MapR: Best Solution for Customer Success
•Als PPTX, PDF herunterladen•
2 gefällt mir•6,101 views
MapR provides a platform for big data that allows organizations to handle both large volumes and real-time data processing. It discusses how MapR's platform can power real-time applications and analytics by speeding up the data to action cycle. The document outlines MapR customers' use cases across various industries and how their platform has helped organizations gain insights, improve customer experiences, and increase revenues.
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie MapR: Best Solution for Customer Success
Ähnlich wie MapR: Best Solution for Customer Success (20)
Mehr von MapR Technologies
Mehr von MapR Technologies (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
MapR: Best Solution for Customer Success
- 1. © 2015 MapR Technologies 1© 2015 MapR Technologies
- 2. © 2015 MapR Technologies 2 MapR: Best Solution for Customer Success Premier Investors High Growth 2X Growth In Direct Customers 90% Subscription Licenses Software Margins 140% Dollar-based Net Expansion 700+ Customers 2X Growth In Annual Subscriptions ( ACV) Best Product Apache Open Source
- 3. © 2015 MapR Technologies 3 CIO “Must Have” Strategies Over Time Distributed Computing? Big Data? Web? Ecommerce? Cloud? Social? Mobile? Open Source?
- 4. © 2015 MapR Technologies 4 CIO “Must Have” Strategies Over Time Distributed Computing? Big Data? Web? Ecommerce? Cloud? Social? Mobile? Open Source? Back Office “Automation” “Cost reduction” “Retrospective” Front Office “Customer intimacy” “Revenue creation” “Just-in-time”
- 5. © 2015 MapR Technologies 5 Advantage From Speeding Data-to-Action Cycle The “As it Happens” Business What happened historically? What can we do to affect change? What’s happening now?
- 6. © 2015 MapR Technologies 6 MapR Vision Empowering the “As-it-Happens” business by speeding up the data-to-action cycle
- 7. © 2015 MapR Technologies 7 MapR Architected A Platform For The Age Of Big Data Apps Databases Operational App platform Storage 1980s 2000s 2010s Big data apps RDBMs SAN/NAS Monolithic UNIX Linux RDBMs Scale out Web Structured Unstructured Operational Analytics
- 8. © 2015 MapR Technologies 8 Alternatives Do Big OR Fast, But Not Both Big Limited reliability Limited functionality Batch (mostly) Rewrite Existing Apps Fast Expensive Special purpose Coding required for speed Limited scale Limited functionality OR
- 9. © 2015 MapR Technologies 9 Multiple Workloads Today Hadoop Batch Import/ Export Any Operational DBMS Customer 360° Dashboard Churn Analysis (predictive analysis) DB Operations Web Application Server Mobile Application Server Data Exploration (SQL) This is how most clients solve Big and Fast
- 10. © 2015 MapR Technologies 10 For the ”As-it-Happens” business: It Must Be Both Big And Fast Unlimited Scale Data diversity Evolving schemas Affordable Secure Reliable Manageable Fast ingestion Batch & streaming Polyglot RDBMS & NoSQL General purpose Affordable Big FastAND
- 11. © 2015 MapR Technologies 11 Integrated Real Time and Actionable Analytics Real-time Ad Targeting Product/sService Optimization and Personalization • Single cluster • Real time • High performance, low latency • Large-scale analytics • Enterprise-grade HA/DR • Unified file and table administration Customer 360° Dashboard Churn Analysis (predictive analysis) DB Operations Web Application Server Mobile Application Server Data Exploration (SQL) One Integrated System For Big & Fast
- 12. © 2015 MapR Technologies 12© 2015 MapR Technologies I thought all Hadoop Vendors were the same…
- 13. © 2015 MapR Technologies 13 Other Hadoop Distributions Have Limitations APACHE HADOOP AND OSS ECOSYSTEM Security YARN Spark Streaming Storm StreamingNoSQL & Search Juju Provisioning & coordination Savannah ML, Graph Mahout MLLib GraphX EXECUTION ENGINES DATA GOVERNANCE AND OPERATIONS Workflow & Data Governanc e Pig Cascading Spark Batch MapReduc e v1 & v2 Tez HBase Solr Hive Impala Spark SQL Drill SQL Sentry Oozie ZooKeeperSqoop Flume Data Integration & Access HttpFS Hue MapR Data Platform (Random Read/Write) Can’t scale No update in-place or handling streams Single points of failure. No data center-class reliability Batch only. Need separate systems for real time HDFS can only be a platform for Hadoop and nothing else
- 14. © 2015 MapR Technologies 14 MapR: The Only Platform Architected For Big, Fast, Reliable APACHE HADOOP AND OSS ECOSYSTEM Security YARN Spark Streaming Storm StreamingNoSQL & Search Juju Provisioning & coordination Savannah ML, Graph Mahout MLLib GraphX EXECUTION ENGINES DATA GOVERNANCE AND OPERATIONS Workflow & Data Governanc e Pig Cascading Spark Batch MapReduc e v1 & v2 Tez HBase Solr Hive Impala Spark SQL Drill SQL Sentry Oozie ZooKeeperSqoop Flume Data Integration & Access HttpFS Hue MapR Data Platform (Random Read/Write) MapR-FS HDFS and NFS APIs MapR-DB (High-Performance NoSQL) More efficient use of infrastructure First new database designed for operational real-time Surf any data easily Industry’s only mirroring, point- in-time consistent snapshots Trillion files Unique Unique Unique Unique Unique 2-7x faster Open source Projects ‘inherit’ MapR’s platform attributes
- 15. © 2015 MapR Technologies 15 Real-Time Hadoop Requirements REAL-TIME DATA REAL-TIME APPLICATIONS REAL-TIME ANALYTICS
- 16. © 2015 MapR Technologies 16 TechValidate: Others Say So
- 17. © 2015 MapR Technologies 17© 2015 MapR Technologies MapR and Teradata
- 18. © 2015 MapR Technologies 18 "Our customers were saying, 'Look, I have chosen Teradata as best-in-class data warehousing; I have chosen MapR as best- in-class Hadoop infrastructure. I want you guys to integrate those together,'" said Teradata Vice President of Product and Services Marketing Chris Twogood. "Amongst our customers, 90 percent of them were saying we want tighter integration.“ MapR Hadoop Gets Blessing From Data Warehouse Giant Teradata CRN, Nov 19, 2014
- 19. © 2015 MapR Technologies 19 Partnership Announced on November 19th, 2014 • Teradata’s UDA one-stop-shop strategy is now extended to MapR – Teradata customers get single vendor accountable for the full Hadoop ecosystem – Teradata now a strategic partner to design, implement & provide solutions with MapR • Deeper software integration between Teradata and MapR – Seamless access across the Teradata UDA – Teradata QueryGrid to support MapR – Teradata Loom fully supports MapR Distribution for Hadoop • Resell of subscriptions (license + support) for 3 core MapR products – MapR Community Edition (free version, Teradata Customer Support) – MapR Enterprise Edition (license and Teradata Customer Support) – MapR Enterprise Database Edition (license and Teradata Customer Support) • Teradata can resell Professional Services and Training curriculum – Teradata can sell MapR education and training services and professional services to Teradata customers – Through one phone call, provides single vendor accountability across the UDA MapR & Teradata Expand Partnership Partnership extends Hadoop choices by providing integration, joint product development, and unified go-to-market strategy
- 20. © 2015 MapR Technologies 20 • Economically Capture and Store More Data • Analytics on Low Business Value Density Data • Enterprise-grade NoSQL Database • Economically Reuse and Share Data • Analytics on High Business Value Density Data • EDW Leader Right Tool for the Right Job Teradata and MapR – Architecture Matters
- 21. © 2015 MapR Technologies 21 Marketing Executives Operational Systems Frontline Workers Customers Partners Engineers Data Scientists Business Analysts' Math and Stats Data Mining Business Intelligence Applications Languages Marketing APPLICATIONS USERS DISCOVERY PLATFORM INTEGRATED DATA WAREHOUSE ERP SCM CRM Images Audio and Video Machine Logs Text Web and Social SOURCES DATA PLATFORM TERADATA UNIFIED DATA ARCHITECTURE ACCESSMOVEMANAGE STREAMING & NOSQL APP’S STREAMING & NOSQL APP’S
- 22. © 2015 MapR Technologies 22 Capability Community Edition Enterprise Edition Enterprise Database Edition MapR Distribution including Apache Hadoop Apache Hadoop & Open Source Projects Core Hadoop, Cascading, Drill, Flume, HBase, Hive, Hue, HttpFS, Impala, Juju, Mahout, MapReduce, Oozie, Pig, Solr, the full Spark stack, Sqoop, YARN, ZooKeeper ✔ ✔ ✔ MapR Data Platform and System Management Performance ✔ ✔ ✔ Scalability ✔ ✔ ✔ Direct Access NFS™ ✔ ✔ ✔ Standards-based APIs and Tools ✔ ✔ ✔ Manageability ✔ ✔ ✔ Integrated Security ✔ ✔ ✔ Multi-tenancy ✔ ✔ ✔ Advanced Multi-tenancy ✔ ✔ Consistent Snapshots ✔ ✔ High Availability ✔ ✔ Disaster Recovery ✔ ✔ MapR-DB - integrated enterprise-grade NoSQL ✔ ✔ Support Features Community Support ✔ ✔ ✔ 24x7 Commercial Support ✔ ✔ MapR Solutions Resold by Teradata
- 23. © 2015 MapR Technologies 23© 2015 MapR Technologies Customers & Use Cases
- 24. © 2015 MapR Technologies 24 MapR Use Case Inventory HEALTHCARE & LIFE SCIENCES GOVERNMENT ADVERTISING, MEDIA & ENTERTAINMENT • Improved ad targeting, analysis, forecasting and optimization • Personalized recommendations • Superior analytics capability • Enhanced game player engagement FINANCIAL SERVICES • Fraud Detection • Customer Segmentation Analysis • Customer Sentiment Analysis • Risk Aggregation • Counterparty Risk Analytics • New Products and Services for Consumer Card Holders • Credit Risk Assessment • 360-Degree Customer Service • Cybersecurity, Intelligence • Crime Prediction and Prevention • Defense, National Security • Pharmaceutical Drug Evaluation • Scientific Research • Weather Forecasting • Fraud Detection • Emergency Communications/Response • Traffic Optimization TELECOMMANUFACTURING OIL & GAS RETAIL • Personalized Treatment Planning • Assisted Diagnosis • Fraud Detection • Monitor Patient Vital Signs • Assembly Line Quality Assurance • Preventive Maintenance • Supply Chain and Logistics • Monitoring Product Quality through Telemetry Data • Real-time Parts Flow Monitoring • Product Configuration Planning • Market Pricing and Planning • Oil Exploration and Discovery • New oil prospect identification • Seismic trace identification • Oil Production • Equipment Maintenance • Reservoir Engineering • Safety and Environment • Security • Up-Sell/Cross-Sell Recommendations • Social Media Analysis • Dynamic Pricing Across Multiple Channels • Fraud Detection • Clickstream Analysis • Loyalty Program Benefits • 360° Customer View • Operational Intelligence • Customer Churn Analysis • Fraud Detection • Clickstream Analysis • Recommendations • Product Development • Network Management/Optimization
- 25. © 2015 MapR Technologies 25 Cisco was able to analyze service sales opportunities in 1/10 the time, at 1/10 the cost, and generated $40 million in incremental service bookings in the first year. Cisco: 360° Customer View Cisco uses integrated customer data to increase revenues • Create shared view of customer & operations across 75,000 employees • Increase revenue opportunities with sales partners • Customer information was siloed in different divisions • Customer interactions were inconsistent and not satisfying • Missed opportunities for upselling/cross selling • Use MapR to collect customer information across touch points • Integrate billing, support, manufacturing, social media, websites, dial-in data • Generate new sales leads internally and for partners OBJECTIVES CHALLENGES SOLUTION Architecture for Sales Partner Opportunities Business Impact
- 26. © 2015 MapR Technologies 26 Cisco Data Platforms Reference Architecture “The entire market is starting to realize that data is everywhere and an agile ecosystem is paramount. The marketplace demands the flexibility to meet specific needs and decisions are being made based on how well the ecosystem players are integrated.” Arvind Bedi, Director IT, Cisco Systems DATABASES DOCS, CASES, CONTENT, SOCIAL MEDIA, CLICKSTEAM Data Storage and Processing ERP SFDC SAP HANA ON UCS AGILE ANALYTICS MAPR DISTRIBUTION FOR HADOOP Streaming (Spark Streaming, Storm) MapR-DB MAPR DISTRIBUTION FOR HADOOP Batch (MR, Spark, Hive, Pig, …) MapR-FS BIG DATA PLATFORM MISSION CRITICAL REPORTING DATA SECURITY, INFRASTRUCTURE CUSTOMER NETWORK, PRODUCT USAGE INTERNET OF EVERYTHING (IoE) SELF SERVICE DASHBOARD RAPID BUSINESS MODEL DATA EXPLORATION REAL TIME PREDICTIVE MISSION CRITICAL OPERATIONAL REPORTS FINANCIAL REPORTING & EXTRACT DATA ANALYSIS, TEXT ANALYTICS MACHINE LEARNING, STATISTICAL ANALYSIS MACHINE DATA INSIGHTS FINANCIALS STABLE CORE CONTROLLED CHANGE Network of Trust MapR Data Platform Data ConsumptionData Sources ALL Other Sources Data Bases (Mobile/ Browser/ Data Service) Interactive (Drill, Impala)
- 27. © 2015 MapR Technologies 27 Increase in Website engagement and conversion with more relevant offers Improved customer satisfaction and reduced churn Higher performance (35-60% faster) & reliability for customer-facing applications Improving the Customer Experience with Big Data Large brokerage increases customer acquisition rate and improves customer service • Increase conversion rate of online visitors to customers • Provide real-time response for call center agents to service customers • Inability to provide customers with right content through right channel • Lack of real-time 360 view into customers • Operational issues with prior Hadoop distribution • Easy ingestion of customer, application and clickstream data with NFS • Deeper insights to customer preferences across web, mobile, call center • Real-time contextual offers to clients using MapR-DB • Real-time streaming application for customer support with Storm on MapR OBJECTIVES CHALLENGES SOLUTION Business Impact LARGE BROKERAGE
- 28. © 2015 MapR Technologies 28 MAPR DISTRIBUTION FOR HADOOP NFS, Sqoop, Flume MapR-DB Hive & Datameer Ingest, Transform, Enrichment (Batch Processing) Interactive analytics Operational Drill MapR-FS Streaming MapR Data Platform MapR-FS MapR-DB Storm Architecture and Use Cases Data Movement Data Access Relevant customer offers & experience CUSTOMIZED FINANCIAL PRODUCT CONTEXTUAL CUSTOMER SUPPORT Real-time call center response CUSTOMER 360 & WEB Web Clickstream (Omniture) Application Logs (Splunk) Customer Data (Teradata) Data Sources Customer 360 database DISTRIBUTION FOR HADOOP 160 customer attributes Call Center Records
- 29. © 2015 MapR Technologies 29 For More Details Visit MapR.com
- 30. © 2015 MapR Technologies 30 Freeon-demand Hadoop training leading to certification Start becoming an expert now mapr.com/training 50MIn Free Training
- 31. © 2015 MapR Technologies 31 Q&A @mapr maprtech sales@mapr.com Engage with us! MapR maprtech mapr-technologies
Hinweis der Redaktion
- The MapR distribution for Hadoop is globally recognized as the technology leader Forrester published a Wave for Big Data Hadoop Solutions where it placed MapR as the highest ranking product based on current offering as well as roadmap. Cloud: MapR has been selected by two of the companies most experienced with MapReduce technology which is a testament to the technology advantages of MapR’s distribution. Amazon through its Elastic MapReduce service (EMR) hosted over 2 million clusters in the past year. Amazon selected MapR to complement EMR as the only commercial Hadoop distribution being offered, sold and supported as a service by Amazon to its customers. MapR was also selected by Google – the pioneer of MapReduce and the company whose white paper on MapReduce inspired the creation of Hadoop – has also selected MapR to make our distribution available on Google Compute Engine.
- I talk about the evolution of “Must Have” strategies over time Mention how nothing goes away IoT is next? Ask audience what they think might be next
- I usually give an example of something the CIO might be worried about with “Back Office” and “Front Office” Back Office, I talk to my time as IT person and talk about ensuring back office tools are available – back in the 80’s it was ok to just have phone and email working in the morning, now the expectation is much much higher Front Office, I talk about things like dashboards, metrics, numbers for C-level and across LOB. Again, expectation of getting information in easily consumable manner for front office very high today
- Examples of the questions that are being asked Example of the thought process of how data gets to action Data Lake/Data Hub example that for this to work data can’t be siloed
- 1980’s very regimented approach to the stack. Things happened in a certain way with structured data (schema first) and that was it, no options 2000’s we start with scale-out, not scale-up – the notion of the 80s of just throw more hardware at the problem is no longer acceptable 2010s data lake, operational and analytic apps together for query, no-schema’s, visualization of the apps is key
- Big Example of one bullet – HDFS vs. NFS for supporting/rewriting legacy apps (difficult, requires planning, resources, people, time) Fast Example of one – special purpose real-time apps or appliances, Oracle is always a good target here
- Talk about data movement, the grey arrows, how that’s still hard to do today. Moving data to then do batch processing on multiple workloads across structured/unstructured is not optimal. There has to be a better way.
- Big Pick one to talk to – I usually talk to schemas and give the Portal example (see next slide) Fast Polyglot – has to be able to support multiple languages, talk about developers. Can use example of the question I got on webinar from a C++ developer, “How much Java do I need to know to be able to work in Hadoop?”
- How MapR “fixes” Big & Fast and makes it doable, enterprise grade, fast, manageable, affordable.
- Pick one or two to talk to. I usually talk about HA/DR and how that’s an important component to big and fast. Also mention how this view is all the Apache Hadoop parts and next build is where our value add comes in.
- I usually talk to our start as a File System company and how that basis for our distribution differentiates us from the competition. Then talk to a couple of our unique differentiators. MapR-DB is a good one as most of the folks in the room will know little about us as a Hadoop vendor, so can really surprise them with no only Hadoop but database vendor as well.
- Again, pick one or two to talk to. As I usually have talked to HA/DR, I leave that one and talk about Multi-tenancy and Performance.
- This is the “Why Teradata and MapR” slide. It really speaks for itself and I usually put it up and let folks read it and make a comment along the lines of “when a company the size of Teradata says that 90% of their customers want us working together, we listen.”
- Point out QueryGrid support for MapR which we announced at Teradata Universe EMEA event in April. Teradata Loom support coming this calendar year. And the reseller part is something that no one knew in the ones I’ve done thus far. So, yes, Teradata customers can purchase any of our core products from their Teradata rep. While it mentions the purchase of training, I usually push our free ODT training here too.
- MapR subscribes to the Gartner Logical Data Warehouse view Hadoop NOT a replacement for the DW – part of larger ecosystem Our value props Note: this exact model is how we position with SAP too.
- For the visual thinkers in the room. How MapR fits into the Teradata UDA (Unified Data Architecture). Data Sources (structured and unstructured) on the left Feed into MapR and Teradata In UDA – management, movement, access of data – data lake, hub Exported to apps And finally to UI
- Cisco IT built a Big Data Platform to transform data management and provide big data analytics services to Cisco business teams. Cisco used MapR for their enterprise Hadoop architecture to unlock hidden business intelligence of their globally distributed large data sets, including structured and unstructured information, while also providing service-level agreements (SLAs) for internal customers. The complete infrastructure solution let Cisco analyze service sales opportunities in 1/10 the time, at 1/10 the cost; generated $40 million in incremental service bookings in the current fiscal year; and yielded a multi-tenant enterprise platform while delivering immediate business value. Case study: https://www.mapr.com/customers/cisco
- This image is an abbreviated version of what Cisco has shown us as their big data reference architecture within their IT organization. Cisco uses MapR as their corporate Hadoop standard including the backbone of their real-time security information and event management (SIEM) solution. (get more details on Cisco use case slides here: https://drive.google.com/open?id=0B5TzetWfnSOGcW03ZkRhb1ZlNkE&authuser=0 Here you can see the “best of breed” approach Cisco maintains where MapR is used for large scale data storage, text analytics and machine learning and the DW is used for mission-critical financial reporting. SAP used for dashboarding.
- 1st use case was clickstream using applications logs which are ingested into Splunk and then into MapR 2nd 1/2 of last year they started using TDCH connector to bring data into Hadoop All ETL jobs using Hive and Datameer. use also for user analytics. generate some reports use Hive to create aggregated table with 160 attributes per user --> using to get into user 360 degree database - extract data and report in Tableau - reporting on # of visitors that landed on site and then converted into services, the banners they clicked on 2H2014 - 2nd phase of this project is they moved into MapR-DB and make it available to application users for personalized This customer 360 database is used to provide relevant Smart Banner was 2nd application and is separate cluster. They did the implementation themselves. 1st phase was moving use case onto MapR themselves 2nd phase - sent people to M7, admin, and Hive training - 10-12 people for training 3rd phase - real-time stream processing. Using our PS to develop real-time streaming application using Storm. Using data fomr schwab -- RT aggregation, ranking, and sorting for customer ... what are top 5 things they looked at, purchased, etc... then feed into real-time Oracle RT system for customer service so they can see what people looked at and best understand what next phase - make data more self-service to generate reports rather than going through IT. want to make the data more accessible to end users using Drill