Big data oracle_introduccion
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Big data oracle_introduccion
- 1. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.1
- 2. Transformado el negocio con Big Data y Analytics Fran Navarro
- 3. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.3
- 4. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.4
- 5. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.5
- 6. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.6 222011-2016 12.5 Billion 2020 1.3 Billion Today Crecimiento Smart Phone Crecimiento Producción Datos
- 7. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.7 Big Data Drivers Ciudadanos Cosas Procesos Ingestar Almacenar Consultar Analizar
- 8. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.8 Uso Datos 12% Ejecutivos que entienden el impacto que los datos tendran en sus organizaciones Producción Datos
- 9. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.9 Capturando volumenes masivos de datos Analizando los datos Disponiendo de una plataformaunificada y segura Uso Datos Producción Datos ¿Como reducir este GAP?
- 10. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.10 Los Cuatro Pilares de la Gestión de los Datos Movlidad • Acceder a los datos donde y cuando se necesiten • Soportar variedad de dispositivos In-Memory • Velocidad y eficiencia • Reducir el coste de operación y admin Cloud • Elastico, Esc alable Flexible • Aumentar los ratios de utilización Big Data • 4 V’s: • Volumen • Variedad • Velocidad • Valor • Utilizar todos los datos disponibles
- 11. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.11 Definición de Mercado de Tecnologias Big Data Las tecnologías Big Data describen una nueva generación de arquitecturas y tecnológicas diseñadas para extraer valor (económico) para gran Variedad-Volumen de datos caracterizados por la alta velocidad de captura, descubrimiento y/o análisis Data Volume Data Variety Value Data Velocity Petabytes+Terabytes StreamingBatch $$$$$$ UnstructuredStructured
- 12. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.12 NoSQL y Big Data The Big Picture VOLUME VELOCITY VARIETY VALUE SOCIAL BLOG SMART METER 101100101001 001001101010 101011100101 010100100101
- 13. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.13 Nuevos Perfiles Big Data
- 14. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.14 Nuevos Perfiles Big Data
- 15. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.15 Time Big Data: El Reto en una Oportunidad Harness Big Data to Increase Business Value Business Value Today → High Variety → High Volume → High Complexity → High Velocity Big Data Challenges → Deep Analytics → High Agility → Massive Scalability → Real Time Tomorrow Big Data Platform VOLUME VELOCITY VARIETY VALUE SOCIAL BLOG SMART METER 101100101001001 001101010101011 100101010100100 101 Decisions based upon transactional data • Video and images • Documents • Social data • Machine-generated data Decisions based upon all data Before Big Data After Big Data
- 16. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.16 Solución Big Data = Datos + Análisis + Herramientas Fuente: McKinsey “Big data: What’s your plan?” (March 2013) TOOLS Self Service Data Discovery DATA Any Data, Any Source ANALYTICS Out-of-the box Analytics, New Models On Premise, On Cloud, On Mobile
- 17. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.17 Oracle Complete Big Data Analytics Solution OBIEE ENDECA BAM BIG DATA APPLIANCE BIG DATA CONNECTORS NoSQL DB DATA MINING SPATIAL,GRAPH ORACLE R RTD OEP On Premise, Oracle Cloud, On Mobile Big Data no es nada si no se puede analizar y Big Data & Analytics no son nada sin la infraestructura adecuada
- 18. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.18 El patron de uso con Big Data Cargas de Trabajo ETL y proceso Batch en Hadoop Data Factory SQL SQL NoSQL DW & BI Analytics Web • Scalable • Flexible • Cost Effective
- 19. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.19 Expandir el Data Warehouse con pequeños almacenes de datos MartsData Warehouse Σ Σ Business Intelligence Archiving • Online • Scalable • Flexible • Cost Effective Piscina de Datos El patron de uso con Big Data
- 20. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.20 Oracle Big Data Solution Oracle BI Foundation Suite Oracle Real-Time Decisions Endeca Information Discovery Decide Oracle Advanced Analytics Oracle Database Oracle Spatial & Graph Acquire – Organize – Analyze Oracle Big Data Connectors Oracle Data Integrator Stream Oracle Event Processing Apache Flume Oracle GoldenGate Oracle NoSQL Database Cloudera Hadoop Oracle R Distribution
- 21. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.21 Oracle Big Data Solution Oracle NoSQL Database Cloudera Hadoop Oracle R Distribution Scalable key-value store Scalable, low-cost data storage and processing engine Statistical analysis framework
- 22. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.22 Highlights 1. Multitenant Architecture 2. In-Memory 3. Adaptive Query Optimization 4. SQL Pattern Matching Oracle 12c for Data Warehousing
- 23. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.23 Ingredients for Performance: Optimized Use of Storage and Memory Oracle Database In-Memory DISK PCI FLASH DRAM Online Data Hottest Data Active Data Exadata Smart Flash Cache Exadata Storage Servers 10’s GB/sec 100’s GB/sec 50-100 GB/sec
- 24. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.24 Oracle Database In-Memory Option Leading edge In-Memory technology – Seamlessly integrated into Oracle Database Delivers Extreme Performance for – Analytics and Ad-Hoc reporting on live data – Enterprise OLTP and Data Warehousing – Scale-up and Scale-out Trivial to Deploy for All Applications and Customers
- 25. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.25 BOTH row and column in-memory formats for same data/table Simultaneously active and transactionally consistent 100X Faster Analytics & reporting: column format 2X Faster OLTP: row format Oracle Database 12c Breakthrough In-Memory Database Technology Column Format Memory Row Format Memory AnalyticsOLTP Sales Sales
- 26. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.26 Comparing Hadoop and RDBMS 14:45 02:33 00:00 05:00 10:00 15:00 20:00 Pattern matching within a full 1B record dataset Exadata Quarter Rack (2 nodes) Big Data Appliance (18 nodes) Exadata Big Data Appliance Identify suspicious transactions based on transaction time and transaction location
- 27. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.27 Comparing Hadoop and RDBMS 01:13 00:10 00:01 00:00 00:20 00:40 01:00 01:20 Pattern matching: single day out of 60 days history Oracle Database delivers interactive performance ExadataBig Data Appliance In-memory
- 28. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.28 Engineered System Commodity Cluster Deployment Options
- 29. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.29 Platform Strengths Big Data Appliance + Hadoop Exadata + Oracle Database Low-cost Scalability Flexible Schema on Read Abstract Storage Model Open Rapid Evolution Extreme Performance Highly Secure Analytic SQL Rich Tool Set Vast Expertise
- 30. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.30 Hadoop Cluster HW Design Identify SW Optimize HadoopProcurement Rack and Stack Software Install Do It Yourself Hadoop Cluster $0k $700k Support Install Software Hardware Design Initial Infrastructure Cost1 1 http://www.oracle.com/us/corporate/analystreports/industries/esg-big-data-wp-1914112.pdf
- 31. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.31 Oracle Big Data Appliance Initial Infrastructure Cost1 Beats DIY Clusters on: Initial Cost Time to Value Operations Performance $0k $700k DIY BDA 1 http://www.oracle.com/us/corporate/analystreports/industries/esg-big-data-wp-1914112.pdf
- 32. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.32 Oracle Big Data Appliance 40% Cost Savings 33% Faster Time to Value OptimizedLower risk. Engineered to perform.
- 33. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.33 Produciendo Resultados Hoy 26% Performance improvement experienced from big data today Source: Economist Intelligence Unit, .”The Deciding Factor: Big Data and Decision Making“
- 34. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.34 Casos de uso de Big Data 34 Activity Revenue assurance Churn analysis Pricing optimization Smart meter monitoring Fraud Detection Traffic flow optimization Customer behavior analysisSocial network analysis Legal discovery Healthcare outcomes analysis Life sciences research Natural resource exploration Weather forecasting IT infrastructure optimization Warranty management Document mgmt and access Web application optimization Advertising analysis Equipment monitoring
- 35. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.35 Market Maturity Model for Target Verticals*1 Low High Low High A C B Gaming Oil & Gas Health care Retail G’ment Internet & Social Research G’ment Intel Emerging Markets Telco Mfg. Financial Services Big Data Technology Usage BigDatausecase definitionlevel *1 Source – Review of Big Data market by Oracle Product and Marketing Management Today’s Challenge New Data What’s Possible Healthcare Expensive office visits Remote patient monitoring Preventive care, reduced hospitalization Manufacturing In-person support Product sensors Automated diagnosis, support Location-Based Services Based on home zip code Real time location data Geo-advertising, traffic, local search Public Sector Standardized services Citizen surveys Tailored services, cost reductions Retail One size fits all marketing Social media Sentiment analysis segmentation
- 36. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.36 Indexación y Catalogación ¿Dónde están las oportunidades de Big Data? Enriquecimiento de Textos y NLP Descubrimiento Amenazas Control de Fraude Real Time Aduanas Innovación Estar preparado Identificación de Patrones y Relaciones Análisis de misiones Éxito/Impacto Clasificación y Clustering Reputación Análisis de Sentimiento Movilización en las redes Modelos Dinámicos Integración y Cruce de Información Adopción / Compartición de datos Terrorismo, Tráfico de drogas, identificación de actividades criminales Búsqueda avanzada en textos Análisis de Costes Análisis financiero
- 37. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.37 Las tecnologías Big Data ofrecen un nuevo abanico de aplicaciones en administración publica. Oracle Dispone de una arquitectura completa e integrada para un despliegue rápido y sin riesgos. En Resumen
- 38. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.38
- 39. Copyright © 2013, Oracle and/or its affiliates. All rights reserved.39
Notas do Editor
- Público
- And this is just the beginningSmart Devices are predicted to grow from 1.3B in 2013 to 12.5B in 2020And data generated from “things” is growing at a rate of 22 times over 5 years, from 2011-2016 (Source: IDC 2011, Cisco,, Cloudera, and Machina Research http://blog.iobridge.com/2012/02/cisco-reports-mobile-internet-of-things-traffic-to-grow/)There’s lots of available data, representing huge new opportunities to create new value. So, what’s the problem?
- The problem is the world’s ability to produce data has outstripped most organizations’ ability to use it.One of the largest airlines in the world, employing dozens of operational research analysts, throws away most of its fleet operational data at the end of the day because it’s so big there’s nowhere to put it and analyze it. (Source: a presentation by Jim Diamond, Managing Director of Operations & Research at American Airlines. Given at the Evanta CIO event in Dallas, TX 6/7/13)The same is true for many businesses: the information they need to improve products and services already exists, they’re just not quite sure how to use it.According to a study we conducted with The Economist Intelligence Unit, only 12% of executives feel they understand the impact data will have on their organizations over the next three years.” (Source: http://www.oracle.com/webapps/dialogue/ns/dlgwelcome.jsp?p_ext=Y&p_dlg_id=13367869&src=7634271&Act=143 )
- Third bullet - unify your data platform
- Las tecnologías Big Data describen una nueva generación de arquitecturas y tecnológicas diseñadas para extraer valor(económico)para granvariedad -Volumende datos caracterizados por la alta velocidad de captura, descubrimiento y/o análisis
- Depending on which analyst you talk to data volume is growing between 25%-50% per year, but one thing they all agree on is that data is growing faster than most businesses can deal with it effectively. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. For instance, a single jet engine can generate 10TB of data in 30 minutes. With more than 25,000 airline flights per day, the daily volume of just this single data source runs into the Petabytes. Smart meters and heavy industrial equipment like oil refineries and drilling rigs generate similar data volumes, compounding the problem. Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day). Variety. Traditional data formats tend to be relatively well described and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. As new services are added, new sensors deployed, or new marketing campaigns executed, new data types are needed to capture the resultant information. Value. The economic value of different data varies significantly. Typically there is good information hidden amongst a larger body of non-traditional data; the challenge is identifying what is valuable and then transforming and extracting that data for analysis. Notice, there is no focus on social here. It is because we are Oracle. And for us to succeed in selling big data solutions to our customers, we must represent the enterprise cases, and not get confined to merely the social data cases.
- Unix Admin 1.000Linux Admin 2.000Windows Admin 2.500Data Scientist 1.666iOs Developer 3.217Java Developer 13.500
- Unix Admin 1.000Linux Admin 2.000Windows Admin 2.500Data Scientist 1.666iOs Developer 3.217Java Developer 13.500
- So IT is seeing a change in the challenges they are facing – more data sources are coming online and the speed of refresh for those datasets is increasing. The variety of data types is growing – we are moving from the simple VARCHAR, NUMBER data types to more complex structures: images, videos, sound, emails, spatial coordinates, key-value pairs (e.g smart meters) as well as complex analysis of that data (test mining, data mining, spatial analytics, network analysis etc). Trying to manage all this data is proving to be complex. But by meeting these challenges you can increase the business value of the data you are storing and the processes accessing that data.To help support these challenges Oracle is proposing a Big Data Platform – this platform provides the ability to support: real-time data loading to meet the need for high velocityMassive scalability to meet the need to support high data volumesHigh agility to manage the variety of data sourcesDeep analytics to meet the need for complex analysis
- Other databases have row and column formats but you must choose ONE format for a given table.Therefore you get either fast OLTP or fast Analytics on that table but not both. Oracle’s unique dual format architecture allows data to be stored in both row and column format simultaneously. This eliminates the tradeoffs required by others.Up until now, this could only be achieved by having a second copy of the table (Data Mart, Reporting DB, Operational Data Store), which adds cost and complexity to the environment, requires additional ETL processing and incurs time delays.With Oracle’s unique approach, there is a single copy of the table on storage. So there are no additional storage costs, synchronization issues, etc.The Oracle optimizer is In-Memory aware. It has been optimized to automatically route analytic queries to the column store, and OLTP queries to the row store.
- THEME: Leveraging Strenghts of both WorldsTake a step back and look at the strengths of the two platforms - what can we do to
- WEAKNESS -> new, unfamiliar, not a lot of expertiseIn the early days, teams of developers, hardware experts and network engineers would design the system: identify the best CPU/disk/memory ratios engineer redundancy across the key components procure the components from their server and networking vendors identify key software - like the OS, JVM, Hadoop Distribution and NoSQL database once the servers arrive, they then rack and stack and network the servers procure the OS, a Hadoop distribution and/or NoSQL database install and configure the software across the cluster. Finally, they would tune the hundreds of configuration settings - in Hadoop, Java, OS - in order to ensure their workloads ran in a performant way.
- WEAKNESS -> new, unfamiliar, not a lot of expertiseIn the early days, teams of developers, hardware experts and network engineers would design the system: identify the best CPU/disk/memory ratios engineer redundancy across the key components procure the components from their server and networking vendors identify key software - like the OS, JVM, Hadoop Distribution and NoSQL database once the servers arrive, they then rack and stack and network the servers procure the OS, a Hadoop distribution and/or NoSQL database install and configure the software across the cluster. Finally, they would tune the hundreds of configuration settings - in Hadoop, Java, OS - in order to ensure their workloads ran in a performant way.
- WEAKNESS -> new, unfamiliar, not a lot of expertiseIn the early days, teams of developers, hardware experts and network engineers would design the system: identify the best CPU/disk/memory ratios engineer redundancy across the key components procure the components from their server and networking vendors identify key software - like the OS, JVM, Hadoop Distribution and NoSQL database once the servers arrive, they then rack and stack and network the servers procure the OS, a Hadoop distribution and/or NoSQL database install and configure the software across the cluster. Finally, they would tune the hundreds of configuration settings - in Hadoop, Java, OS - in order to ensure their workloads ran in a performant way.
- Explain market maturity model:Y axis: big data use case, how defined is it?X axis: big data technology usage, how intense is it currently?A – customer has defined that he wants to use Big Data, defined how, project is approved..but has not started using Big Data technologyB – customer has defined clear use cases for Big Data and has already adopted the technologyC – customer is looking for direction on how to apply Big Data in his business, there is no technology in place and (perhaps) no project is definedShaded area is our current sweet spot to sell into
- Empieza ya Adquirir conocimientos de hoy a partir de datos grande Aproveche el mejor gran plataforma de datos de precio / rendimiento Beneficiarse de la conexión más rápida entre los grandes entornos de análisis de datos Proteja su inversión a medida que progresamos en forma colectiva? La gran travesía de datos