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Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | DateTimeLocation Tuesday3:45pm – 4:45pmHotel Nikko - Peninsula Wednesday1:15pm –

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Presentation on theme: "Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | DateTimeLocation Tuesday3:45pm – 4:45pmHotel Nikko - Peninsula Wednesday1:15pm –"— Presentation transcript:

1 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | DateTimeLocation Tuesday3:45pm – 4:45pmHotel Nikko - Peninsula Wednesday1:15pm – 2:15pmHotel Nikko - Peninsula Thursday11:30am – 12:30pmHotel Nikko - Peninsula Big Data Hands-On Labs: Or download: Big Data Lite Virtual MachineBig Data Lite Virtual Machine

2 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Big Data Appliance for Customers and Partners Jean-Pierre Dijcks Oracle Big Data Product Management Paul Kent SAS VP Big Data 2

3 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Big Data Appliance for Customers and Partners Big Data Appliance Recap Why You Should Consider Big Data Appliance Driving Business Value with SAS on Big Data Appliance Q&A

4 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Big Data Management System SOURCES Oracle Database Oracle Industry Models Oracle Advanced Analytics Oracle Spatial & Graph Big Data Appliance Cloudera Hadoop Oracle NoSQL Database Oracle R Advanced Analytics for Hadoop Oracle R Distribution Oracle Database Oracle Advanced Security Oracle Advanced Analytics Oracle Spatial & Graph Oracle Exadata Oracle Big Data Connectors Oracle Data Integrator Oracle Big Data SQL

5 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Recap: Big Data Appliance Overview Big Data Appliance X4-2 Sun Oracle X4-2L Servers with per server: 2 * 8 Core Intel Xeon E5 Processors 64 GB Memory 48TB Disk space Integrated Software: Oracle Linux, Oracle Java VM Oracle Big Data SQL* Cloudera Distribution of Apache Hadoop – EDH Edition Cloudera Manager Oracle R Distribution Oracle NoSQL Database 5 * Oracle Big Data SQL is separately licensed

6 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Recap: Standard and Modular 6  Starter Rack is a fully cabled and configured for growth with 6 servers  In-Rack Expansion delivers 6 server modular expansion block  Full Rack delivers optimal blend of capacity and expansion options  Grow by adding rack – up to 18 racks without additional switches

7 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Recap: Harness Rapid Evolution 7 b b b BDA 4.0 BDA 4.0 – Sept 2014 Big Data SQL Node Migration BDA 3.x – April 2014 CDH 5.0 (MR2 & YARN) AAA Security Encryption BDA 2.x – April 2013 Starter Rack In-Rack Expansion EM Integration BDA 1.0 – Jan 2012 Initial BDA Mammoth Install

8 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Operational SimplicitySimplify Access to ALL Data 8 Core Design Principles for Big Data Appliance

9 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Operational SimplicitySimplify Access to ALL Data 9 Oracle Big Data SQL – Oracle SQL on ALL your data – All Native Oracle SQL Operators – Smart Scan for Optimized Performance Oracle Security – Govern all Data through a Single Set of Security Policies Core Design Principles for Big Data Appliance

10 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Big Data SQL – A New Architecture Powerful, high-performance SQL on Hadoop – Full Oracle SQL capabilities on Hadoop – SQL query processing local to Hadoop nodes Simple data integration of Hadoop and Oracle Database – Single SQL point-of-entry to access all data – Scalable joins between Hadoop and RDBMS data Optimized hardware – Balanced Configurations – No bottlenecks Oracle Confidential – Internal/Restricted/Highly Restricted10

11 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Big Data SQL 11 SELECT w.sess_id, c.name FROM web_logs w, customers c WHERE w.source_country = ‘Brazil’ AND w.cust_id = c.customer_id; Relevant SQL runs on BDA nodes 10’s of Gigabytes of Data Only columns and rows needed to answer query are returned Hadoop Cluster Big Data SQL Oracle Database CUSTOMERS WEB_LOGS

12 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Big Data SQL 12 SELECT w.sess_id, c.name FROM web_logs w, customers c WHERE w.source_country = ‘Brazil’ AND w.cust_id = c.customer_id; Relevant SQL runs on BDA nodes 10’s of Gigabytes of Data Only columns and rows needed to answer query are returned Hadoop Cluster Big Data SQL Oracle Database CUSTOMERS WEB_LOGS SQL Push Down in Big Data SQL Hadoop Scans on Unstructured Data WHERE Clause Evaluation Column Projection Bloom Filters for Better Join Performance JSON Parsing, Data Mining Model Evaluation

13 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Feedback Loop Data Management Big Data Platform (Hadoop/NoSQL) Relational Data Warehouse (OCDM) Analytic Apps Customer Experience Operations Monetization Adapters ETL/ELT Adapters Real-Time Adapters Third Party Data Sources Oracle Comms Apps (BSS/OSS) Oracle Comms Ntwk Products (Tekelec & Acme) Other Oracle Apps (CRM, ERP, etc.) Third Party Sources Oracle Communications Data Model Reference Architecture To Other Apps

14 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Operational SimplicitySimplify Access to ALL Data 14 Core Design Principles for Big Data Appliance

15 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | No Bottlenecks Full Stack Install and Upgrades Simplified Management – Cluster Growth – Critical Node Migration Always Highly Available Always Secure Very Competitive Price Point Operational SimplicitySimplify Access to ALL Data 15 Core Design Principles for Big Data Appliance

16 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Successful Big Data Systems Grow From Cluster Install with HA to Large Clusters to Dealing with Operational Issues 16 Day 1 12 node BDA for Production Hadoop HA and Security Set-up Ready to Load Data RCK_1 Full install with a single command:./mammoth –i rck_1

17 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Successful Big Data Systems Grow From Cluster Install with HA to Large Clusters to Dealing with Operational Issues 17 N N Example Service: Hadoop Name Nodes Day 1 RCK_1

18 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Successful Big Data Systems Grow From Cluster Install with HA to Large Clusters to Dealing with Operational Issues 18 N RCK_1 RCK_2 Day 90 Add 12 New Nodes across two Racks N Cluster expansion with a single command: mammoth –e newhost1,…,newhostn

19 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Successful Big Data Systems Grow From Cluster Install with HA to Large Clusters to Dealing with Operational Issues 19 N RCK_1 RCK_2 This expansion automatically optimizes HA setup across multiple racks N Cluster Expansion with a single command: mammoth –e newhost1,…,newhostn Because of uniform nodes and IB networking, no data is moved

20 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Successful Big Data Systems Grow From Cluster Install with HA to Large Clusters to Dealing with Operational Issues 20 N RCK_1 RCK_2 N Day n Critical Node Failure => Primary Name Node

21 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Successful Big Data Systems Grow From Cluster Install with HA to Large Clusters to Dealing with Operational Issues 21 N RCK_1 RCK_2 N Automatic Failover to other NameNode Automatic Service Request to Oracle for HW Failure

22 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Successful Big Data Systems Grow From Cluster Install with HA to Large Clusters to Dealing with Operational Issues 22 N RCK_1 RCK_2 N Restore HA with a Single command bdacli admin_cluster migrate N1 Reinstate the Repaired Node with a Single Command: bdacli admin_cluster reprovision N1

23 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Operational Simplicity 23 Core Design Principles for Big Data Appliance 30% 21% Quicker to Deploy Cheaper to Buy “Oracle Big Data Appliance is an excellent choice for customers looking to work with the full suite of Cloudera’s leading Hadoop-based technology. It’s more cost- effective and quicker to deploy than a DIY cluster.” ⁻Mike Olson, Cloudera founder, Chief Strategy Officer, and Chairman of the Board

24 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |  Real-time access to better data means better insights, which means better decisions and better business results  Integrate data associated with customer telemetry, configurations, service history, diagnostics, knowledge & support information Big Data Oracle Global Support Services AnticipateAnticipateDetectDetectPredictPredictAutomateAutomateDelightDelight

25 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Operational Simplicity Simplify Access to ALL Data 25 Core Design Principles Enable Success

26 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | There is one more thing… 26 Business Value = Applications

27 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Big Data Appliance powers instant Business Value 27 Customer Experience Management Cyber Security Solutions Communications Data Model

28 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Introducing 28 Paul Kent - SAS

29 Copyright © 2014, SAS Institute Inc. All rights reserved. Big Data and Big Analytics – So Much more Gunpowder! Paul Kent VP BigData, SAS Research and Development

30 Copyright © 2014, SAS Institute Inc. All rights reserved. 1. Change 2. Safari Pics

31 Copyright © 2014, SAS Institute Inc. All rights reserved. [CON8279] Oracle Big Data Appliance: Deep Dive and Roadmap for Customers and Partners Oracle Big Data Appliance is the premier Hadoop appliance in the market. This session describes the roadmap for customers in the areas of high-performance SQL on Hadoop and securing big data, plus overall performance improvements for Hadoop. A special focus in the session is the roadmap and benefits Oracle Big Data Appliance brings to Oracle partners. To illustrate the benefits of running on a standardized and optimized Hadoop platform, SAS presents the findings of its tests of SAS In-Memory Analytics on Oracle Big Data Appliance.

32 Copyright © 2014, SAS Institute Inc. All rights reserved. Agenda 1.SAS & Oracle Partnership 2.Family Stories 1.Hadoop 2.Oracle Engineered Systems Family 3.SAS Software Family 3.Deployment Patterns

33 Copyright © 2014, SAS Institute Inc. All rights reserved.  Reflection on a stronger partnership than ever  Both leaders in Big Data –  Jointly solving the most difficult and demanding Big Data Problems  Providing simplicity and agility to create flexible configurations  Extensive engineering collaboration  Can we answer:  How Does it Work?  How Does it Perform? 2014

34 Copyright © 2012, SAS Institute Inc. All rights reserved. THE TAMOXIFEN DILEMMA SOURCE:

35 Copyright © 2014, SAS Institute Inc. All rights reserved. Agenda 1.SAS & Oracle Partnership 2.Family Stories 1.Hadoop 2.Oracle Engineered Systems Family 3.SAS Software Family 3.Deployment Patterns

36 Copyright © 2014, SAS Institute Inc. All rights reserved.

37 Elephant :: 3 Good Ideas !! 1.Never forgets 2.Is a good (hard) worker 3.Is a Social Animal (teamwork)

38 Copyright © 2014, SAS Institute Inc. All rights reserved.  MPP (Massively Parallel) hardware running database-like software  “data” is stored in parts, across multiple worker nodes  “work” operates in parallel,on the different parts of the table ControllerWorker Nodes Hadoop – Simplified View

39 Copyright © 2014, SAS Institute Inc. All rights reserved. Head NodeData 1Data 2Data 3Data 4… MYFILE.TXT..block1 ->block1..block2 ->block2..block3 ->block3 Idea #1 - HDFS. Never forgets!

40 Copyright © 2014, SAS Institute Inc. All rights reserved. Head NodeData 1Data 2Data 3Data 4… MYFILE.TXT..block1 ->block1block1 copy2..block2 ->block2block2 copy2..block3 ->block3 copy2block3 Idea #1 - HDFS. Never forgets!

41 Copyright © 2014, SAS Institute Inc. All rights reserved. Head NodeData 1Data 2Data 3Data 4… MYFILE.TXT..block1 ->block1block1copy2..block2 ->block2block2 copy2..block3 ->block3 copy2block3 Idea #1 - HDFS. Never forgets!

42 Copyright © 2014, SAS Institute Inc. All rights reserved. Redundancy Wins!

43 Copyright © 2014, SAS Institute Inc. All rights reserved. Idea #2 – MapReduce – Send the work to the Data  We Want the Youngest Person in the Room  Each Row in the audience is a data node  I’ll be the coordinator From outside to center, accumulate MIN Sweep from back to front. Youngest Advances

44 Copyright © 2014, SAS Institute Inc. All rights reserved. Agenda 1.SAS & Oracle Partnership 2.Family Stories 1.Hadoop 2.Oracle Engineered Systems Family 3.SAS Software Family 3.Deployment Patterns

45 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Recap: Standard and Modular 45  Starter Rack is a fully cabled and configured for growth with 6 servers  In-Rack Expansion delivers 6 server modular expansion block  Full Rack delivers optimal blend of capacity and expansion options  Grow by adding rack – up to 18 racks without additional switches

46 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Big Data SQL – A New Architecture Powerful, high-performance SQL on Hadoop – Full Oracle SQL capabilities on Hadoop – SQL query processing local to Hadoop nodes Simple data integration of Hadoop and Oracle Database – Single SQL point-of-entry to access all data – Scalable joins between Hadoop and RDBMS data Optimized hardware – Balanced Configurations – No bottlenecks Oracle Confidential – Internal/Restricted/Highly Restricted46

47 Copyright © 2014, SAS Institute Inc. All rights reserved. Diversity. It’s a good thing! Impala Nyala

48 Copyright © 2014, SAS Institute Inc. All rights reserved. Agenda 1.SAS & Oracle Partnership 2.Family Stories 1.Hadoop 2.Oracle Engineered Systems Family 3.SAS Software Family 3.Deployment Patterns

49 Copyright © 2014, SAS Institute Inc. All rights reserved. 4 Important Things #1 Join the Family

50 Copyright © 2014, SAS Institute Inc. All rights reserved. HADOOP Hive QL SAS SERVER SAS ACCESS to Hadoop #2 Be Familiar

51 Copyright © 2014, SAS Institute Inc. All rights reserved. SAS / Embedded Process SAS/Scoring Accelerator for Hadoop SAS/Code Accelerator for Hadoop SAS/Data Quality Accelerator for Hadoop proc ds2 ; /* thread ~ eqiv to a mapper */ thread map_program; method run(); set dbmslib.intab; /* program statements */ end; endthread; run; /* program wrapper */ data hdf.data_reduced; dcl thread map_program map_pgm; method run(); set from map_pgm threads=N; /* reduce steps */ end; enddata; run; quit;

52 Copyright © 2014, SAS Institute Inc. All rights reserved. SAS / High Performance Analytics #3 Use the Cluster!

53 Copyright © 2014, SAS Institute Inc. All rights reserved. PrepareExplore / TransformModel HPDS2 HPDMDB HPSAMPLE HPSUMMARY HPCORR HPREDUCE HPIMPUTE HPBIN HPLOGISTIC HPREG HPNEURAL HPNLIN HPCOUNTREG HPMIXED HPSEVERITY HPFOREST HPSVM HPDECIDE HPQLIM SAS / High Performance Analytics HPLSO HPSPLIT HPTMINE HPTMSCORE

54 Copyright © 2014, SAS Institute Inc. All rights reserved. Controller Client SAS / High Performance Analytics

55 Copyright © 2014, SAS Institute Inc. All rights reserved.

56 #1 Join the Family #2 Be Familiar #3 Use the cluster #4 Have a pretty face!

57 Copyright © 2014, SAS Institute Inc. All rights reserved.  Interactive exploration, dashboards and reporting  Auto-charting automatically picks the best graph  Forecasting, scenario analysis, Decision Trees and other analytic visualizations  Text analysis and content categorization  Feature-rich mobile apps for iPad® and Android SAS Visual Analytics

58 Copyright © 2014, SAS Institute Inc. All rights reserved.

59 SAS Visual Statistics July-2014  Interactive, visual application for statistical modeling and classification  Multiple methods: logistic, Regression, GLM, Trees, Forest, Clustering and more…  Model comparison and assessment  Group BY Processing

60 Copyright © 2014, SAS Institute Inc. All rights reserved.

61 4 Important Things (for cluster friendly software) 1.Join the Family 2.Be Familiar 3.Performance 4.Have a pretty face

62 Copyright © 2014, SAS Institute Inc. All rights reserved. Agenda 1.SAS & Oracle Partnership 2.Family Stories 1.Hadoop 2.Oracle Engineered Systems Family 3.SAS Software Family 3.Deployment Patterns

63 63 Copyright © 2013, SAS Institute Inc. All rights reserved. SAS BIG DATA ON BIG DATA APPLIANCE Flexible Architectural options for SAS deployments Can run on Starter, Half and Full configurations Optionally select nodes “N, N-1, N-2, …” for additional SAS Services such as SAS Compute Tier, SAS MidTier Optionally select node subset “N, N-1, N-2, N-3, …) for more dedicated resources for SAS Analytic Compute Environment by shifting Big Data Appliance roles Option to selectively add more memory on a per node basis depending on specific workload distribution

64 64 Copyright © 2013, SAS Institute Inc. All rights reserved. SAS Midtier STARTER BDA … … SAS Visual Analytics Metadata Server SAS Compute SAS HPA Root Node SAS VISUAL ANALYTICS, HIGH-PERFORMANCE ANALYTIC COMPUTE ENVIRONMENT CO-LOCATED WITH HADOOP

65 65 Copyright © 2013, SAS Institute Inc. All rights reserved. SAS Midtier STARTER BDA … … SAS Visual Analytics Metadata Server SAS Compute SAS HPA Root Node SAS VISUAL ANALYTICS, HIGH-PERFORMANCE ANALYTIC COMPUTE ENVIRONMENT CO-LOCATED WITH HADOOP Consider: Extra Memory for 5,6?

66 66 Copyright © 2013, SAS Institute Inc. All rights reserved. SAS Midtier FULL RACK BDA … LASR Worker 17 HDFS Data 17 … … Metadata Server SAS Compute SAS HPA Root Node LASR Worker 18 HDFS Data 18 SAS VISUAL ANALYTICS, HIGH-PERFORMANCE ANALYTIC COMPUTE ENVIRONMENT CO-LOCATED WITH HADOOP

67 67 Copyright © 2013, SAS Institute Inc. All rights reserved. FULL RACK BDA ASSEMBLED IN OSC, SYDNEY AUSTRALIA

68 68 Copyright © 2013, SAS Institute Inc. All rights reserved. FULL RACK BDA ASSEMBLED IN OSC, SYDNEY AUSTRALIA

69 69 Copyright © 2013, SAS Institute Inc. All rights reserved. FULL RACK BDA ASSEMBLED IN OSC, SYDNEY AUSTRALIA

70 70 Copyright © 2013, SAS Institute Inc. All rights reserved. FULL RACK BDA ASSEMBLED IN OSC, SYDNEY AUSTRALIA Basic Smoke Tests Confirmed: Interoperate with Hadoop and Map Reduce Read and Write text files to/from HDFS Read and Write Tabular files to/from Hive (will confirm Oracle BIGSQL in OSC-SC) Read and Write SAS binary format files to/from HDFS High Degree Of Parallelism (DOP) reads via Map-Only jobs SAS LASR server co-exists on/with datanodes SAS HPA tasks scheduled on datanodes

71 71 Copyright © 2013, SAS Institute Inc. All rights reserved. Table 1: Summation of 5/20/100/200 columns; Baseline: DOP=1 (no parallelism) 120M rows, 400 columns, reg_simtbl_400 SAS High-Performance Analytics Performance SAS Format Data (SASHDAT) 1107 var Mobs 97GB 5.7GB/node 1107 var Mobs 608GB 35.7GB/node 6x Create sec sec11 Scan/Count24.60 sec sec10.5 HPCORR HPCNTREG HPREDUCE (u) HPREDUCE (s)

72 72 Copyright © 2013, SAS Institute Inc. All rights reserved. OSC-AU FullRack BDA 408 Threads 600 GB dataset 17 servers Your Problem solved ASAP

73 73 Copyright © 2013, SAS Institute Inc. All rights reserved.

74 74 Copyright © 2013, SAS Institute Inc. All rights reserved.

75 75 Copyright © 2013, SAS Institute Inc. All rights reserved.

76 76 Copyright © 2013, SAS Institute Inc. All rights reserved. EXADATA INTEGRATION SAS EMBEDDED PROCESSING (EP) TO EXADATA LEVERAGING BIG DATA SQL … SAS Midtier LASR Worker 18 … HDFS Data 18 SAS Visual Analytics Metadata Server SAS Compute SAS HPA Root Node SAS EP Big Data SQL

77 77 Copyright © 2013, SAS Institute Inc. All rights reserved. Table 1: Summation of 5/20/100/200 columns; Baseline: DOP=1 (no parallelism) 120M rows, 400 columns, reg_simtbl_400 SAS High-Performance Analytics Performance SAS EP Parallel Data Feeders DOP=1DOP=24 (flash cache) Add(5)1.25min1.5min.5min Add(20)2.5min1.5min.5min Add(100)13min1.5min.6min Add(200)16min~2min1.25min (10x)

78 78 Copyright © 2013, SAS Institute Inc. All rights reserved. Table 2: Scan times for 2 tables (200 columns, 400 columns, 120M rows); Baseline: SAS/ACCESS vs. HPA EP feeder SAS High-Performance Analytics Performance SAS EP Parallel Data Feeders AccessAccess / DBSlice SAS HPA Using EP Reg_sim_2001:01:120:28:370:08:00 Reg_sim_4001:49:110:55:330:16:05 (7x!)

79 79 Copyright © 2013, SAS Institute Inc. All rights reserved. Table 1: Summation of 5/20/100/200 columns; Baseline: DOP=1 (no parallelism) 120M rows, 400 columns, reg_simtbl_400 SAS High-Performance Analytics Performance SAS Format Data (SASHDAT) and Oracle EXADATA 1107 var Mobs 97GB 5.7GB/node SASHDAT 907 var Mobs 79.7GB 4.7GB/node EXADATA 1107 var Mobs 608GB 35.7GB/node SASHDAT Create sec sec sec Scan/Count24.60 sec sec sec HPCORR HPCNTREG HPREDUCE (u) HPREDUCE (s)

80 80 Copyright © 2013, SAS Institute Inc. All rights reserved. ORACLE ENGINEERED SYSTEMS FOR SuperClusterExaDataExaLogic Virtual Compute Appliance Big Data Appliance Database Backup, Recovery, Logging Appliance ZFS Storage Appliance

81 Copyright © 2012, SAS Institute Inc. All rights reserved. SAS AND ORACLE WORKING TOGETHER TO CREATE CUSTOMER VALUE Joint R & D development and Product Management teams in Cary and Redwood Shores Focus on driving SAS technology components to run natively in Oracle database Joint performance engineering optimizations Template physical architectures developed based on use-cases Physically tested and benchmarked together Reduction in physical effort Overall reduction in lifecycle costs Best Practice papers SAS and Oracle Engineers provide joint "Sizing and Architecture Analysis and Design"

82 Copyright © 2013, SAS Institute Inc. All rights reserved. SAS AND ORACLE BETTER TOGETHER paulmkent

83 Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |Oracle Confidential – Internal/Restricted/Highly Restricted83

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