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Ibm leads way with hadoop and spark 2015 may 15
1.
© 2015 IBM
Corporation IBM Leads the Way with Hadoop and Spark The Keys to Getting Value out of Big Data
2.
© 2015 IBM
Corporation2 IBM’s Framework for Getting Value out of Big Data All agree on Big Data’s potential, but wide divergence on how to exploit it Pioneers who have started to harness Big Data have benefited greatly We see Big Data adoption as a continual process – maturity levels IBM’s approach enables faster adoption of Big Data technologies Open source innovation (Hadoop, Spark) Standards-based technologies (ODP, SQL, R) Familiar interfaces and integration with established tools (IBM innovations) Advanced analytics (IBM innovations) IBM’s commitment for continued innovation
3.
© 2015 IBM
Corporation3 Hadoop and Spark Offer Significant Business Benefits Operations Data Warehousing Line of Business and Analytics New Business Imperatives Big Data Maturity High High Low Data-Informed Decision Making • Full dataset analysis (no more sampling) • Extract value from non-relational data • 360 o view of all enterprise data • Exploratory analysis and discovery Warehouse Modernization • Data lake • Data offload • ETL offload • Queryable archive and staging Lower the Cost of Storage Business Transformation • Create new business models • Risk-aware decision making • Fight fraud and counter threats • Optimize operations • Attract, grow, retain customers Value
4.
© 2015 IBM
Corporation4 IBM Investing in Four Catalysts for Big Data Adoption Familiar Interfaces & Integration with Established Tools Open Source Innovation Technical Standards New Analytics Capabilities
5.
© 2015 IBM
Corporation5 • Reliability • Resiliency • Security • Multiple data sources • Multiple applications • Multiple users Hadoop Advantages • Files • Semi-structured • Databases Unlimited Scale Enterprise Platform Wide Range of Data Formats
6.
© 2015 IBM
Corporation6 Hadoop MapReduce Challenges • Need deep Java skills • Few abstractions available for analysts • No in-memory framework • Application tasks write to disk with each cycle • Only suitable for batch workloads • Rigid processing model In-Memory Performance Ease of Development Combine Workflows
7.
© 2015 IBM
Corporation7 In-Memory Performance Ease of Development • Easier APIs • Python, Scala, Java • Resilient Distributed Datasets • Unify processing Spark Advantages • Batch • Interactive • Iterative algorithms • Micro-batch Combine Workflows
8.
© 2015 IBM
Corporation8 Spark Libraries Apache Spark Spark SQL Spark Streaming GraphX MLlib SparkR
9.
© 2015 IBM
Corporation9 Spark on Hadoop Apache Spark Spark SQL Spark Streaming GraphX MLlib SparkR Apache Hadoop-HDFS Apache Hadoop-YARN Resource management Storage management Compute layer Slave node 1 Slave node 2 Slave node n…
10.
© 2015 IBM
Corporation10 Spark on Mesos Apache Spark Spark SQL Spark Streaming GraphX MLlib SparkR Apache Hadoop-HDFS Apache Mesos Resource management Storage management Compute layer Slave node 1 Slave node 2 Slave node n…
11.
© 2015 IBM
Corporation11 Spark as a Service Apache Spark Spark SQL Spark Streaming GraphX MLlib SparkR Amazon S3 Resource management Storage management Compute layer Apache Hadoop-YARN Amazon EC2 node 1 Amazon EC2 node 2 Amazon EC2 node n…
12.
© 2015 IBM
Corporation12 Spark on the Amazon Cloud Apache Spark Spark SQL Spark Streaming GraphX MLlib SparkR Amazon S3 Resource management Storage management Compute layer Apache Hadoop-YARN Amazon EC2 node 1 Amazon EC2 node 2 Amazon EC2 node n…
13.
© 2015 IBM
Corporation13 Spark Running in Standalone Mode Apache Spark Spark SQL Spark Streaming GraphX MLlib SparkR Single node, with local storage Resource management Storage management Compute layer
14.
© 2015 IBM
Corporation14 Spark Resilient Distributed Datasets Slave node 1 c3 d2 a2 b1 partition3 partition1 partition2 Slave node 2 c2 d1 a1 b2 partition1 partition3 Slave node 3 c1 d2 a3 b3 partition2 partition2 partition1 RDD1 RDD2 RDD3 Spark RDD In-memory distribution HDFS On-disk distribution
15.
© 2015 IBM
Corporation15 The Combination: The Flexibility of Spark on a Stable Hadoop Platform In-Memory Performance Ease of Development Combine Workflows Unlimited Scale Enterprise Platform Wide Range of Data Formats
16.
© 2015 IBM
Corporation16 IBM Open Platform with Apache Hadoop 100% open source code Commitment to currency: “days, not months” Includes Spark Free for production use Decoupled Apache Hadoop from IBM analytics and data science technologies Production support offering available Apache Open Source Components HDFS YARN MapReduce Ambari HBase Spark Flume Hive Pig Sqoop HCatalog Solr/Lucene IBM Open Platform with Apache Hadoop
17.
© 2015 IBM
Corporation17 IBM is Committed to Open Source Open source technologies are the base for IBM software and solutions IBM’s long history of deep open source commitment Apache Software Foundation: Founding member in 1999 Cloud Foundry: #1 contributor; Basis for Bluemix OpenStack: #4 contributor; Basis for IBM’s IaaS Linux: #3 contributor; IBM first enterprise backer of Linux Hadoop/Spark: Extensive investment in open source contribution; Integration with Analytics software Infrastructure Systems Application
18.
© 2015 IBM
Corporation18 Goal of the Apache Software Foundation: Let 1000 Flowers Bloom! • 249 Top Level Projects, 40 Incubating • 2 Million+ Code Commits • IBM co-founded the ASF in 1999 and is a Gold Sponsor • The “Apache Way” is about fostering open innovation • Not a standards organization
19.
© 2015 IBM
Corporation19 Apache Hadoop Ecosystem: Rapid Innovation, Few Standards Distributions include different projects at different version levels “This proliferation of baskets [Hadoop distributions with different project versions] creates significant drag when it comes to building reliable applications ... makes it harder for customers to assess which basket of Hadoop that they need and harder for application developers to create solutions that work broadly.” – Raymie Stata, CEO, Altiscale Even though the project versions match, there are interface differences “Setting a baseline of Hive 13 so we get access to some new syntax. Try it on one, it works great... Try it on another that says it also has Hive 13, and we get ‘syntax error’ …” - Craig Rubendall, VP, SAS If the industry is truly committed to developing big data technologies and solutions …, it will require an ecosystem of providers … to create a consistent framework around which everyone can develop. - Siki Giunta, SVP, Verizon The Hadoop ecosystem is evolving at a faster pace than is comfortable “My personal speculation is that it comes from some who have been evaluating for a while seeing change occur so rapidly that they are dropping back for another look.” – Merv Adrian, VP, Gartner
20.
© 2015 IBM
Corporation20 Certify a standard “ODP Core” set of open source Hadoop family projects with specific versions and patch levels Develop tools and methods to help solution providers to test applications against the ODP Core. Contribute changes and fixes in the ODP Core Hadoop family projects to the ASF using the ASF processes. http://opendataplatform.org/
21.
© 2015 IBM
Corporation21 Open Data Platform Initiative Representation across the Hadoop ecosystem… • Hadoop distribution vendors • Software application providers • System integrators/consultants • Hardware vendors • Customers … who all believe in the need for a community-based effort to standardize Hadoop, which will lead to improved adoption
22.
© 2015 IBM
Corporation22 IBM Open Platform with Apache Hadoop adopts ODP Core BigInsights will include ODP certified Apache packages ODP will initially target core packages of a Hadoop distribution Packages will expand over time First certification set expected this summer Our goal for BigInsights on ODP Better compatibility and less testing against ecosystem software Enable IBM Hadoop capabilities to run on other ODP-certified Hadoop distributions HDFS YARN MapReduce Ambari HBase Spark Flume Hive Pig Sqoop HCatalog Solr/Lucene ODP * Candidate set of certified ODP modules – expected summer 2015 Apache Open Source Components IBM Open Platform with Apache Hadoop
23.
© 2015 IBM
Corporation23 Goal of the ODP: Enable Innovation to Flourish on a Common Platform • Complements the Apache Software Foundation’s governance model • ODP efforts focus on integration, testing, and certifying a standard core of Apache Hadoop ecosystem projects • Fixes for issues found in ODP testing will be contributed to the ASF projects in line with ASF processes • The ODP will not override or replace any aspect of ASF governance
24.
© 2015 IBM
Corporation24 Text Analytics POSIX Distributed File System Multi-workload, Multi-tenant scheduling IBM BigInsights Enterprise Management Machine Learning with Big R Big R IBM Open Platform with Apache Hadoop IBM BigInsights Data Scientist IBM BigInsights Analyst Big SQL BigSheets Big SQL BigSheets for Apache Hadoop IBM BigInsights for Apache Hadoop
25.
© 2015 IBM
Corporation25 IBM BigInsights for Apache Hadoop IBM System zIBM PowerIntel Servers On Cloud Your choice of infrastructure and deployment model
26.
© 2015 IBM
Corporation26 IBM Analytic Platform Capabilities IBM Software Integrates and Extends Hadoop and Spark Data Warehousing PureData for Analytics, Operational Analytics Entity Extraction and Matching Big Match Security and Compliance Optim, Guardium Audit and Encryption Data Integration and Governance Information Server Enterprise Search Watson Explorer Real-time Analytics Streams Predictive Modeling and Descriptive Statistics SPSS, Big R and Scalable Algorithms Analysis, Reporting, and Exploration Watson Analytics, Cognos, BigSheets Fast, ANSI SQL 2011, and Secure SQL Big SQL Enterprise File System GPFS-FPO Cluster Resource and Workload Management Platform Symphony Large Scale Text Extraction Big Text IBM Open Platform with Apache Hadoop
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© 2015 IBM
Corporation27 IBM Leads the Market and Analysts Agree “IBM’s all-in bet on Apache Hadoop clearly has had the biggest impact among developers we polled” - Evans Big Data Survey Leading Hadoop Distribution Leading Streaming Analytics Solution
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© 2015 IBM
Corporation28 IBM’s Investment in the Big Data Community Over 250,000 benefit from free Big Data skills training http://bigdatauniversity.com
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© 2015 IBM
Corporation29 Spark Technology Center Focal point for IBM investment in Spark Code contributions to Apache Spark project Build industry solutions using Spark Evangelize Spark technology inside/outside IBM Agile engagement across IBM divisions Systems: contribute enhancements to Spark core, and optimized infrastructure (hardware/software) for Spark Analytics: IBM Analytics software will exploit Spark processing Research: build innovations above (solutions that use Spark), inside (improvements to Spark core), and below (improve systems that execute Spark) the Spark stack Goal: To be the #1 contributor and adopter in the Spark ecosystem
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© 2015 IBM
Corporation30 The IBM Difference IBM delivers the foundation for Big Data – now and in the future Embraces open source Establishes standards Integrates with familiar interfaces and established systems Delivers advanced analytic capabilities Enables you to benefit from broader data and analytics capabilities Data Integration and Governance Predictive and Real-time Analytics Provides expertise to help you on your journey 6,000 partners Analytics services and solution centers
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