WHITE PAPER A BLUEPRINT FOR PURE ANALYTICS WITH ORACLE AND ORACLE R

Transcription

1 WHITE PAPER A BLUEPRINT FOR PURE ANALYTICS WITH ORACLE AND ORACLE R

2 TABLE OF CONTENTS EXECUTIVE SUMMARY... 3 INTRODUCTION... 3 GOALS AND OBJECTIVES... 5 AUDIENCE... 5 ANALYTICAL APPLICATIONS WORKLOADS... 6 Analytical Applications... 6 ORACLE SOFTWARE LAYER ARCHITECTURE... 7 Application Layer... 7 Software Layer... 8 Data Management Layer... 8 Oracle dnfs... 9 PURE DATA CENTRIC ARCHITECTURE... 9 Pure Storage FlashStack Pure Storage FlashBlade Purity for FlashBlade (Purity//FB 2) Pure1 Intelligent, Self-Driving Storage Evergreen Storage PERFOMANCE AND SCALABILITY Server Configuration FlashBlade Configuration Oracle dnfs Configuration Workload Configuration Test Descriptions ORACLE R ENTERPRISE EXAMPLE Understanding the Data Oracle R and R Studio CONCLUSION REFERENCES

3 EXECUTIVE SUMMARY Data assets represent the aggregate experience of an enterprise the factual and historical record of interactions with customers and the ingredients from which organizations learn and make informed decisions. Predictive analytics assists that learning, enabling companies to gain and maintain a competitive edge. As the raw input to predictive analytics, data is also a strategic asset, and thus the speed of that data determines, to a large degree, the efficiency of the enterprise. Data and insight drive the creation, delivery, and life cycle of digital products and services. From an organizational standpoint, these projects connect the Chief Information Officer (CIO) and Information Technology (IT) groups to farflung parts of the company where they must cultivate new relationships. Despite these trends, a recent Gartner survey shows that corporations are still slow to make progress in data management and analytics: 91 percent of organizations have not yet reached a transformational level of maturity in data and analytics, despite this area being a number one investment priority for CIOs in recent years. 1 Architecting the proper data management infrastructure is the first step to reaching a transformational level of maturity in analytics. Pure Data Centric Architecture is purpose-built to help enterprises realize their goals of digital transformation, and the architectural blueprint presented in this paper demonstrates how Pure s all-flash technologies can drive data and analytics. We will also examine a use case for how this applies to running Oracle R for analytics. INTRODUCTION Organizations typically evolve through four stages of data processing needs. First Development of databases devoted to online operations of hot data generated by transactional, web, mobile, and internet of things (IOT) applications. Second The analysis of historical data contained in modern business intelligence applications like data warehouses. Third Moving to big data analytics to support the processing of vast amounts of cold semi-structured and unstructured data. Fourth Creating real time and batch data pipelines for unified data movement. These four stages are part of a data journey designed to increase data value. This paper will focus primarily on the third stage in this journey around building efficient and cost-effective analytics engines. 1 Gartner Survey Shows Organizations Are Slow to Advance in Data and Analytics, February 5,

4 According to 451 Research, when asked what challenges organizations contend with in achieving results with predictive analytics projects, respondents most frequently cited infrastructure requirements, scaling challenges, cost and performance issues. 2 Predictive analytics enables organizations to examine large and varied data sets i.e., big data to uncover hidden patterns, unknown correlations, trends, and other useful information that can help them make more-informed business decisions. It has also been used to create many solutions around big data such as next best offer, fraud detection, supply chain automation, predictive maintenance, and payment optimization, among others. The top three strengths of predictive analytics are time to value and a boost to the top and bottom line (Figure 1), according to respondents to the 451 Research survey. FIGURE 1. What do you consider the greatest strength of your log/machine data analytics technology? N=188 Source: 451 Research Without the right IT infrastructure in place, however, predictive analytics initiatives can fail to deliver the benefits anticipated by enterprise decision-makers. Figure 2 shows infrastructure challenges that organizations face when using data for predictive analytics. FIGURE 2. What challenges have you faced with your current use of log/machine data analytics technology? N=188 Source 451 Research 2 The Need for Speed: Machine Data Analytics in , November

5 For CIOs and other senior IT leaders whose enterprises still rely on legacy systems, these challenges simply reflect reality older IT equipment and processes weren t built to handle the exponential volume of data generated in recent years, as detailed in the IDC FutureScape: Worldwide Analytics and Information Management 2018 Predictions 3 report: By 2020, the heavy workload demands of next-generation applications and new IT architectures in critical business facilities will have forced 55% of enterprises to modernize their datacenter assets. The following sections present a technical blueprint 4 for a predictive analytics solution approach with the Pure Storage solutions and Oracle software components. GOALS AND OBJECTIVES Pure Data Centric Architecture provides all the storage services block, file, and object needed to consolidate everything, whether databases, virtual machines, analytics, or web scale applications. It delivers enterprise data services for all workloads, and drives productivity with industry-leading data reduction, proven FlashArray % availability, non-disruptive everything, built-in data protection, global flash management, endless extensibility, and effortless simplicity. Pure solutions include FlashArray for block and virtual volumes, FlashBlade for file and object, and FlashStack for converged infrastructure solutions. Whether accelerating a single database, powering Virtual Machines (VM) and desktops, or establishing an all-flash data pipeline, Pure Data Centric Architecture empowers organizations to put data at the center of everything they do. The objective for this blueprint is to demonstrate the software components from Oracle installed on Pure Data Centric Architecture to create an environment for analytical workloads without the complexities of open source software configurations and commodity infrastructure. Pure Storage array generic performance metrics are also presented. Predictive analytics workloads are completely dependent on the algorithms and the data used for modeling. A performance characterization of an analytical workload is out of scope in this document. However, we intend to present generic throughput and data ingestion rates based on performance tests executed at Pure Storage. AUDIENCE The target audiences for this document are data engineers, data scientists, systems architects, database administrators, field technical engineers, IT Managers, and storage administrators who are exploring ways to leverage an architecture that is data centric and addresses the four key challenges described above infrastructure requirements, scalability, expense, and performance. A working knowledge of Oracle database, Oracle R, R Studio, and Oracle SQL for the example presented below is assumed. 3 IDC FutureScape: Worldwide Analytics and Information Management 2018 Predictions 4 A blueprint is defined here as a design or pattern that can be followed to build a solution. 5

6 ANALYTICAL APPLICATIONS WORKLOADS Big data analytics workloads place high demands on underlying infrastructure in terms of performance, capacity, reliability, and security. In addition, the enormous growth of unstructured data forces IT departments to embrace infrastructure solutions that are both cost efficient, simple to deploy, and easy to scale. Without these capabilities, they run the risk of spiraling costs when supporting these hyper-growth workloads. Analytical Applications Data science teams should be focusing on the value-added task of building models without worrying about infrastructure. The faster analytical models are created, tested, and moved into production, the greater the impact they will have for the bottom line of a company. Today s organizations are increasingly turning to analytics to help solve difficult problems and uncover new opportunities. Common use cases include: Detecting Fraud The combination of multiple analytical methods can improve pattern detection and prevent criminal behavior. Optimizing Marketing Campaigns Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities in real-time. Improving Operations Many companies use predictive models to forecast inventory and manage resources. Reducing Risk Credit scores are used to assess a buyer s likelihood of default for purchases and are a well-known example of predictive analytics. Figure 3 shows the top areas organizations are investing their time with analytics. Operations, Security, and Fraud Detection are the top three, according to 451 Research. FIGURE 3: If you use log/machine data analytics in the future, which areas do you think would be the most likely to use? N=12 Source: 451 Research For predictive analytics, processes are needed to understand the data and apply statistics or machine learning algorithms to get insights for business objectives. This involves several phases or steps: Business Problem Define the business problem. Understanding the Data How the data applies to the challenge. Data Cleaning and Transformation Data cleaning includes removing and replacing junk data, and potentially filling in gaps. Data transformation consists of transforming the data to achieve the objective. 6

7 Data Enhancement Adding value to the data provided by seeking external sources or non-traditional data types. Data Analytics With all data in the desired format, data scientists perform analysis which provides insights for the business and aids in decision making. Data Visualization Presentation of the data outcomes. A step-by-step example of how to use Oracle R is presented in the Oracle R Enterprise Example section below. It highlights some common functions in R that are used to explore data and conduct statistical analysis. Analytics applications like R enable more advanced solutions using artificial intelligence and deep learning techniques. The next section examines the software and hardware architecture required to optimally support the workloads described above. ORACLE SOFTWARE LAYER ARCHITECTURE Many organizations leverage Hadoop to create their analytic environments. Hadoop is a large-scale distributed batch processing infrastructure. While traditionally it was used on a single machine, its true power lies in its ability to scale to hundreds or thousands of systems, each with several processing cores and direct attached storage. Hadoop is also designed to distribute large amounts of work across a set of machines. However, there are a number of challenges that data science and information technology teams face when building predictive analytics environments in Hadoop. 1. Hadoop is a complex distributed system with low-level APIs. It requires integration and interoperation of multiple open Apache source projects. 2. Specialized skills are required for using Hadoop, preventing most developers from effectively building solutions. 3. Real-time and batch ingestion requires deeply integrating several components REDUCE COST BY SIMPLIFYING YOUR ANALYTICAL ENVIRONMENT Consolidation and simplification is always top-of-mind for IT managers. Below is an overview of Pure Data Centric Architecture for analytics with Oracle 12c and Oracle R that simplifies the overall architecture deployment for analytics (Figure 4). Each layer depicts the application, tools and database used. Application Layer The application layer depicts typical solutions and models that data science teams build. These applications are used by analysts and other end users. 7

8 FIGURE 4. Oracle software architecture layer Software Layer Moving down the stack, the software layer shows a selection of Oracle tools used to build those analytics solutions, such as: Oracle Database Enterprise Edition. Oracle R Enterprise R Studio Oracle SQL Developer Oracle dnfs Oracle Linux Data Management Layer Oracle enterprise edition database is one of the world s most popular databases. Pure s FlashStack converged infrastructure solution for Oracle is optimal for hosting the following Oracle 12c components and/or features: Oracle Database 12c Release 2 ( ) Enterprise Edition Oracle Grid Infrastructure 12c ( ) Oracle Flex ASM & ASM Cluster File System (ACFS) Oracle Direct NFS Client For complete details about Oracle R Enterprise, JDeveloper, SQL Developer, and Oracle database, please refer to the Oracle web site. Oracle Direct Network File System (dnfs) is a key enabler for this solution. A brief description follows. For configuration details of dnfs, see the Oracle Installation and Upgrade Guide for Linux. 5 5 Oracle Installation and Upgrade Guide for Linux 8

9 Oracle dnfs Oracle dnfs is the NFS client functionality directly integrated in the Oracle RDBMS server. dnfs makes the task of configuring the Oracle database on Network Attached Storage (NAS) much easier compared to the standard Network File System (NFS) protocol. The dnfs Client in Oracle Database 11g, 12c, or higher supports NFSv3, NFSv4, and NFSv4.1 protocols to access the NFS server. The dnfs Client is capable of performing concurrent direct I/O by bypassing Operating System level caches. It also performs asynchronous I/O, which allows processing to continue while the I/O request is submitted and processed. These are two key performance and scalability advantages provided when compared to Kernel NFS clients. Another key feature of the dnfs Client is high availability. The dnfs Client performs load balancing requests across all specified paths (up to 4 parallel network paths). If one network path fails, then the dnfs Client will reissue I/O commands over any remaining paths, ensuring fault tolerance and high availability. One of the primary challenges of NFS administration is inconsistency with configurations across different platforms. The dnfs Client eliminates this problem by providing a standard NFS client implementation across all platforms supported by Oracle Database. This also makes NFS a viable option on platforms, like Windows, which don t natively support NFS. PURE DATA CENTRIC ARCHITECTURE Pure Data Centric Architecture (Figure 5) provides all the storage services block, file, or object needed to consolidate everything, including databases, virtual machines, analytics, and web scale applications. Pure FlashArray provides six nines ( %) availability for mission-critical enterprises. As mentioned, the product portfolio includes FlashArray for block and virtual volumes, FlashBlade for file and object, and FlashStack for converged infrastructure. Pure solutions empower IT organizations to drive intelligence and advantage from their data. FIGURE 5. Pure Data Centric Architecture A brief description of key components used in this blueprint follows. 9

10 Pure Storage FlashStack FlashStack is a converged infrastructure solution. It is 100% all-flash and built with best-of-breed components from the Cisco Unified Computing System (Cisco UCS ) and Pure Storage. FlashStack s key differentiators are precisely those that drive enterprise digital transformation: open standards and stateless operation, ample performance and easy scalability, maximum density and minimal cabling. And while a FlashStack solution provides industry-leading simplicity, flexibility, and efficiency, it is also more economical than legacy disk-based converged infrastructure solutions. FIGURE 6. FlashStack with FlashBlade components Additional information about FlashStack can be found here: FlashStack website Flashstack Solution Brief Pure Storage FlashBlade FlashBlade is a new and innovative scale-out storage system designed to accelerate modern analytics applications while providing best-of-breed performance in all dimensions of concurrency including IOPS, throughput, latency, and capacity. FlashBlade is as simple as it is powerful, offering elastic scale-out storage services at every layer alongside DirectFlash technology for global flash management. FlashBlade is the industry s first all-flash array purpose-built for modern analytics, delivering unprecedented performance and scale for big data applications. It features a massively distributed architecture that enables consistent performance for all analytics applications using NFS, S3/Object, SMB, and HTTP protocols. Learn more about FlashBlade. 10

11 Purity for FlashBlade (Purity//FB 2) FlashBlade is built on the scale-out metadata architecture of Purity for FlashBlade, capable of handling tens of billions of files and objects while delivering maximum performance, effortless scale, and global flash management. The distributed transaction database built into the core of Purity means storage services at every layer are elastic: simply adding blades grows system capacity and performance instantly. Purity//FB 2 is the industry s most advanced software for scale-out storage, providing linear performance at unprecedented scale. It supports S3-compliant object store, offering ultra-fast performance at object store scale. It also offers a wave of new enterprise features, like snapshots, SMB, LDAP, network lock management (NLM), and IPv6, to extend FlashBlade into new use cases. Learn more about Purity. Pure1 Intelligent, Self-Driving Storage Pure1, is an AI-driven, cloud-based management, analytics, and support platform. It expands the self-managing, plugand-play design of Pure s products with the machine learning predictive analytics and continuous scanning of Pure1 Meta to enable an effortless, worry-free storage experience. Pure1 Manage In a Cloud IT operating model, installing and deploying management software should be easy: simply login. Pure1 Manage is SaaS-based, allowing you to manage your array from any browser or from the Pure1 Mobile App with nothing extra to purchase, deploy, or maintain. Just login, and from a single dashboard, manage all arrays, with full visibility on the health and performance of all storage deployed. Pure1 Analyze Pure1 Analyze delivers revolutionary true performance forecasting providing customers complete visibility into the performance and capacity needs of their arrays both current usage and projections into the future. Performance forecasting enables intelligent consolidation and unprecedented workload optimization. Pure1 Support Pure s philosophy is always to do what is right for the customer. We focus on delivering a solution regardless of the situation. The combination of an ultra-proactive support team with the predictive intelligence of Pure1 Meta offers unrivaled support that is a key component in our proven FlashArray % availability. Pure1 Support even fixes issues you did not even know existed. Learn more about Pure1. Evergreen Storage The Evergreen Storage ownership model operates like SaaS. Deploy storage once and benefit from a subscription to continuous innovation as you expand and improve performance, capacity, density, and/or features for 10 years or more all without downtime, performance impact, or data migrations. Evergreen Storage provides expandability and 11

12 upgradability for multiple product generations via its modular, stateless architecture. And the blade-based design of FlashBlade delivers linear scale with DirectFlash technology and compute simply by adding blades. Learn more about Evergreen Storage. PERFOMANCE AND SCALABILITY The complexity and diversity of data analytics presents difficult challenges. Modern data analytics workloads change quickly, and it is impossible to benchmark and measure every possible workload. This section describes the test environment used to gather performance metrics. 6 The environment tested is a deployment of Oracle Database 12c RAC on FlashStack with FlashBlade. For the compute layer, we have used two chassis with 8 identical Intel CPU based Cisco UCS B-series B200 M4 blade servers for hosting the 8-node Oracle RAC database. Server Configuration Two chassis with 4 identical Intel CPU based Cisco UCS B-series B200 M4 blade servers were deployed for hosting the 8-node Oracle RAC database. These servers have Cisco UCS VIC 1340 cards and were connected by four ports from each Cisco Fabric extender of the Cisco UCS chassis to the Cisco Fabric Interconnect and were then connected to the Cisco Nexus 9000 switch for upstream connectivity to access the Pure FlashBlade NFS volumes. The server configuration is described below. COMPONENT DESCRIPTION PROCESSOR 2 X INTEL XEON E V3 2.6 GHZ (2 CPUS WITH 14 CORES EACH) MEMORY 2.1GHZ (8 X 32GB) HBA 4 X 10G PORTS ON CISCO UCS VIC 1340 (UCS-IOM-2208XP) 40GBPS NIC 4 X 10G PORTS ON CISCO UCS VIC 1340 BIOS TURBO BOOST, HYPER THREADING, VIRTUALIZATION TECHNOLOGY (VT), VT FOR DIRECTED IO, INTEL ATS SUPPORT WERE ENABLED UCS FIRMWARE (ACROSS ALL COMPONENTS) 3.1 (2B) 6 Full details of the test can be found in the Oracle FlashStack with FlashBlade white paper. 12

13 FlashBlade Configuration Pure Storage FlashBlade is purpose-built for modern analytics. FlashBlade can handle high volumes of data, leveraging a massively parallel architecture, and delivering a powerful environment that is fast, big, and simple. Demonstrating scalability and the high throughput performance of FlashBlade for Oracle were the primary focus areas for this test. The table below shows the components of the array. COMPONENT DESCRIPTION FLASHBLADE 7 X 17TB BLADES TO 14 X 17TB BLADES RAW CAPACITY RANGE 65 TB 167 TB CONNECTIVITY 4 X 40 GB/S REDUNDANT ETHERNET PHYSICAL 4U O.S VERSION PURITY PROTOCOL USED NFS V3 There were no special configurations or value changes from any normal confirmation. There are no performance knobs to tune on FlashBlade. The hosts are redundantly connected to the controllers through 4 x 40Gb connections (2 x 40Gb per controller module) from the redundant Cisco Nexus 9K switches. FlashBlade was loaded with Purity//FB version 2.0.6, which includes an NLM (Network Lock Manager) feature for the NFS protocol to provide advisory file locking semantics. Oracle dnfs Configuration Oracle dnfs was enabled at the RDBMS level on all database nodes, and the oranfstab was updated to reflect the same across every node. Note: Oracle dnfs is by default enabled on Oracle 12c. To disable dnfs, the RDBMS should be relinked with the dnfs_off option. How to check if Oracle dnfs is enabled at the database level and working as expected: Check the alert.log on the database server. It should show the following entry. Oracle instance running with ODM: Oracle Direct NFS ODM Library Version

14 Run a SQL query against v$dnfs_servers that should show the details of the dnfs mounts. SQL> select svrname, dirname, mntport, nfsport, wtmax, rtmax from v$dnfs_servers; SVRNAME DIRNAME MNTPORT NFSPORT WTMAX RTMAX /oradata /oraredo /oradata /oradata /oradata rows selected. Workload Configuration This benchmark seeks to examine large volumes of data with a high degree of complexity and give answers to critical business questions. The test was created to demonstrate a high-bandwidth, high throughput workload on FlashBlade. The database was approximately 15TB in size, and the workloads were generally sequential in nature, read intensive, and exercised large I/O sizes. Test Descriptions To illustrate FlashBlade s high throughput and scalability performance, the following tests were performed. Data Ingest Test Load 10TB of data onto the database, which tests the loading portion of the ETL operation. This test showcased the speed and scale of data to be handled in analytical environments. Blade Scalability Test Performed queries across 7, 11, and 14 blades. The test showed not just the general scalability capability of FlashBlade, but the scale-out functionality that makes it very simple to add and gain performance. DATA INGEST TEST RESULTS One of the objectives of the data load test was to attain the highest achievable data load based on the write bandwidth of FlashBlade. This simulates a real-world scenario for data loading where data would predominantly be read from a storage system that is different from the target system. Therefore, the setup included a Pure Storage FlashArray system (FA //M50) to hold the data (Figure 7). This avoided reading and writing to the same host, which would alter the results. To improve the overall ingest time and to reduce read time of the source data, Oracle ASM Clustered File System (ACFS) was configured using 8 equally sized volumes from the //M50, which was mounted automatically on all the RAC nodes. FlashBlade was configured with 7 blades. 14

15 FIGURE 7. Benchmark environment architecture The following process was used to generate data files and load them onto Oracle. 1. ACFS was setup on Pure FlashArray//M50 using 8 volumes and mounted across all 8 nodes. 2. Data was generated with a scale factor of 10,000 on ACFS. 3. External tables were built inside the Oracle database pointing to the raw data files on ACFS. 4. A permanent table structure was created on the database where the data was to be loaded. 5. Records from the external tables were selected using parallel clause and inserted into the permanent tables with parallel clause. The following table illustrates the time taken to load the data. The data was loaded serially but each individual insert as select was run using parallel clause. METRIC DATA SIZE LOAD TIME (M:S) TOTAL LOAD TIME TB 1H 20M 48S OVERALL WRITE RATE N/A 2.29 GB/S INGEST RATE N/A 8.05 TB/HR 15

16 The data load test achieved an 8.22 TB/hour ingest rate on FlashBlade with 7 blades. The ingest rate would be expected to increase with more blades added to the FlashBlade system, as the maximum write rate a 15-blade system can achieve is around 3.75 GB/s, which equates to 13 TB/hr. BLADE SCALABILITY TEST RESULTS As part of the blade scalability tests, a set of jobs that examine large volumes of data with a high degree of complexity were identified and performed by varying the blade count in FlashBlade. We also tested a full table scan query against the biggest table which had 60 billion records for 7, 11, and 14 blades on FlashBlade. The following graph illustrates the blade scalability test results. The overall response time of the jobs executed on the 8-node environment was plotted against 7, 11, and 14 blades. FIGURE 8. Blade scalability test Note: Throughput was calculated by dividing total physical reads performed by total run time. Runtime is the end-to-end time reported by Oracle in executing the queries and is not just limited to the I/O time waited by Oracle. Therefore, the throughput does not reflect the maximum throughput FlashBlade can achieve, but rather, it reflects only the result for queries. The results demonstrated the tremendous scalability of FlashBlade, as throughput went from 5.48 GB/s with 7 blades, to 7.92 GB/s with 11 blades, a 69% increase in throughput. 14 blades achieved 9.29 GB/s, an 85% throughput improvement. Ultimately, FlashBlade performance improved dramatically as more blades were added to the test. The second scalability test was performed by running full scans in parallel against the biggest table (7.5TB in size). The run time was captured while testing different blade quantities on FlashBlade. During testing, most time was spent on direct path reads, which performs reads directly into the user space and bypasses Oracle s buffer cache. 16

17 FIGURE 9. Second blade scalability test The results clearly show linear scalability from 7 blades to 11 blades and 14 blades. Oracle also equally distributed the parallel queries across the 8 RAC nodes, which helped in delivering high performance across all infrastructure components where I/O passes between hosts and storage (I/O modules, Fabric Interconnects, 9K switches). This test also validates FlashBlade s simple approach for users to scale out and gain performance. The following section demonstrates how to leverage Pure s Data Centric Architecture to deploy analytical projects with Oracle Enterprise R and reduce the complexity of other solutions. 17

18 ORACLE R ENTERPRISE EXAMPLE Understanding the Data What do you do with the data once you have it? What should you look for? What tools should you use? This section opens with some ideas on facilitating data understanding. In this case semi-structured data is loaded to the Pure solutions and needs to be accessed in Oracle R. A dataset in semi-structured format is loaded into the FlashBlade file system. DATA LOADING Step 1: Move your semi-structured data files (XML, JSON, CSV, et cetera), to FlashBlade. To access the data in Oracle, an Oracle directory needs to be created. The directory points to the location of the file in the operating system. Figure 1 shows the directory (EXT_TAB_DIR) where the Oracle external tables are created. FIGURE 10. Oracle directory Step 2: Create an external table in the Oracle database that points to the file in the OS file system (Oracle directory created in the previous step). The LOCATION clause has the name of the external file. In this case acs_or.csv. Once created, the data can be queried using SQL. FIGURE 11. External table create SQL statement 18

19 Oracle R and R Studio Step 1: Load the ORE (Oracle R Enterprise) library for R and connect to the database library (ORE). Once connected, the ore.sync() command is used to synchronize. Synchronization prepares the relational table to be used as a data frame in R. You can list the data frames with the ore.ls() command. FIGURE 12. Connection and synchronization in Oracle R Step 2: Assign the external table to a data frame acs_df <= ore.pull(ext_acm). Using the class function to identify the object type. You can see the object class by using the class method. In this case you can see the class(ext_acm) is of ore.frame type and class(acs_df) is an R data frame. The last three commands highlighted in the screenshot show another way to assign the external table to a data frame and print the first few rows. You can also be selective and reduce the number of columns using head(ext_acm[,c(1,4,10)]). FIGURE 13. External table to data frame in R 19

20 Step 3: Run simple statistics (mean, median, quantile, variance, or standard deviation) using the external file. Figure 14. Basic statistics Step 4: Do some Exploratory Data Analysis (EDA) using the summary command and plot the data. Figure 15. Exploratory Data Analysis (EDA) The embedded R execution capability of Oracle Advanced Analytics enables invoking scripts from R Studio. This greatly simplifies the process of computing and storing models. Moreover, standard database backup and recovery mechanisms already in place can be used to avoid having to devise separate specialized practices. From this point forward you can use any of the R algorithms to create your predictive analytics models. 20

21 CONCLUSION Armed with faster and deeper data insights, organizations that leverage Pure Data Centric Architecture with Oracle software can better serve their most demanding customers and gain a competitive edge over the competition. This blueprint revealed how to simplify the deployment of typical data analytics environments. It also provided a relevant and real world set of performance characteristics based on testing performed at Pure to demonstrate high performance, high availability, and the low TCO of the solution. A summary of the key benefits described in this blueprint are: Performance Excellent throughput performance, achieved across test workloads through high-end data ingest and queries Data load and high-end ingest capability by loading datasets with a higher degree of scaling (8.22 TB/hour ingest rate on FlashBlade with 7 blades) Consistent sub-millisecond latency based on 100% flash storage Scalability Throughput was demonstrated to scale linearly from 7 to 11 and to 14 blades on FlashBlade Pure Storage helps users scale easily, without disruption Achieve repeatable growth through multiple FlashStack deployments Low TCO Dramatically simplify infrastructure and software requirements Huge savings in power, cooling, and floor space with all-flash Data reduction through compression Additional Benefits should be noted: Operational Simplicity (further reduces TCO) Fully tested, validated, and documented for rapid deployment Reduced management complexity No storage tuning or tiers necessary Consolidation of applications in a single rack Enterprise Grade Resiliency Highly available architecture and redundant components Non-disruptive operations Upgrade and expand without downtime or performance loss Overall, the blueprint and the test results prove that Pure Data Centric Architecture and Oracle is an ideal use case for FlashStack Converged Infrastructure with FlashBlade for data analytics. The solution delivers a host of benefits that simplify operations and lower cost for complex analytics workloads. 21

22 REFERENCES Gartner Survey Shows Organizations Are Slow to Advance in Data and Analytics February 5, IDC FutureScape: Worldwide Datacenter 2018 Predictions October Oracle FlashStack with FlashBlade Deploy a modern data warehouse Pure Storage Data Centric Architecture FlashBlade The industry s most advanced storage for unstructured data, consolidating complex data silos (like backup appliances and data lakes) to simplify infrastructure and accelerate tomorrow s discoveries and insights. The Need for Speed: Machine Data Analytics in November Pure Storage, Inc. All rights reserved. Pure Storage, the P Logo, Pure1, Pure1 Meta, FlashStack, and FlashBlade are trademarks or registered trademarks of Pure Storage, Inc. in the U.S. and other countries. Cisco UCS, and Cisco Nexus are registered trademarks of Cisco Systems, Inc. in the U.S. and other countries. Oracle and Oracle Linux are the registered trademarks of Oracle Corporation in the U.S. and other countries. The Pure Storage product described in this documentation is distributed under a license agreement and may be used only in accordance with the terms of the agreement. The license agreement restricts its use, copying, distribution, decompilation, and reverse engineering. No part of this documentation may be reproduced in any form by any means without prior written authorization from Pure Storage, Inc. and its licensors, if any. THE DOCUMENTATION IS PROVIDED AS IS AND ALL EXPRESS OR IMPLIED CONDITIONS, REPRESENTATIONS AND WARRANTIES, INCLUDING ANY IMPLIED WARRANTY OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NON-INFRINGEMENT ARE DISCLAIMED, EXCEPT TO THE EXTENT THAT SUCH DISCLAIMERS ARE HELD TO BE LEGALLY INVALID. PURE STORAGE SHALL NOT BE LIABLE FOR INCIDENTAL OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH THE FURNISHING, PERFORMANCE, OR USE OF THIS DOCUMENTATION. THE INFORMATION CONTAINED IN THIS DOCUMENTATION IS SUBJECT TO CHANGE WITHOUT NOTICE. ps_wp22p_flashstack-analytics-with-oracle_ltr_01 22