Delivering Hadoop-as-a-Service To Your Organization

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1 1 Delivering Hadoop-as-a-Service To Your Organization

2 2 Why Hadoop? Fast and Cheap Way For Exploiting Massive Amounts of New Data Sources Internet of Things Mobile Sensors Social Media Video Surveillance Dark Data Smart Grids Oil Exploration Medical Imaging

3 Why Hadoop? Save Money Or Make Money Improve Company Performance Increase Revenue Reduce Costs Increase Demand Manage Demand Build Brand Equity Click Fraud Production Cost / Efficiency General and Administrative Increase Spend Efficiency Increase Customer Acquisition Increase Customer Engagement Increase Basket Size Demand Analysis Price Optimization Increase Reach Improve Customer Loyalty Transaction Anomaly Detection Supply / Demand Forecasting Workforce Analytics Employee Churn Ad Optimization Hyper Targeting Campaign Optimization Ad Effectiveness Analytics Purchase Funnel Analysis Customer Segmentation Churn Prevention Customer Lifetime Value Affinity Analytics Next Best Offer Cross-Sell / Upsell Digital Marketing Social Media Social Graph / Influencers Loyalty Program Analytics Customer Satisfaction Customer Care Analytics IT / Security Analytics Market Mix Modeling Coupon Redemption Copyright 2014 EMC Corporation. All rights reserved. 3

4 Hadoop Overview Hadoop is an open-source framework from Apache that allows for parallel batch processing of very large data sets MapReduce is the Hadoop process that divides the workload so multiple devices can process it HDFS is the file system for the data. It provides data protection and locality with multiple mirrors (usually 3 times) Copyright 2014 EMC Corporation. All rights reserved. 4

5 5 IT Challenges With Hadoop Time consuming and complex creating shadow IT Bare metal capacity utilization is low Multiple Hadoop Distribution deployments creating data siloes

6 6 Typical Enterprise Deployment Multiple, siloed clusters to manage Dept A: Recommendation engine Production Dept B: Ad targeting Production Redundant common data in separate clusters Test Log files Test Peak compute and I/O resource is limited to number of nodes in each independent cluster Experimentation Experimentation Historical cust behavior Social data

7 7 What If You Consolidate & Virtualize? Recommendation engine Production Ad targeting Production Experimentation Test/Dev Test Test Production recommendation engine Production Ad Targeting Experimentation Experimentation One physical platform to support multiple virtual big data clusters

8 8 EMC Hadoop Starter Kit Consolidate And Virtualized Hadoop With EMC Isilon And Vmware Support for major Hadoop distributions NameNode Data Quickly deploy, manage, and scale Hadoop clusters GUI simplifies management tasks nam e node nam e node nam e node nam e node data node HDFS Apache Elastic scaling optimizes cluster performance and resource utilization

9 9 Why Shared Storage For Hadoop?

10 10 Hadoop Bare Metals Deployment 1 2 Dedicated Storage Infrastructure One-off for Hadoop only Lacking Enterprise Data Protection No Snapshots, replication, backup Hadoop DAS Environment 1x NameNode 1x 1x 3 Poor Storage Efficiency 3X mirroring 2x 2x 4 Fixed Scalability Rigid compute to storage ratio 2x 3x 5 Manual Import/Export No protocol support 3x 3x

11 11 Hadoop On EMC Isilon Scale Out NAS Scale-Out Storage Platform Multiple applications & workflows End-to-End Data Protection SnapshotIQ, SyncIQ, NDMP Backup Industry-Leading Storage Efficiency >80% Storage Utilization Independent Scalability Add compute & storage separately Multi-Protocol Industry standard protocols NFS, CIFS, FTP, HTTP, HDFS

12 12 EMC Isilon Addresses Hadoop Challenges 1 Dedicated Storage Infrastructure One-off for Hadoop only 1 Scale-Out Storage Platform Multiple applications & workflows 2 Lacking Enterprise Data Protection No Snapshots, replication, backup 2 End-to-End Data Protection SnapshotIQ, SyncIQ, NDMP Backup 3 Poor Storage Efficiency 3X mirroring 3 Industry-Leading Storage Efficiency >80% Storage Utilization 4 5 Fixed Scalability Rigid compute to storage ratio Manual Import/Export No protocol support 4 5 Independent Scalability Add compute & storage separately Multi-Protocol Industry standard protocols NFS, CIFS, FTP, HTTP, HDFS

13 13 Why Virtualize Hadoop?

14 Hadoop with Virtualization Elastic, Multi-Tenant VM Combined Storage/ Compute VM VM Comput e Storage VM VM VM T1 T2 Storage Hadoop in VM VM lifecycle determined by Datanode Limited elasticity Limited to Hadoop Multi-Tenancy Copyright 2014 EMC Corporation. All rights reserved. Separate Storage Separate compute from data Elastic compute Enable shared workloads Raise utilization Separate Compute Tenants Compute cluster per tenant Stronger VM-grade security and resource isolation Enable deployment of multiple Hadoop runtime versions 14

15 15 Virtualized Hadoop Performance Native vs. Virtual, 32 hosts, 16 disks/host Source:

16 16 Example Deployment With Pivotal HD Pre-requisities Isilon OneFS version or higher VMware vsphere 5.0 (or later) Enterprise or Enterprise Plus Download Vmware Big Data Extensions (Free) Configure Isilon cluster for HDFS (Free license) Configure Big Data Extensions to use Pivotal HD Deploy Hadoop Cluster Run a simple program to test

17 17 Hadoop Data Services Real-time, Interactive, And Batch Processing

18 18 WGSN Retail Challenges Rapidly launch new market intelligence service for fashion retailers Support large and growing volumes of Big Data Solution Performance, scalability, and tight integration with Hadoop were the key reasons we chose Isilon. We also felt very Pivotal Greenplum Database comfortable with the partnership between EMC and Pivotal HD Pivotal. In the end, the EMC and Pivotal solution offered EMC Isilon the ideal balance of storage and compute with the right Pivotal Data Science Labs level of support. Results Fast deployment with native Hadoop integration, enabling rapid launch of new service Delivered high performance scalability Simplified platform administration

19 19 Download Hadoop Starter Now Rapid provisioning High availability Elasticity Multi-tenancy Portability

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