Data Analytics

Nagesh Madhwal

Client Solutions Director, Consulting,

Southeast Asia, Dell EMC

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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

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

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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

EMAIL

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