Evolution of Big Data at Intel - Crawl, Walk and Run Approach
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Evolution of Big Data at Intel - Crawl, Walk and Run Approach
- 1. Evolution of Big Data at Intel - crawl, walk and run approach Gomathy Bala | Director Chandhu Yalla | Manager & Architect Key Contributors: Sonja Sandeen, Seshu Edala, Nghia Ngo and Darin Watson IT BI Big Data Team
- 2. Copyright © 2014, Intel Corporation. All rights reserved. Legal Notices This presentation is for informational purposes only. INTEL MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY. The content in this presentation is being shared Under NDA. Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries. * Other names and brands may be claimed as the property of others. Copyright © 2014, Intel Corporation. All rights reserved. 2
- 3. Copyright © 2014, Intel Corporation. All rights reserved. Agenda • Intel IT Big Data Journey • Enterprise DW architecture • BI Big Data 3 yr Roadmap • Big Data Ecosystem Architecture • Platform Strategies & BKMs • Summary 3
- 4. Copyright © 2014, Intel Corporation. All rights reserved. 2011 2012 2013 2014 2015 Intel IT Big Data Journey 4 Big Data & Analytics Strategy Production Online Telmap: 1st Use Case Preproduction Online Hadoop Evaluation IDH to CDH Hadoop 2.0 $176M BV Production: Security BI, Attribute Reduction System, ATM Ellipses Engine, IAH- Retail Analytics 6 Environments CDH 5.3 4 Use Cases in Preproduction 12 POC Use Cases 6 Use Cases in Production $290K investment $948/TB 3 Use Cases in Production Smart-What, Marketing- IAH, Incident Predictability $6M BV CDH 5.1 IAH – Cloud CRM In Production Enterprise Standards, Guidance, Processes for Platform & Capabilities 15 Active Use Cases | $290K + 10.5 HC Investment | Delivered $182M BV
- 5. Copyright © 2014, Intel Corporation. All rights reserved. Big Data & Analytics Really Delivers! 5From 2014 – 2015 Intel IT Business Review – Annual Edition Kim's Video
- 6. Copyright © 2014, Intel Corporation. All rights reserved. Any Data Source ERP In Memory Real-Time Data Platform CRM SCM SRM ECC BW ECCW Real-Time & Self Service Analytics Platform MDG NW Teradata Cloudera Hadoop Data Lake Reporting Tools Data Tiering Hot-Cold data Enterprise Data Warehouse Other Apps Custom Intel … NR T Predictive Analytics BPC BCS Cloud BI Saa S New Apps. Downstream Applications 2014-2017 Vision: Real-Time Enterprise 6
- 7. Copyright © 2014, Intel Corporation. All rights reserved. FE Tools CLS/Proxy High speed data loader BigData • Machine Learning • Log Processing • Unstructured data Use Cases • High volume counter Analytics • Text Parsing/Mining • Strategic/Operational reporting • Interactive Reporting Use Cases • High Concurrent user analytics - Supply/Order • Mission critical analytics – Finance/HR SQL on Hadoop Enterprise Data Architecture with Hadoop and Other MPP DWH Current & Future Strategy Future Present EDWMfg Data A %ge of Traditiona l BI use cases IMT
- 8. Copyright © 2014, Intel Corporation. All rights reserved. BI Big Data | 3-Year Roadmap 8 Big Data + AA Big Data + SSAA + Traditional BI Big Data + SSAA + Traditional BI 2015 2016 2017 Scalable and well designed Hadoop Platform Evolve IMT + Hadoop Data Lineage & Data Catalog Streaming Capabilities Advanced SQL on Hadoop ACID semantics Evolve Big Data + SSAA per ecosystem roadmaps BC/DR End to end enterprise features Enterprise ready: OLAP and Traditional DW Hadoop is an open source framework designed for big data analytics. Hadoop is evolving rapidly, but it will still take a couple of years for it to mature and support “traditional bi” use cases. Legend Orange Text: Traditional BI Capabilities Green Text: Big Data/AA Capabilities Security (RBAC, ITS/IRS) Data Governance Data Discovery Self Service AA Framework IMT + Hadoop AVP + Hadoop In-memory + Near real time capabilities SQL on Hadoop
- 9. Copyright © 2014, Intel Corporation. All rights reserved. Data Integration Big Data Platform – Ecosystem Architecture & Maturity 9 NRT/Stream Processing In-Memory Processing Processing Layer Batch Processing Data Virtualization Data DiscoveryAdv. AnalyticsAdv. Visualization Data Management Presentation Layer End User Data Steward Business Analyst Data Scientist DeveloperUser layer Auditor Machine Learning Analytical layer Statistical Numerical Time series Textual/Log Spatial Graph Textual/Log DB Hierarchy DBRelational DB Graph DB Storage Model Platform Virtualization Infrastructure Platform Management Network Management Systems Management Data Ingestion Continuous IntegrationDev Framework Security Source/Target APIs 3rd Party Drivers Ent. Scheduler Srvs Metadata MgmtWorkload Mgmt Middleware *Other names and brands may be claimed as the property of others. Columnar DB Data Egression Other Vendors offered capabilities Majority CDH offered capabilities Data Consumption Prescriptive Guidance Change Release GovernanceEngagement Service Management Training Support Processes
- 10. Copyright © 2014, Intel Corporation. All rights reserved. BI Big Data Platform 10 Hadoop Project Sandbox – CDH 5.3 Multiple Instances Deployed on Intel Cloud & MyCloud environments. TTM to business: 2-3 Days Hadoop Pre-Production – CDH 5.3 10 data nodes | 399TB | 320 vcores Use cases in Dev/POC: 14 Hadoop Production – CDH 5.3 22 data nodes | 658TB | 704 vcores Use cases Live in prod: 7 Hadoop 2.0 architecture provides reliability, scalability & performance High availability and scalability design Well positioned to meet 2015 business use case requirements Repeatable architecture for faster builds. Capacity additions: Add data node. White boxes, Waterfall equipment or HP servers TTM: Varies depending on HW (3 wks-2 months) Job/Workflow Management Data Node Data Node Data Node Data Node Data Node Name Node Resource Mgr Name Node Resource Mgr heartbeat, balancing, replication YARN Scale to meet business needs Gateway Nodes (NN hi-av) Gateway nodes Login (ssh) : AD authentication & authorization, access cluster, run HDFS commands, submit jobs, etc. Management Node Source Data DB Data Visualization Tools Data Movement/ETL EDW or Datamart DB data Unstructured Semi-structured
- 11. Copyright © 2014, Intel Corporation. All rights reserved. • Skills and resources with time to ramp up • Starting small is ok. Focus on design and scalability for the platform. • Technical product evaluation Stick with a distribution which is core Hadoop open source stack vs proprietary software • Security is a big deal to Intel, Big Data Security capabilities implementation is key focus • Methodology to understand the data is to use an iterative discovery method with technical, business and modeling teams. • Intel IT Big Data Journey benefited heavily from Cloudera partnership • Open source will play a big role in advancing Big Data capabilities and analytics BKM’s | Summary
- 12. Copyright © 2014, Intel Corporation. All rights reserved. BI Big Data IT@Intel Resource Info 12 BI Big Data IT@Intel Resource Links: 1. Hadoop Migration Success Story: How Intel IT Moved to Cloudera 2. Mining Big Data in the Enterprise for Better Business Intelligence 3. Enabling Big Data Platforms and Solutions with Centralized Data Management 4. Integrating Apache Hadoop* into Intel’s Big Data Environment 5. Using a Multiple Data Warehouse Strategy to Improve BI Analytics To learn more: www.intel.com/bigdata
- 13. Copyright © 2014, Intel Corporation. All rights reserved. Q & A 13
- 14. Intel Confidential — Do Not Forward
- 15. Copyright © 2014, Intel Corporation. All rights reserved. Backup 15
- 16. Copyright © 2014, Intel Corporation. All rights reserved. Big Data Capability Catalog Hive HDFS MapReduceZookeeper Pig Mahout NetworkServers Storage Security OS Hi-AvEAM / AD Integration HDFS Compress WHIRR Hbase Governance Change Release Engagement Service mgmt. Prescriptive Guidance Training SQOOP JDBC Other DW Infrastructure Process Cloudera* Distribution of Hadoop (CDH) *Other names and brands may be claimed as the property of others. Storm Hcatalog ACCUMULOYARN SPARK Autosys SecureGIT Impala JDBC HiveODBC 3rd Party SW/Connectors Integration HUE SOLRIMPALA PARQUET DataFu Impala ODBC TDCH Oozie Kafka Sqoop DI Gateway Flume SFTP SMBClient Data Integration Camel Enabled PlannedWIP Avail. Now 1-3 Months 3-6+ Months Cloudera Manager* System Management Cloudera Navigator* Data Management Audit Access Control Discovery Explore Lineage Lifecyle DeploymentMonitoring Reporting Diagnostics Alerting Service Management Rolling Upgrades Config Rollbacks List includes only the capabilities planned for next 6 months. 16 Google Analytics SFDC Sentry
- 17. Copyright © 2014, Intel Corporation. All rights reserved. i. Find Differences with a Comparative Evaluation in a Sandbox Environment ii. Define Your Strategy for the Cloudera Implementation iii. Split the Hardware Environment iv. Upgrade the Hadoop Version v. Create a Preproduction-to- Production Pipeline vi. Rebalance the Data Migration to Cloudera – 6 BKMs
- 18. Copyright © 2014, Intel Corporation. All rights reserved. Building Block Strategy to Enterprise Security of Hadoop Q1’15: Perimeter access with LDAP + finer grain controls with Sentry. The second building block towards enterprise grade security design. Q2’15: Add Kerberos to enable more Hadoop components and further secure the platform 2H’15: Exploration starting, awaiting product and target to adopt in 2H’15 in Production. NowQ2’15 2H’15
- 19. Copyright © 2014, Intel Corporation. All rights reserved. Hadoop Maturity & Evolution 19 MapReduce (batch data processing, cluster resource management) HDFS 1.0 (redundant, reliable data storage) Hadoop 1.0 YARN (cluster resource management) HDFS 2.0 (redundant, reliable data storage) Interactive (Impala) In-Memory (Spark) Batch (Map Reduce) Online (Hbase) Others (Search, Storm etc.) Graph Applications Run Natively In Hadoop + Scalable data storage and processing platform + Positioned for Batch processing workloads for Map and Reduce only + Apache Hive offers SQL like query language - Lacks reliability and stability - No support for low latency queries Apache YARN allows you to run multiple applications in Hadoop and provides reliability, scalability and performance Advanced Resource Management Apache Hive offers a 50x improvement in performance for queries Cloudera Impala to support low latency query requirements with SQL-92 and SQL- 2000 support Data at Rest Encryption and Row Level/Cell Level Security planned Data Streaming and Search Capability GraphDB Expanded Data Governance IMT + Hadoop Integration Improved Front End tool integration/support Deeper Diagnostics for multiple components 2005 - 2012 2013 - 2014 Hadoop 2.0 HDFS (redundant, reliable data storage) YARN (cluster resource management) Batch (Map Reduce) Others (data processing) 2015 - 2017
- 20. Copyright © 2014, Intel Corporation. All rights reserved. 2014 Intel IT Vital Statistics 20 >6,300 IT employees 59 global IT sites >98,000 Intel employees1 168 Intel sites in 65 Countries 64 Data Centers (91 Data Centers in 2010) 80% of servers virtualized (42% virtualized in 2010, goal of 75%) >147,000+ Devices 100% of laptops encrypted 100% of laptops with SSD’s >43,200 handheld devices 57 mobile applications developed Source: Information provided by Intel IT as of Jan 2014 1Total employee count does not include wholly owned subsidiaries that Intel IT does not directly support Copyright © 2014, Intel Corporation. All rights reserved.
- 21. Copyright © 2014, Intel Corporation. All rights reserved. Big Data in the Industry 21 Recommendation Engine Fraud Detection Sentiment Analytics Behavioral Targeting Customer Experience AnalyticsMarketing campaign Analytics
- 22. Copyright © 2014, Intel Corporation. All rights reserved. Learn more about Intel IT’s Initiatives at www.intel.com/IT Sharing Intel IT Best Practices With the World
Notas do Editor
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- Stream Processing or Complex Event Processing -- where small chunks of data come at rapid intervals [smaller quantum, requiring transformation]. E.g., Sensory data from manufacturing floors. Batch Processing -- aggregated chunks of data, perhaps collected over a long span, waiting to be analyzed in one run. OLAP processing. E.g. Gold path analysis on intel.com In-memory processing -- running interactive analytics over large batches of summary/factual data by leveraging the memory as the pre-emptive transient store. E.g. SQL aggregates/operational metrics from OLAP process Machine Learning -- class of unsupervised and supervised learning techniques destined for a decision support or an expert system Unsupervised Learning (No "response" variable. Just observations) -- tools Mahout Clustering -- E.g. customer segmentation; clustering users by age, ethnicity, gender, income standards, geo, profession, and buying propensity to new form factors. Frequent pattern mining -- E.g. co-branding strategies. People buying realsense cameras also downloading Intel XDK kits within 7 days of purchase. Supervised Learning [predicting a "response" variable when encountering a new "condition". The response patterns learned from prior training sets of course…] -- H2O Regression -- E.g. YoY growth for DCG Xeon co-processor shipment at 16% between 2011 and 2014. This year, we will ship 36 million units; current inventory levels at 23 mill Classification -- E.g. Customer (Widgets Inc) responses to email automation and phone calls favorable in the last 3 months. Last upgrade was 2 years ago. The likelihood of an enterprise upgrade is "high". Textual -- class of algorithms that "derive" meaning from what is otherwise flat left-to-right-top-to-bottom "text". Shred sentence structure into nouns-verbs-adjectives-adverbs; count entities and turn "text" into "terms" [features]. Encode the feature into a term-document or a "graph" representation so traditional analytics -- machine learning (supervised and unsupervised techniques may be applied). Lucene, SOLR is useful for indexing/tokenizing text; NLTK or Stanford parsers are useful to "tag" terms to class of linguistic tokens such as nouns and verbs. E.g. identify service management tickets that entail Windows 8.1 issues. Log -- Logs are textual in syntax but do not possess linguistic rigor. Such contents are useful just indexing as is and searching. The machines do not "decode" meaning. Humans synthesize and add logical rules when the content is surfaced back via a search interface. E.g Logstash used to monitor errors in log4j logs of Hive jobs. Spatial -- Class of problems that deal with spatial layout of entities. E.g. every die is sacred. Rationing and allocating sub-systems on a die via simulatory techniques to optimize wastage loss and maximize "premium" quality. Or optimizing lithographic etches that minimize orthogonal cuts by employing space-filling heuristics. Statistical -- class of problems that infer patterns from data that exhibits stochastic characteristics -- e.g. identifying aggreations like stddev, min, max, avg yields of a graphics die; and performing outlier analysis. Numerical -- class of problems that deal with data that exhibits deterministic characteristics -- e.g. Taguchi methods or iterative monte carlo methods that search and seek global minima/maxima. Genetic algorithms, deep learning methods/neural networks etc. Time-series -- class of problems that deal with data that exhibits stochasticity, but also exhibits temporal/seasonal resonance patterns. E.g. noise-cancellation filters that employ feedback loops; or predicting stock-price movement etc Graph -- class of problems that compute statistics about entities connected to other entities. E.g. computing pagerank/link-popularity of a web page, congestion patterns of a traffic flow, sewage system planning etc Storage Models Textual/Binary -- No DDL. All data is stored row-first, column-next where there is only one BLOB column per row. E.g Zip files, MainFrames Relational -- well specified DDL, but data is stored row-first [co-located fields of a row]; locking semantics at row level. Yields faster entity retrievals but poorer compression ratios when heterogeous fields co-exist in data. The index is built for row-offsets; e.g. -- Oracle, MySQL Columnar -- well specified DDL; but data is stored column-first [all first names are co-stored in ine file, last-names co-stored in another etc]; locking semantics at cell level. Yields faster aggregates [min, max on a single field], better compression ratios [because all fields of a columnar file are a homogenous type]. But lacks atomic consistency because a record change transpires into mutations in multiple "columnar/co-location" files. E.g. HBase, Cassandra Hierarchy -- well specified structural definition. Mostly follows a denormalized parent-child taxonomy. All fields relevant to a record are stored as a "hierarchic document" ala XML or JSON document. Yields a great consistency model because the grain of the data is a "document". Any mutation will always mean a complete denormalized update of the full document -- json or xml. E.g. MongoDB, CouchDB GraphDB -- native adjacency property graph that stores entities as "vertices" of a graph, relations as "edges", and attributes as "properties". Since indices are combinatorially developed on all -- entities, relations, and attributes -- adjacency mining, filtering, mutations are performant and atomic. E.g. Neo4J, TitanDB
- SLIDE PURPOSE: Who Are We … we are the IT organization at Intel (IT@Intel) .. Core background information on Intel IT and our mission/goals/capabilities Key Messages: We are the IT organization Inside Intel’s Business. Our organization is large, diverse multi-national enterprise with a wide variety of operational requirements and needs Our Vision is to accelerate Intel’s quest to connect and enrich the lives of every person on Earth by the end of the decade. Our Mission is to Grow Intel’s Business through Information Technology for Intel by facilitating IT Consumerization, delivering IT efficiency and continuity through Cloud Computing, increase employee productivity through seamless connectivity and Security, provide significant business value through Business Intelligence initiatives and drive increased collaboration through Social Computing. Review some of the Information/Key Stats shown here. Size and Location: 6,334 IT employees … Supporting over 98,000 employees. Note: Intel IT only reflects the number of employees we support directly (we exclude Intel employees who support wholly owned subsidiaries) Remote Support is Vital. Data Centers and Facilities: 59 Data Centers worldwide (down from 142 in 2007) Need to confirm this data[~55,000 servers (down from 100,000 in 2007) consuming a large electrical and power/cooling load (roughly 55MW total power) Our Data Centers also support 300M email messages (per month), >2,183 Terabytes WAN traffic (per month)] and store 45 petabytes of raw storage capacity Employee / Client Technology: Support over 147K devices (note >1 per employee ratio .. This ratio is growing with support of BYO and custom technology delivery to meet business needs) >We have been 80%+ mobile PCs (laptops) as our core employee technology standard since 1997 We have been actively evaluating, enabling and supporting many companion devices for improved productivity and flexibility Need to add what we are doing with tablets - Janet >43,200 Handhelds (variety of form factors (phones/tablets) vendors, software and solutions) the majority of these devices are now EMPLOYEE OWNED Intel IT continues to embrace consumerization of IT and mobile applications are a major component of our strategy. We have delivered 57 mobile apps and counting to support new form factors. Our goal is to deliver a seamless, secure experience for our employees across a wide spectrum of devices by putting user experience first. Enabled Leadership Business Capabilities: Enable a top 25 supply chain (recognized by Gartner, previously AMR Research) . #25 in 2009, #18 in 2010, #16 in 2011, #7 in 2012 and #5 in 2013 key focus for IT innovation … delivered solid business results and competitive differentiation for Intel Additional fun facts … 100% Intel laptops support SSD and 100% are deployed with disk encryption