please read – instructions for adding page numbers “x … · rdbms data lake (hadoop) machine...
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Data Analytics
Nagesh Madhwal
Client Solutions Director, Consulting,
Southeast Asia, Dell EMC
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3 Dell - Internal Use - Confidential
Next 15 yearsBusiness-centric
Cloud-Native Applications
Prescriptive Analytics
Agile Infrastructure
Internet of Everything
Last 15 yearsIT-centric
Traditional Applications
Traditional Analytics
Rigid Infrastructure
Internet
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Evolution of Workloads
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Your business must evolve.
Future success is achieved
by unlocking ALL DATA.
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When it comes to Applications
and Data Analytics
TIME is everything.
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The Power of “Now”
Business Event “Moment of Impact”
Data Captured
Intelligence Delivered
Decision Taken
Valu
e
Time
Real time Batch
Big Data
Opportunity Missed Opportunity “Too Late To Take Action”
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Traditional Analytics
Limited to static data
Reactive and slow
Restricted sources and access
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Traditional ..
Data Warehouse
Reporting
Source Systems
Files
Sources 1 to x
Batch Data SourcesExisting DBs, ERP
Staging Transform
ation
ETL Data Marts
Analytics
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Traditional…
Data Warehouse
Reporting
Source Systems
Files
Sources 1 to x
Batch Data SourcesExisting DBs, ERP
Staging Transform
ation
ETL
Data Marts
Analytics
ETL
ETL
ODS
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Modern Analytics
Analyze ALL data
Deliver anywhere, anytime analytics
Empower your end users
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The Data Lake
12
ETL Offload | Analytics as a Service | Data Science | Decision Support |
Data Visualization | Executive Reporting | Predictive Modeling | Threat Analysis
Structured Unstructured
Managed using NoSQL
Static Schema
RDBMS/EDW
Dynamic Schema
Hadoop Eco Sys
file types: videos, pdf, ppt,
mp3, doc, email, pics
Managed using SQLdata types: numeric, currency,
alphabetic, name, date, address
Sources:
• ERP
• CRM
• SCM
• POS
Sources:
• Social
• IOT
• Text
• Geo
• Media
Use Cases /APPS
Kafka
BigSQLSqoop
Spark Streaming
Hive
FlumeImpala
HAWQ…
Application-Integrated Protocols
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Modern Data Lake Environment
All Data
Fed Into
The
Data
Lake
HADOOP
DATA LAKE
ETL
- Exploratory, Ad Hoc
- Unpredictable Load
- Experimentation
- Loosely Governed
- Best Tools
- Production
- Predictable Load
- SLA Drive
- Heavily Governed
- Standard Tools
DWH
MPP
Analytics
Sandbox
Analytics / Sandbox Environment
Data Prep & Enrichment
ISILON SCALE OUT
NAS STORAGE
Foundation of your Data Management and Analytics
Architecture
Active Archive
BI / DWH Environment
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Reference Architectures
DATA LAKE (HADOOP)RDBMS
MACHINE IOT
STATISTICAL MODELING/NLP EXPLORATION
TRANSFORM
BI
ORGANIZE MANAGE/CATALOG
DATA WAREHOUSESTREAMCEP
NEARREAL-TIME
MODELS MAY TAKE HOUR OR DAYSQUERIES MAY RETURN IN SECONDS OR MINUTES
SECONDS
SEARCH/INDEX
ENTERPRISE LOG ANALYSIS
APPLICATIONS
3rd PARTY
SOCIAL MEDIA SQL ON HADOOP
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Big Data Journey To BDaaS
BDaaSAgility
Valu
e
Control
Prototyping
Dev/Test and Pre-Production Lifecycle Management
< 20 Nodes
Template Libraries, Shared Data Across Clusters, Rapid Prototyping and Evaluation
Dev / Test Lab
Multiple Hadoop Distros, Spark, Transient Workloads
Departmental
Hadoop and Spark in a Secure Production Environment
20+ Nodes
Performance, Security, Capacity Prioritization, Compute/ETL Offload,
Separate Compute/Storage
Dev/Test/Stage/QA/UAT
Improve Utilization, Scaling, Consistent Data
Big-Data-as-a-Service
Multi-Tenant Hadoop and Spark Deployment On/Off Premise
50+ Nodes
Multi-Tenancy, Self-Service, Logs, APIs, Tenant/Admin
Controls, Shared Data Lake with Access Controls
Heterogeneous Production Environments, Diverse Tenants
and User Groups
Support Multiple LOBs, Dynamic Resource
Management/QoS, Automation
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Multi-Tenant Big-Data-as-a-Service
Multiple lines of business, multiple user
groups
Multiple use cases
Multiple ecosystem products
(including non-Hadoop, BI/ETL
tools)
Compute isolation between tenants
Multiple environments
per tenant
Multiple versions and/or
distributions
Data isolation by tenant (incl.
ability to physically isolate storage)
Data/Storage
Prod Dev/Test POC Prod Dev/Test
Data Isolation
Data Isolation
MARKETING R&D MANUFACTURING
360 Customer View Log Analysis Predictive Maintenance
MARKETING R&D MANUFACTURING
Shared, Centrally Managed Server Infrastructure
Compute Isolation
Compute Isolation
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Analytics
Infrastructure
Integration
Range of solutions
44% of organizations still struggle with how to approach Big Data
S e r v i c e s
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Data Analytics Journey
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Big Data Systems
• VxRail
• VxRack
• Vblock
• XC Series
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Big Data Foundations
• Servers
• Storage
• Networking
• Software
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Big Data Solutions
• Reference Architectures
• Engineered Solutions
• Customized Designs
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BUSINESS
TECHNOLOGY
DEPLOYASSESS PROVE
Big Data
Proof of Value
Big Data
Proof of
Technology
Big Data
Applied Analytics
Implementation
Big Data
Technology
Implementation
Big Data Vision
Workshop
Big Data Technology
Advisory
DELL EMC Big Data
Portfolio Implementation
Global Services
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Subject Details
Workshop Objective To understand the key business initiatives and requirements for big data in order to understand where
and how to start the big data journey
Workshop Vision How to become data driven through the use of big data and analytics
Workshop Duration Half Day or 1 Day on customer site
Workshop Agenda 1) Business & IT Goals
2) Business Initiatives
3) Current Environment Review
4) Use Cases
5) Data Sources Review
6) Data Science / Analytics / BI Requirements
Recommended types of participants: IT and Business Users
Expected Outcomes 1) List of business opportunities and use cases
2) Business Value and Feasibility
3) Identify and prioritize data sources mapped with use cases
4) Prioritized used cases with potential business value and impediments
5) Document workshop results
6) Big Data Technology Roadmap with clear next steps
Next Step After Workshop Proof of Value (POV) or Proof of Technology (POT)
Dell EMC Workshop Team Head of Big Data Practice SEA, Head of Consulting SEA, Dell EMC Account / Sales Manager
Example of a Big Data Workshop
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Customer Success Stories
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