer 360
• Fraud Detection

15. 15
Why Customers Are Interested in Elastic?
Technology Differentiation
• Scale - Massively Scalable
Distributed Design
• Speed – Results in Milliseconds
• Relevance – Highly Relevant
Results
• Dell Customer Example:
– Large North American Bank able to
ingest 25TB in 18 hours with minimal
VMware footprint on VxFlex
Business Differentiation
• Numerous Customers are
Seeking Alternatives for Machine
Data and Search
– Reduced Licensing Costs
– Dell Customer Example:
– Global Technology and Entertainment
company where just one of hundreds of
use cases includes ingesting 100TB/day
from endpoint devices

16. 16
Example Dell Reference Architecture

17. 17
Why Dell Technologies?
• Elastic Technology Partner
• Elastic Customer
– Dell Recognized at Denver Elastic{on} 2020
Keynote
• Validated Elastic Solutions
– VxFlexOS
– ECS and Isilon for Elasticsearch Snapshot
• Breadth of Infrastructure Portfolio
– PowerEdge
– VxRail (vSAN)
– VxFlex (VxFlexOS)
– High Performance SAN
– ECS Object Storage
• Elastic OEM Reseller
• Elastic Infrastructure
Growth/Consumption Models
– Dell Financial Services

18. 18
Advanced Analytics Platform

19. 19
SQL Server 2019 solves modern data challenges
Modern platforms with compatibility
ARM64
Built-in Machine Learning and
extensibility
R
Intelligent performance Layers of security and compliance
ciphertext
plaintext
Data virtualization and Big Data
Clusters
Microsoft
SQL Server
SQL
Business critical high availability

20. 20
The same abstraction layer with SQL Server on Linux
SQL Platform Abstraction Layer
(SQLPAL)
RDBMS IS AS RS
Windows Linux
Windows Host Ext.
Linux Host
Extension
SQL Platform Abstraction Layer
(SQLPAL)
Host extension mapping to OS system calls
(IO, Memory, CPU scheduling)
Win32-like APIsSQL OS API
SQL OS v2
All other systems
System resource &
latency sensitive code paths
Choice across OS and containers

21. 21
What is SQL Server PolyBase?
SQL Server
T-SQLAnalytics Apps
ODBC NoSQL Relational databases Big data
PolyBase external tables
 Distributed compute engine integrated with SQL Server
 Query data where it lives using T-SQL
 Distributed, scalable query performance
 Manual/deploy with SQL Server
 Auto deploy/optimize with Big Data Clusters
 Easily combine across relational and non-relational data
stores
“It’s all about Data Virtualization”

22. 22
Microsoft SQL and Big Data
 Facilitates simplified access to desperate data types
 Reduces the need to move (ETL) data from source systems
 Allows for standard T-SQL queries across Relational and Non-Relational data sets
 Brings Data Science to Microsoft SQL environments
 Includes full AI / ML framework and engine
 No additional costs. All functionality included in SQL license
SQL Server Big Data Clusters

23. 23
Big Data Clusters deployed on Kubernetes (K8s)
SQL Sever 2019 Cluster (BDC)
Compute pool
SQL Compute
Instance
SQL Compute
Instance
SQL Compute
Instance
…
External data sources
IoT data
Directly
read from
HDFS
Storage pool
Persistent storage – plus HDFS Tiering
… SQL
Server
Spark
HDFS Datanode
SQL
Server
Spark
HDFS Datanode
SQL
Server
Spark
HDFS Datanode
Kubernetes pod
AnalyticsCustom apps BI
SQL Server
master instance
Node Node Node Node Node Node Node
SQL
Data pool
SQL
Datapool
Instance
SQL
Datapool
Instance
Storage Storage
Application pool
.Net, Java
Python, R
Applications
Kubernetes Control Plane
Controller Svc
Configuration Store (SQL Server)
Grafana
Elastic Search
Proxy
InfluxDB
Kibana
Applications
SQL Compute
Instance
SQL Compute
Instance
Data Science
Spark jobs
External tables with
Polybase++
AI / ML Services

24. 24
Compute Pools
The compute pool is a cluster of servers each
running SQL Server for the purpose of
supporting distributed queries
• Compute pool instances query partitions from external data
sources in parallel
• Compute pool instances work together to perform cross-
partition aggregations and sorts
• Compute pool offloads query execution from the master
instance and provide scale-out compute capacity
• Compute pool works over data pool, storage pool, or
external data sources
• Compute pool is stateless and can be scaled up/down as
needed
Scale-out data pool
Cosmos DB Oracle
Shard 1 Shard nShard 2
SQL Server
T-SQL
PolyBase external tables
SQL
Server
HDFS Data Node
Spark
SQL
Server
HDFS Data Node
Spark
SQL
Server
HDFS Data Node
Spark
Scale-out compute pool
Compute
instance
Compute
instance
Compute
instance
Compute
instance
Direct,
no cache
Direct,
no cache

25. 25
Data Pools
The Data pools are similar to a Data Mart –
caching data.
– Data is automatically distributed across the data pool
instances
– The data pool can accelerate data processing as it uses
sharding across the SQL Server instances.
– Scale the data pools as needed for better performance
• Cache and combine datasets from external sources without
writing code
• Offload query execution from source databases
• Columnstore indexes are automatically applied to compress
data storage and boost query performance by up to 10x
• Filtering and local aggregations happen in parallel across the
data pool instances
Scale-out data pool
Cosmos DB Oracle
PolyBase
connectors
Shard 1 Shard nShard 2
SQL Server master instance
T-SQL
PolyBase external tables
SQL
Server
HDFS Data Node
Spark
SQL
Server
HDFS Data Node
Spark
SQL
Server
HDFS Data Node
Spark

26. 26
Storage Pools
Storage pool is a group of pods hosting the
following services: SQL Server engine, HDFS
data node, and Spark.
• HDFS name node deployed
• Mount external HDFS with Tiering
• SQL Server engine in pool reads HDFS directory
• sqlhdfs connector
• webhdfs or livy endpoints
• YARN deployed
• Zookeeper for HA
• Compute Pool used if exists
Scale-out data pool
Cosmos DB Oracle
Shard 1 Shard nShard 2
SQL Server
T-SQL
PolyBase external tables
SQL
Server
HDFS Data Node
Spark
SQL
Server
HDFS Data Node
Spark
SQL
Server
HDFS Data Node
Spark
Scale-out compute pool
Compute
instance
Compute
instance
Compute
instance
Compute
instance
Direct,
no cache
SparkML
SparkSQL

27. 27
Using a Big Data Cluster through Endpoints
sql-server-
master
• SQL Server
Master
Instance
gateway
• Gateway to
access
HDFS files,
Spark
webhdfs
• HDFS File
System
Proxy
livy
• Proxy for
running
Spark
statements,
jobs,
applications
app-proxy
• Application
Proxy
sql-
server-
master-
readonly
controller – Cluster Management Service
"/api/v1/bdc/endpoints

28. 28
Why SQL Server Big Data Clusters?
Deployed single solution
 Deployed SQL Server with built-in Availability Groups and Active Directory Authentication
 Use Data Virtualization through deployed Polybase Scale Out Groups
 A deployed HDFS Cluster
 A deployed Spark engine
 A deployed built-in Data Mart for cached or offline results using a built-in PolyBase connector
 Access to Machine Learning with SparkML and SQL Server Machine Learning Services
 Deploy applications such as Machine Learning Models and SSIS packages
 Deployed set of services for control, management, and monitoring
 Use the power of containers and Kubernetes for basic HA, storage, networking, and scale.

29. 29
SQL Server 2019 – on-premises
SQL data engine
Scale out compute and caching
to boost performance
T-SQLAnalytics Apps
SQL Server External Tables
Compute pools and data pools
Open DB
connectivity
NoSQL Relational
databases
HDFS
SQL server, Spark and Data Lake
Store high volume data in a data lake and
access it easily using either SQL or Spark
Admin portal and management services
Integrated AD-based security
SQL Server
Scalable, shared storage (HDFS)
Spark
Complete AI platform
Ingest and prep data and then train, store,
and operationalize models all in one system
REST API containers for models
SQL Server
ML Services
External data sources HDFS
Spark &
Spark ML
Windows or Linux OS (Containers)
Dell EMC
Storage
(block, file, object)
Data Protection
(HW and SW)
CI | HCI Servers Networking

30. 30
Machine Learning

31. 31
The AI Technology Enablers
1950 1960 1970 1980 1990 2000 2010 2020
Cost of Compute
Amount of Data
Artificial Intelligence
Machine Learning
Deep
Learning
- Artificial Intelligence (AI) is human intelligence mimicked by
machine algorithms, examples: Chess, Go, Facial Recognition
- Machine Learning (ML) is a subset of AI algorithms to parse
data, learn from data, and then make a determination or
prediction, example: Spam Detection, Preventative
Maintenance
- Deep Learning (DL) a subset of machine learning algorithms
that leverage artificial neural networks to develop relationships
among the data, examples: Driverless Cars, Cyber-Security
Traditional Programming
Machine Learning

32. 32
AI is Transforming All Industries
• Alerts and diagnostics from real-
time patient data
• Disease identification and
risk stratification
• Patient triage optimization
• Proactive health management
• Healthcare provider sentiment
analysis
Healthcare & Life Sciences
• Predictive maintenance or
condition monitoring
• Warranty reserve estimation
• Prosperity to buy
• Demand forecasting
• Process optimization
Manufacturing
• Risk analytics and regulation
• Customer segmentation
• Cross-selling and up-selling
• Sales and marketing campaign
management
• Credit worthiness
evaluation
Financial Services
Retail
• Predictive inventory planning
• Recommendation engines
• Upsell and cross-channel
marketing
• Marketing segmentation
and targeting
• Customer ROI and lifetime value
• Cyberattack intrusion attempt
detection, analysis
• Smart power, transportation
design for resiliency
• Terrorist threat prediction
• Socioeconomic trends
and population planning
Government
• Power usage analytics
• Seismic data processing
• Carbon emissions and trading
• Customer specific pricing
• Energy demand and supply
optimization
Energy
• Vehicle crash avoidance systems
• Smart traffic routing
• Public transportation planning for
maximum mobility
• Smart service vehicles for optimal
routes, autonomous
navigation
Transportation
• Aircraft scheduling
• Dynamic pricing
• Social media-consumer feedback
and interaction analysis
• Consumer complaint resolution
• Traffic patterns and
congestion management
Travel & Hospitality
IDC: By 2025, at least 90% of new enterprise applications will embed
artificial intelligence

33. 33
Driven to be Disruptive – OTTO Motors
Opportunity
Material handing automation (which can account for up to 70% of
the final cost of good) offers the potential for significant
performance improvements and allows facility operators to focus
more resources on core, value-added operations.
Solution
OTTO, the self-driving vehicle, is designed exclusively for material
transport in industrial environments such as manufacturing
facilities and warehouses. The use of Dell technology allows
OTTO to work with a single provider that
understands the need for performance, security and the necessity
of a cost-effective and scalable solution.
Benefits
By partnering with Dell Technologies, and harnessing the power of
cloud, big data and IoT, OTTO Motors is democratizing robotics
technology for their customers, and making it possible for
companies of all sizes to adopt self-driving vehicles in their work
environments.
“We have, over the last few years, evolved
significantly into an autonomous systems and
software company, and with that, really taken the
promise of data collection and data analysis to
heart. I’d say that it’s something we’ve rapidly seen
the benefit from, and are rapidly encouraging the
adoption of.”
- OTTO CTO -

34. 34
Why AI Now?
Algorithms
 Deep Neural Networks (DNN)
Big Data
 Leveraging more data (unstructured)
AI Accelerators
 Over 1,000,000x improvement in
processing power since 2008
 Accept bigger DNN models
 Ingest more data

35. 35
AI Accelerators
Flexibility Efficiency
and many more…

36. 36
Vsphere User Defined Virtualization VSphere Network Attached AI
Abstract Pooling, Sharing and Automating
GPUGPU GPU
GPU
GPU
GPU GPUGPU
GPU GPU GPU GPU
GPU GPUGPU GPUGPU GPU
GPUGPU GPU
GPU
GPU
GPU GPUGPU
GPU GPU GPU GPU
GPU GPUGPU GPUGPU GPU
NETWORK
GPUGPU GPU
GPU
GPU
GPU GPUGPU
CLOUD
GPU GPU
GPU GPU
GPU GPU
GPU
GPU GPUGPU GPU
GPU GPUGPU GPU
GPU GPU
Maximize UtilizationMaximize Efficiency
GPU Virtualization Economics

37. 37
AI Magic Quadrants
Data science and machine-learning platforms are defined as:
• A cohesive software application that offers a mixture of basic building blocks
essential both for creating many kinds of data science solution and incorporating
such solutions into business processes, surrounding infrastructure and products.
Cloud AI developer services are defined as:
• Cloud-hosted services/models that allow development teams to
leverage AI models via APIs without requiring deep data
science expertise
Data Science and Machine Learning Platforms Cloud AI Developer Services
The Marketplace
Continues To
Evolve

38. 38
Data Analytics and AI Use Cases – Partner Solutions
IOT / Streaming /
Machine Data Analytics
Deliver Near Real-Time
Analytics
• Analyze IOT / Streaming
data
• Improve IT operations and
security leveraging Machine
Data
• Computer vision
applications
Machine / Deep Learning
Transform the business
with analytical insights
• Data Science / Machine
Learning Platform
• Industry-focused AI
platforms
Data Lake/Unstructured
Data Infrastructure
Improving Data Access
and Agility
• Create an enterprise data
platform for structured and
unstructured data
• ETL offload to lower costs
• On-demand deployment of
container-based
environments
Augmented Analytics
and Data Warehouse
Improve Decision
Making
• Support augmented
business analytics
• Create an enterprise data
platform to support
analytics
• Data integration and
Master Data Management
*Note, some products can deliver capabilities that address multiple use cases

39. 39
The Next Generation Analytics Platform - ThoughtSpot
No More Waiting
Get answers instantly without relying
on data analysts for every report request
Incredibly Easy to Use
Anyone can use search to build
reports and dashboards in seconds
Shared Company-wide Metrics
Get everyone in the company looking
at one single version of the truth
BUSINESS PEOPLE
Search to Analyze Data
SpotIQ Automated Insights
Scales to Billions of Rows
Easy Cross-Source Analysis
Lightning Fast Performance
Company-Wide Governance
No Performance Tuning
Stop building cubes, aggregate views, summary
tables or data marts to optimize for performance
Centralized Control & Visibility
Easily manage row-level access for thousands of users with
centralized governance
Bye, Bye, BI Backlog
Eliminate your reporting backlog and
focus on more strategic data initiatives
DATA PROFESSIONALS BENEFITS
By 2020, 50% of analytic queries will be generated using search, natural-language
processing or voice, or automatically generated.

40. 40
Customer and Employee Health and Safety Solutions
• Detection of persons/objects
• Display showing temperature differences accurate
to 0.1°C
• Alarm in case of exceeding or falling below defined
temperature ranges
• Event Triggers (alarm, network message, activation
of a switching output)
• Temperature range from -40 to +550 °C
•Face Redaction for privacy
Dell Workstation
with NVIDIA
Dell Technologies Surveillance Solutions
- Open Data Lake Platform
- Scalable Infrastructure
- Analytics-ready
Image, Video and Thermal-based AI Applications
Applications
- Fraud Detection
- Loss Prevention
- Workplace Accident Reduction
- Customer Insight
- Public Safety
- Counter Terrorism

41. 41
The Digital Future Demands a New Perspective
Cloud First Data First
Infrastructure-centric Business-centric
Takes into consideration:
• Data gravity
• Data velocity
• Data control
• Data privacy and compliance
Driven by:
• Lower infrastructure CapEx
• Offload infrastructure maintenance
• Improve time to market (deployment
time for infrastructure)
Evolve to a Data-Driven Business

42. 42
Skill Gap, Use Case Identification, Strategy and Funding Are Key
Challenges to Be Overcome for Organizations to Adopt AI
What are the top three challenges to the adoption of artificial intelligence for organizations?
54%
37% 35% 35%
30%
27%
23%
Lack of necessary
staff skills
Defining our AI
strategy
Identifying use
cases for AI
Funding for AI
initiatives
Security or privacy
concerns
Complexity of Determining how to
integrating AI with measure value from
our existing using AI
infrastructure
Base: n = 83 Gartner Research Circle Members
Q08. What are the top three challenges to the adoption of artificial intelligence within your organization?

43. 43
Top tasks likely outsourced to machines
“Which tasks do you anticipate organizations will outsource to machines/automate by 2030?” Base: 3800
The Next Era of Human-Machine Partnerships Study – Dell Technologies
Almost all (96%)
respondents think
that organizations
will outsource tasks
to machines /
automate by 2030
On average,
respondents
identified 5
processes ripe for
automation within
Inventory management
Financial admin (i.e. invoicing, POs)
Trouble-shooting
Logistics/supply chain (e.g. delivery drivers)
Administration: scheduling meetings, data input
Product design
Customer service
Marketing and communications
HR admin: recruitment and training
Medical/health diagnoses
Legal admin (i.e. drafting and amending contracts)
Management of employees
Sales
Surgery
Caring for the elderly
Educating children
I do not think organizations will automate anything
Don’t’ know
42%
41%
39%
37%
37%
33%
32%
29%
27%
24%
22%
21%
18%
17%
13%
12%
2%
2%

44. 44
The Value of Dell for AI Infrastructure
- Comprehensive and Scalable AI/Analytics Platform Portfolio
- Workstations, Servers, Clusters, Storage, Networking
- Infrastructure and Data Science and Analytics Expertise
- HPC and AI Innovation Lab
- IoT / Intelligent Video Analytics Lab
- Solution-based Offerings
- Pre-configured AI Ready Offerings
- IoT / Safety and Security and
Thermal Vision Solutions
- GPU Virtualization
- ML Platforms
Infrastructure
Scalability
Reduce
Complexity
Address
Demand
Partner
Ecosystem
Cost
Effective

45. 45
- Appendix -
Dell Technologies
AI and Data Analytics Solutions

46. 46
Dell Technologies AI and Data Analytics Solutions
AI / Machine Learning / Deep Learning
• Domino Data Science Platform Design Document
• HPC for AI and Data Analytics Ready Architecture
• Retail Loss Prevention Ready Solutions
• DataRobot Reference Architecture
• H2O AI Reference Architecture
• Kubeflow Reference Architecture
• OneConvergence Dkube Reference Architecture
• Iguazio Reference Architecture
• Deep Learning with NVIDIA Ready Solutions
• Isilon with NVIDIA DGX-1 Reference Architecture
• Isilon with NVIDIA DGX-2 Reference Architecture
• Isilon with Dell Precision 7920 Data Science Workstation Reference Architecture
• Isilon with Dell EMC DSS8440 Reference Architecture
• Noodle.ai (OEM) Solution Bundle
IoT / Streaming / Machine Data Analytics
• IntelliSite (OEM) Thermal Detection Solution
• Retail Loss Prevention Ready Solutions
• Dell IoT Safety and Security Portfolio
• Real-Time Data Streaming Ready Architecture
• Splunk Enterprise on Dell EMC Infrastructure
• Streaming Data Platform
• ElasticSearch (OEM) Solution Bundle
© Copyright 2020 Dell Inc.
Augmented Analytics and Data Warehouse
• Spark on Kubernetes
• Kinetica (OEM) Solution Bundle
• ThoughtSpot (OEM) Solution Bundle
• Pivotal Greenplum
• Dell Boomi
Data Lake / Unstructured Data Infrastructure
• Microsoft SQL Server 2019: Big Data Cluster Ready Solution
• Cloudera Hadoop Ready Architecture
• Hortonworks Hadoop Ready Architecture
• Kubernetes Containers with Diamanti (OEM) Solution Bundle
• Grid Dynamics Reference Architecture
• Red Hat OpenShift Reference Architecture
HPC Ready Solutions
• HPC Digital Manufacturing
• HPC Life Sciences
• HPC Research
• HPC BeeGFS Storage
• HPC Lustre Storage
• HPC NFS Storage
• HPC PixStor Storage
*Note, some products can deliver capabilities that address multiple use cases
Product Offerings and Technical Collateral for Analytical Use Cases

47. 47
Three solutions, powered by Dell Technologies OEM
infrastructure, to provide enterprise search, observability,
and endpoint security of large enterprise
organizations
Reliably and securely take data from any source, in any
format, then search, analyze, and visualize it in real time
• Easily implement powerful,
modern search experiences
across your website, app, or
digital workplace
• Bring your entire IT
Infrastructure logs, metrics, and
traces together into a single
stack so you can monitor,
detect, and react to events with
speed
Streaming analytics
OEM Solution
Bundle with
Elastic Search
LARGE MULTI-TENANT - Based on VxRail or VxFlex platforms
SMALL AND MEDIUM - Based on PowerEdge R740xd
© Copyright 2020 Dell Inc.
https://www.elastic.co
https://infohub.delltechnologies.com/l/elastic-stack-solution-on-dell-emc-
vxflex-family/integrated-rack-18

48. 48
Ready Solution for
Microsoft: SQL Big
Data Cluster
• Enable intelligence over all
data
• Remove limitations created
from data silos
• Simplify provisioning,
management and orchestration
of container storage
Microsoft SQL Server 2019 Big Data Cluster hosted on Dell EMC infrastructure
for a scalable data management and analytics platform
Data Lake analytics
https://infohub.delltechnologies.com/t/microsoft-sql-server-2019-big-data-clusters-a-
big-data-solution-using-dell-emc-infrastructure/
Ingest data from multiple sources, regardless
of location or type, to remove the boundaries
that inhibit comprehensive data analysis
© Copyright 2020 Dell Inc.

49. 49
Analytics platform that delivers search and AI-driven
analytics at human scale
• Built specifically for business
users to enable “self-service”
• Analyze billions of rows at sub-
second speed
• Fast drill down analysis, gets to
your answers quickly
Configuration
Small (1TB Cluster)
Medium (3TB Cluster)
Large (10TB Cluster)
Streaming analytics
OEM Solution
Bundle with
Thoughtspot
PowerEdge C4140
© Copyright 2020 Dell Inc.
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