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Big Data and Implications on Platform Architecture
BIGS002
Fayé A Briggs, PhD Intel Fellow and Chief Server Platform Architect, Intel
2
Agenda
• What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action
The PDF for this Session presentation is available from our Technical Session Catalog at the end of the day at: intel.com/go/idfsessionsBJ
URL is on top of Session Agenda Pages in Pocket Guide
3
Agenda
• What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action
4
What is Big Data?
Time
Vol
ume
Structured Data
Unstructured Data
†”Big data: The next frontier for innovation, competition, and productivity”, McKinsey Global Institute
Analyze
Manage
Store
Capture Corporate Data
Big Sensed Data
Big Web Data
Big Corporate Data
datasets whose of typical database software tools to capture, store, manage and analyze†
size is beyond the ability volume, variety, value and velocity Unstructured
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Velocity Real-time rather than batch-style analysis Data streamed in, tortured, and discarded Making impact on the spot rather than
after-the-fact
The Four Pillars and Associated Challenges of Big Data
Volume Massive scale and growth of unstructured data 80%~90% of total data Growing 10x~50x faster than structured (relational) data 10x~100x of traditional data warehousing
Variety Heterogeneity and variable nature of Big Data Many different forms (text, document, image, video, ...) No schema or weak schema Inconsistent syntax and semantics
Value Predictive analytics for future trends and patterns Deep, complex analysis (machine learning, statistic modeling,
graph algorithms, …), versus Traditional business intelligence (querying, reporting, …)
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Big Data Has Gone Mainstream
Behold the Big Data sign gracing Times Square
Greeting commuters on Highway 101 in Silicon Valley, a giant Walmart* Big Data sign
“ . . . . every large company is working on Big Data projects “at a furious pace.” - Gartner analyst Merv Adrian
“. . . the use of Big Data will be effective for every segment of the economy.” - Michael Chui, McKinsey & Co. analyst
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Big Data Usages Examples (Telecom/Financial/Search)
• Telecom – Calling patterns, signal processing, forecasting Analyze switches/routers data for quality of call, frequency of
calls, region loads, etc. – Act before problems happen. Act before customer calls arrive.
• Financial – Trading behaviour Analyze real-time data to understand market behavior, role of
individual institution/investor – Detect fraud, detect impact of an event, detect the players
• Search Engines – Process the data collected by Web bot in multiple dimensions – Enhance relevance of search
Big Data impacts e-connected businesses through capture, processing and storage of huge amount of data efficiently
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Big Data Usages Examples (E-Biz, Media)
• Click Stream Analysis – Analysis of online users behavior – Develop comprehensive insight (Business Intelligence) to run
effective strategies in real time • Graph analysis
– Term for discovering the online influencers in various walks of life – Enables a business to understand key players and devise effective
strategies • Lifecycle Marketing
– Strategies to move away from spam/mass mail – Enables a business to spend money on high probable customers only
• Revenue Attribution – Term for analyzing the data to accurately attribute revenue back to
various marketing investments – Business can identify effectiveness of campaign to control expenses
Big Data phenomenon allows businesses to know, predict and influence customer behaviors!!!
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Big Data in Health Care
GenBank* is the NIH genetic sequence database, an annotated collection of all
publicly available DNA sequences
Sequencing costs approaches “$1000.” Analytics, compute, networking, & storage to improve
affordability still challenging.
Cancer Care: American Society of Clinical Oncologists “learning health system”, CancerLinQ • Benefits: Collects and analyzes de-anonymized cancer
care data from millions of patient visits Genome Sequencing: Improvements in DNA sequencing driving down costs of processing complete set of genomes • Benefits: Saving lives through better identification and
treatment of diseases
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Big Data Analytics Processing • “Batch”: Sophisticated data processing: enable “better”
decisions – Analyze, transform, scan, etc. large amount of data – E.g., ETL, graph construction, anomaly detection, trend analysis
• “Real-time”: Queries on historical data: enable “faster”
decisions – Data at rest but needs to be served in real-time – E.g., Facebook* uses HBase* “messaging” App serving real-time
data to its users
• “Streaming”: Queries on live data: enable “instantaneous” decisions on real-time streaming data – Large volume of data being ingested and analyzed in real-time – E.g., detect and block worms in real-time (a worm may infect
1mil hosts in 1.3sec)
Adapted from Ion Stoica, UC Berkeley
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Visualization
Analytics
Data Management
Big Data Converted to Knowledge in an Iterative Cycle†
External Unstructured/Semi-structured on Dist. Parallel File System (HDFS*, Lustre*, CloudStore*, GPFS, GlusterFS*, etc.)
Real-Time Processing HBase*, SAP* HANA, Spark,
Shark, Cassandra*, mongoDB, Drill, Impala
Stream Processing
Spark Streaming, Storm, S4–Simple Scalable Stream.
Sys
Batch Processing MR-Hadoop*,
GraphLabs,Giraph
Filtering, Cleansing, ETL, Scribe, etc.
Knowledge
Presentation Tableau*, R, Progress Software*, Pentaho*, IBM*, others
†Based on Frog’s & IDC’s Layered & Iterative Approach
Big Data Ingest
Archive
Compute
Transact
Deliver
Batch Analytics
Call Data Records Gene Seq & Analysis GraphLab*-MLearning, ETL, Search, Index Creation, Click Stream Analysis, BI Analytics
Real-time Analytics
Intelligent Transport Home Energy Financial Analytics Sensor Network Database Time Series Stock Ticks Customer Behavior Ads
Twitter* Spam Analytics Video meta-data Traffic Modeling Virus Intrusion Detection Stock trading Video Surveillance Retail – Video, PoS data
Streaming Analytics
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Agenda
• What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action
13
Telco Usage - China Mobile Group Guangdong Hadoop* Big Data storage and analytics
Analytics
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Telco Usage - China Mobile Group Guangdong Hadoop* Big Data storage and analytics
Usage Model: Deliver real-time access to Call Data Records (CDR) for billing self service • Solution: Hadoop* + Intel® Xeon® processors over RDBMS to remove
data access bottlenecks, increase storage, and scale system • Benefits: Lower TCO, up to 30x performance increase, stable operation,
analytics on subscriber usage for targeted promotions • Characteristics:
– 30TB billing data/month; real-time retrieval of 30 days CDRs – 300k records/sec, 800k insert speed/sec; 15 analytics queries , 133 server nodes+
Platform and Cluster Architectural Attributes: • Compute:
– Scale-out Intel Xeon processor E5 based platform for fast analytic query processing – Memory: 2-4GB/Core for Data Management Hadoop JVM – PCI Express* Gen3
• Storage: Scale-out Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast record inserts
and reads • Network: High network bandwidth for Hadoop Shuffle data; 10GbE TORS; 10-40GbE
Inter-Rack Switch
15
Real-Time Analytics: DuPont*– Crop Genetics HBase* Big Data analytics with “BLAST” to compare protein in genomic data
Usage Model: Comparative genomic research. Run “BLAST” to compare protein with every other protein in the genomic code. • Solution: The current RDBMS didn’t scale to planned data growth. HBase*/HDFS*
proved a reduction in processing time from over 30 days to less than 7 hours.
• Characteristics: – Genetic data for 4 million organisms – 1TB in size; scale to 14 million – 12 Trillion HBase rows; single record search takes 1.2 seconds
Platform and Cluster Architectural Attributes: • Compute:
– Scaleout Intel® Xeon® processor E5 base platform for real-time data serving – Memory: Higher Memory Capacity 4GB/Core for HBase Memstores – PCI Express* Gen3
• Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast data
access to HBase tables • Network: High network bandwidth for HBase region servers; 10GbE TORS; 10-
40GbE Inter-Rack Switch
16
Telco - China Unicom Hadoop* & HBase* for Behavioral Analysis
Subscriber Usage & Billing
ETL
• Log Analysis • Daily Reports
Storage, Analytics
New Customer Segmentation & Insights
17
Telco - China Unicom Hadoop* & HBase* for Behavioral Analysis
Usage Model: Analyze subscriber Web usage and billing to derive new information products • Solution: Scale out storage based on Hadoop* & HBase* with network optimization based on
Web traffic, log analysis for daily reporting • Benefits: New customer segmentation • Characteristics:
– 188 nodes, 14TB/server – 2.5PB raw disk capacity – High speed data loading – Real-time query (latency <1s) Daily statistics & reports (sum, count, join, etc.)
Platform and Cluster Architectural Attributes: • Compute:
– Scaleout Intel® Xeon® processor E5 based platform for real-time data serving in < 1 sec – Memory: Higher Memory Capacity 4GB/Core for HBase Memstores – PCI Express* Gen3
• Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast data
access to HBase tables • Network: High network bandwidth for HBase region servers; 10GbE TORS; 10-
40GbE Inter-Rack Switch
18
In-Memory GraphLab* Analytics: PageRank Big Data analytics with GraphLab
19
In-Memory GraphLab* Analytics: PageRank Big Data analytics with GraphLab
Usage Model: Deliver Page Ranking for search • Solution: Hadoop* + Intel® Xeon® processors: Large number of ML, Genomics,
Web, etc. applications can be efficiently run in Graph Parallel solution • Benefits: Significantly faster solutions to Graph(Vertices, Edges) domain
problems • Characteristics:
– XML docs, News Feeds, Web Pages – Data collected from Web Pages for Page Ranking
Platform and Cluster Architectural Attributes: • Compute:
– Scaleout Intel Xeon processor E5 based platform for fast analytic in-memory processing – Memory: 2-4GB/Core for Data Management Hadoop JVM; For graph data – PCI Express* Gen3
• Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast record inserts and
reads • Network: High network bandwidth for Hadoop Shuffle data during graph construction;
10GbE TORS; 10-40GbE Inter-Rack Switch
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Pipelined In-Memory Analytics: Twitter* Feed Spam Analytics Spark Streaming Big Data Analytics
• Run a streaming computation as a series of extremely small, deterministic batch jobs
• Batch sizes as low as ½ second, latency ~ 1 second
*
21
Pipelined In-Memory Analytics: Twitter* Feed Spam Analytics Spark Streaming Big Data Analytics
Usage Model: Process and filter out Twitter* feed spam as tweets are ingested • Solution: Spark Streaming from Berkeley • Characteristics:
– Data ingested from Twitter feeds
Platform and Cluster Architectural Attributes: • Compute:
– Scaleout Intel® Xeon® processor E5 based platform for fast analytic query processing – Memory: Very large Memory Capacity close to the CPU; High memory bandwidth – PCI Express* Gen3
• Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast record
inserts and reads • Network: High network bandwidth; 10GbE TORS; 10-40GbE Inter-Rack
Switch
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Smart City Sensor Model
Smart building sensors
Smart Grid sensors
Pollution sensors
Smart meters
Industrial automation
sensors
Portable medical imaging services
Medical sensors on ambulances
Sensors on smartphones
Meteorological sensors
Inductive sensors
Traffic cameras
INTELLIGENT CITY
INTELLIGENT HOSPITAL
INTELLIGENT FACTORY
INTELLIGENT HIGHWAY
Sensors on vehicles
Embedded
Cloud
Dedicated
HPC
Transactional
Social
Location
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The Fusion of Internet of Things and Big Data
Planogram monitoring (Real-time stock level)
Surveillance camera (store statistics)
Interactive display (Behavioral marketing)
Real-time transaction
Store heat map (hot merchandise, browsing history, conversion rate)
RFID
RFID
Dynamic pricing Real-time, personalized ad Auto promotion/coupon Social network connection
24
Smart Traffic Intelligent Transport System HBase* Application for Predictive Analytics
25
Smart Traffic Intelligent Transport System HBase* Application for Predictive Analytics
Usage Model: Analyze city traffic to derive statistics for crime prevention, info sharing, and predictive traffic analysis • Solution: Embed HBase* client in camera for real-time inserts of structured/unstructured
data • Benefits: Automated queries for traffic violation, data mining of fake licenses <1 minute for
all data captured for a week, predictive traffic forecasting • Characteristics:
– 30000 + camera data collection points – Petabytes of traffic data & terabytes of images – 2 billion HBase records
Platform and Cluster Architectural Attributes: • Compute:
– Scaleout Intel® Xeon® processor E5 based platform for real-time data serving – Memory: Higher Memory Capacity 4GB/Core for HBase Memstores – PCI Express* Gen3
• Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast data
access to HBase tables • Network: High network bandwidth for HBase region servers; 10GbE TORS; 10-
40GbE Inter-Rack Switch
26
E2E Analytics Use Case - Safe City
Private Cloud Public Cloud
Edge Client (Video capture)
Edge Device Video
Indexer/Analyzer/Transcoder (Image extraction & Metadata Creation)
Video Storage (Edge or Centralized)
Data Center/ Cloud
(Private/Public)
Data Services (VSaaS, VAaaS)
Checkpoint District City State Country Sm
art
Camera
Police Car
Management System
Typical Scenario : Automated traffic violations E.g., 70% Traffic violation detection by video in 2011
Edge & Backend VA’s Value
Metadata tagging and compression at the edge Enables 10s of new use models for traffic mgmt Fastest time to information and public safety
By end of 2017 457 PB
Raw Video per Day
Traffic video streamed by HD
cameras
Video created Video analyzed Video Cold storage Video metadata stored
2-3 TB Video data/Camera/Month
300 GB metadata/Camera/Month
By end of 2017 76 PB
Metadata per Day
People, cars, geospatial
information
27
Smart City Big Data Architecture Framework
Core System-on-a-Chip
Microserver
Intel® Core™
Hor
izon
tal
Sca
le
Hor
izon
tal &
V
erti
cal S
cale
Local Analytics
Preprocessing/ Cleansing/Filtering/
Aggregation Sto
rag
e
Analytics Processing Complex Event Processing
Data Acquisition
Streaming Analytics
Batch Analytics
Visualization & Interpretation
[Un]Structured
Data
Sensors Cameras
Based on Intel microarchitecture-EN
Based on Intel® microarchitecture-EX
Intel® Many Integrated Core Architecture
Based on Intel microarchitecture-EP
Data Acquisition Video Analytics
28
Agenda
• What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action
29
Up to 4 channels DDR3 1600 MHz memory
Up to 8 cores Up to 20 MB cache
Integrated PCI Express* 3.0 Up to 40 lanes per socket
Choice of Compute Platforms Optimized for Big Data
• Preferred solution for Hadoop* and scale-out analytic/DW engines
• Up to 80%** performance boost compared to prior generation
• Intel® Integrated I/O with PCI Express* 3.0 provides more bandwidth for large data sets
• Latest DDR3 memory technology/capacity for reduced memory latency
• Preferred solution for in-memory analytic engines and enterprise databases
• Highest cache and thread performance for large-dataset processing
• Up to 2TB memory footprint (4-socket platform) for in-memory apps
• Highest reliability and 8-socket+ scalability
Up to 10 cores Up to 30 MB cache
QPI 1 QPI 2 QPI 3 QPI 4
Xeon E7-4800
CORE 1 CORE 2
CORE 3 CORE 4
CORE 5 CORE 6
CORE 7 CORE 8
CORE 9 CORE 10
CACHE 4 QPI 1.0 Lanes for robust scalability
RAM
Up to 8 channels DDR3 1066 MHz memory
Intel Xeon processor E7 Family Intel® Xeon® processor E5 Family
Right Analytic Platforms begins with Intel Xeon processors QPI = Intel® QuickPath Interconnect. **See backup slides for 80% claim
30
Up to four channels DDR3 1600 MHz memory
Up to eight cores Up to 20 MB cache
Integrated PCI Express* 3.0 Up to 40 lanes per socket
Platform and Software Optimizations for Hadoop*
1 Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. 2 Source: Intel internal measurements of average time for an I/O device read to local system memory under idle conditions comparing Intel® Xeon® processor E5-2600 product family (230 ns) vs.. Intel® Xeon® processor 5500 series (340 ns). See notes in backup for configuration details * Other names and brands may be claimed as the property of others
•
• Up to 80%** performance boost vs. prior generation – Intel® Advanced Vector Extensions - reduce compute time – Intel® Turbo Boost Technology - increased performance
• Intel® Distribution for Apache Hadoop* software – Built on open source releases – Custom tuning for data types and scaling approaches
** See backup slides for 80% claim QPI = Intel® QuickPath Interconnect Intel® Xeon® processor E5
31
Intel® Xeon® processor 2S concept
Intel Xeon processor 4S concept
Low Density Servers
Delivering Performance/Power Efficiency
32
Microserver: High Density, Low Power System Innovations
• Addressing the low power, high density packaging
• Based on Intel® Atom™ processors – Next generation Intel Atom
processor codename Avoton – Workloads
Web tier, SaaS, IaaS, PaaS and light data analytics
– For scale-out apps
33
Transforming Storage Data explosion …
Source: Intel
Driving storage opportunity
Big Sensed Data
Big Corp Data
Big Web Data
Structured Data
Unstructured Data
Corporate Data
Time
Volume
690% Growth in storage capacity 2010-
2015+
Intel® Xeon® processors provide storage intelligence
• Deduplication • Thin
provisioning • Erasure code • MapReduce
• Encryption
Traditional Storage
Distributed Storage
30% CAGR
16% CAGR
690 percent growth in storage capacity based off Intel analysis and IDC data, between 2010 (26,066 petabytes) to 2015 (179,327) which is ~690%
34
Real-Time Data Analytics
Intelligent Distributed Storage Optimizations
APPLI 1
APPLI 2
TRADITIONAL ALLOCATION THIN PROVISIONING
ALLOCATED -FREE ALLOCATED - USED
ALLOCATED -USED ALLOCATED -FREE
APPLI 1 APPLI 2
SYSTEM-WIDE CAPACITY RESERVED
BEFORE AFTER
On-demand utilization of available storage – virtual and real capacity
Analysis of real-time storage determines extent and nature of compression
Strategic positioning of faster storage devices, improves storage performance
Intelligent pattern matching reduces large blocks of repeated data
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Agenda
• What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action
36
Call to Action
• Big Data represents a huge industry opportunity for innovation – get involved! – New solutions for analytics – Hardware infrastructure innovation – across the platform
• Customers from across enterprise, cloud, government, HPC and telecom are looking to improve decision making with big data – If you are a developer of solutions: Understand the market
opportunities – If you are a manager of solutions: Understand where big
data can help your organization
• Intel is deeply engaged in big data – Work with us on delivery of big data solutions
37
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• Intel product plans in this presentation do not constitute Intel plan of record product roadmaps. Please contact your Intel representative to obtain Intel's current plan of record product roadmaps.
• Intel processor numbers are not a measure of performance. Processor numbers differentiate features within each processor family, not across different processor families. Go to: http://www.intel.com/products/processor_number.
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• *Other names and brands may be claimed as the property of others. • Copyright ©2013 Intel Corporation.
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• Intel® Turbo Boost Technology requires a system with Intel Turbo Boost Technology. Intel Turbo Boost Technology and Intel Turbo Boost Technology 2.0 are only available on select Intel® processors. Consult your PC manufacturer. Performance varies depending on hardware, software, and system configuration. For more information, visit http://www.intel.com/go/turbo.
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39
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Backup
41
Disclaimer for “Up to 80% performance boost compared to prior generation” • Performance comparison using best submitted/published
2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. Baseline score of 271 published by Itautec on the Servidor Itautec MX203* and Servidor Itautec MX223* platforms based on the prior generation Intel® Xeon® processor X5690. New score of 492 submitted for publication by Dell on the PowerEdge T620 platform and Fujitsu on the PRIMERGY RX300 S7* platform based on the Intel® Xeon® processor E5-2690. For additional details, please visit www.spec.org. Intel does not control or audit the design or implementation of third party benchmark data or Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase.