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Dell Digital Transformation Through AI and Data Analytics Webinar

The document discusses the impact of COVID-19 on business operations, highlighting trends such as remote working, reliance on digital platforms, and the critical role of data analytics in decision-making. It emphasizes the importance of machine data and advanced analytics solutions, particularly through tools like the Elastic Stack and Microsoft SQL Server, to enhance performance and insights across various industries. The content further explores the growing significance of AI and machine learning in transforming business processes and improving operational efficiency.

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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 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 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 • 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 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 IoT / Streaming Analytics
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 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 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 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 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 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 What is the Elastic Stack? https://www.elastic.co/webinars/elasticsearch-sizing-and-capacity-planning
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 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 Example Dell Reference Architecture
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 Advanced Analytics Platform
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 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 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 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 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 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 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 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 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 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 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 Machine Learning
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 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 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 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 AI Accelerators Flexibility Efficiency and many more…
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 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 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 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 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 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 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 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 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 - Appendix - Dell Technologies AI and Data Analytics Solutions
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 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 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 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. A guided, intelligent, search experience that enables anyone to analyze data in seconds https://www.thoughtspot.com/

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Dell Digital Transformation Through AI and Data Analytics Webinar

  • 1.
    1 Digital Transformation Through Dataand 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 theNew 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 Portfolioof 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 BusinessChallenges 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.
  • 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 Machineand 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 Datainto 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 WeTypically 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 theElastic Stack? https://www.elastic.co/webinars/elasticsearch-sizing-and-capacity-planning
  • 14.
    14 Elasticsearch - DellStack 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 AreInterested 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.
  • 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.
  • 19.
    19 SQL Server 2019solves 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 abstractionlayer 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 SQLServer 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 andBig 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 Clustersdeployed 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 computepool 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 Datapools 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 poolis 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 BigData 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 ServerBig 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.
  • 31.
    31 The AI TechnologyEnablers 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 TransformingAll 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 beDisruptive – 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.
  • 36.
    36 Vsphere User DefinedVirtualization 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 Datascience 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 andAI 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 GenerationAnalytics 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 EmployeeHealth 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 FutureDemands 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, UseCase 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 likelyoutsourced 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 ofDell 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 - DellTechnologies AI and Data Analytics Solutions
  • 46.
    46 Dell Technologies AIand 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, poweredby 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 thatdelivers 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. A guided, intelligent, search experience that enables anyone to analyze data in seconds https://www.thoughtspot.com/