Big Data World Forum (BDWF http://www.bigdatawf.com/) is specially designed for data-driven decision makers, managers, and data practitioners, who are shaping the future of the big data.
Big Data World Forum (BDWF http://www.bigdatawf.com/) is specially designed for data-driven decision makers, managers, and data practitioners, who are shaping the future of the big data.
Big Data World Forum (BDWF http://www.bigdatawf.com/) is specially designed for data-driven decision makers, managers, and data practitioners, who are shaping the future of the big data.
This document provides an overview of big data and real-time analytics, defining big data as high volume, high velocity, and high variety data that requires new technologies and techniques to capture, manage and process. It discusses the importance of big data, key technologies like Hadoop, use cases across various industries, and challenges in working with large and complex data sets. The presentation also reviews major players in big data technologies and analytics.
The document discusses the industry buzz around big data and the cloud. It provides an agenda for a webinar on these topics, including challenges of big data, architectural solutions using the cloud, and case studies. The document notes that data is growing exponentially and coming from more sources faster, creating challenges around complexity, validity, and linking diverse data sources. It argues the cloud can help address these challenges by providing vast, correlated, high confidence data to drive real-time predictions and recommendations.
Why Infrastructure Matters for Big Data & AnalyticsRick Perret
This document discusses how infrastructure is important for big data and analytics. It provides examples of how access, speed, and availability of infrastructure impact organizations' ability to gain insights from data. Specifically, it discusses how IBM's infrastructure capabilities such as data optimization, parallel processing, low latency, and scalability help companies like Bank of Quanzhou, Coca Cola Bottling, and Sui Southern Gas Company optimize access to data, accelerate insights, and maximize availability of information.
This document summarizes a conference on big data in the telecommunications industry. A key theme was that while telcos collect vast amounts of customer data, they have failed to fully utilize this data due to organizational silos. Belgacom provided a case study on how they consolidated customer data sources to better use all available information. Additionally, presenters discussed challenges around data quality and skills gaps that limit telcos' ability to generate insights from their data. Successful case studies demonstrated using predictive analytics to improve network investment and customer experience management. Overall, the conference highlighted that telcos should focus on using big data internally before attempting external sales of customer insights.
Big Data: Infrastructure Implications for “The Enterprise of Things” - Stampe...StampedeCon
At StampedeCon 2014, Jean-Luc Chatelain (DDN) presented 'Big Data: Infrastructure Implications for “The Enterprise of Things”.'
The amount of data in our world has been exploding, and storing and analyzing large data sets—so-called big data—will become a key basis of competition for the new “Enterprise of Things”, underpinning fresh waves of productivity growth, innovation, and consumer surplus. Leaders in every sector – from government to healthcare to finance – will have to grapple with the implications of big data, as data growth continues unabated for the foreseeable future. The quest to make sense of all this big data begins with breaking down data silos within organizations using the cost appropriate, shared infrastructure to ensure optimal extraction and analysis of data, knowledge and insight.
As the leading global e-commerce service, PayPal has transformed the way the company leverages big data storage and hyper-scale analytics to help improve both the safety and purchasing experiences of its online customers. In this discussion, using real-world customer examples such as PayPal, we will explore what Big Data Storage is from high performance file sharing to long-term archiving, as well as ways to break down data silos to reduce the cost and storage complexity of managing demanding workflows and data environments. We will demonstrate how hyperscale storage can enable near-real-time, stream analytics processing for behavioral and situational modeling, as well as for fraud detection, marketing and systems intelligence. We will ask what the greatest barriers to effective business analytics are and how today’s data analytics platforms, including Hadoop, Vertica, Python and Java, can be optimized to enable machine learning, event streaming, forecasting, and reduce overhead associated with human intervention. You’ll come away from this session understanding the infrastructure implications and options for organizations looking to maximize their big data for competitive advantage.
Accelerate Digital Transformation with Data Virtualization in Banking, Financ...Denodo
Watch full webinar here: https://bit.ly/38uCCUB
Banking, Financial Services and Insurance (BFSI) organizations are globally accelerating their digital journey, making rapid strides with their digitization efforts, and adding key capabilities to adapt and innovate in the new normal.
Many companies find digital transformation challenging as they rely on established systems that are often not only poorly integrated, but also highly resistant to modernization without downtime. Hear how the BFSI industry is leveraging data virtualization that facilitates digital transformation via a modern data integration / data delivery approach to gain greater agility, flexibility, and efficiency.
In this joint live webinar session from Denodo and Wipro, you will learn:
- Industry key trends and challenges driving the digital transformation mandate and platform modernization initiatives
- Key concepts of Data Virtualization, and how it can enable BFSI customers to develop critical capabilities for real-time / near real-time data integration
- Success Stories on organizations who already use data virtualization to differentiate themselves from the competition
- Wipro’s role in helping enterprises define the business case, end-to-end services and operating model for the successful data virtualization implementations
Schedule a Discovery Session to learn more about Wipro and Denodo joint solutions for Banking, Financial Services, and Insurance.
Big Data World Forum (BDWF http://www.bigdatawf.com/) is specially designed for data-driven decision makers, managers, and data practitioners, who are shaping the future of the big data.
Big Data World Forum (BDWF http://www.bigdatawf.com/) is specially designed for data-driven decision makers, managers, and data practitioners, who are shaping the future of the big data.
This document provides an overview of big data and real-time analytics, defining big data as high volume, high velocity, and high variety data that requires new technologies and techniques to capture, manage and process. It discusses the importance of big data, key technologies like Hadoop, use cases across various industries, and challenges in working with large and complex data sets. The presentation also reviews major players in big data technologies and analytics.
The document discusses the industry buzz around big data and the cloud. It provides an agenda for a webinar on these topics, including challenges of big data, architectural solutions using the cloud, and case studies. The document notes that data is growing exponentially and coming from more sources faster, creating challenges around complexity, validity, and linking diverse data sources. It argues the cloud can help address these challenges by providing vast, correlated, high confidence data to drive real-time predictions and recommendations.
Why Infrastructure Matters for Big Data & AnalyticsRick Perret
This document discusses how infrastructure is important for big data and analytics. It provides examples of how access, speed, and availability of infrastructure impact organizations' ability to gain insights from data. Specifically, it discusses how IBM's infrastructure capabilities such as data optimization, parallel processing, low latency, and scalability help companies like Bank of Quanzhou, Coca Cola Bottling, and Sui Southern Gas Company optimize access to data, accelerate insights, and maximize availability of information.
This document summarizes a conference on big data in the telecommunications industry. A key theme was that while telcos collect vast amounts of customer data, they have failed to fully utilize this data due to organizational silos. Belgacom provided a case study on how they consolidated customer data sources to better use all available information. Additionally, presenters discussed challenges around data quality and skills gaps that limit telcos' ability to generate insights from their data. Successful case studies demonstrated using predictive analytics to improve network investment and customer experience management. Overall, the conference highlighted that telcos should focus on using big data internally before attempting external sales of customer insights.
Big Data: Infrastructure Implications for “The Enterprise of Things” - Stampe...StampedeCon
At StampedeCon 2014, Jean-Luc Chatelain (DDN) presented 'Big Data: Infrastructure Implications for “The Enterprise of Things”.'
The amount of data in our world has been exploding, and storing and analyzing large data sets—so-called big data—will become a key basis of competition for the new “Enterprise of Things”, underpinning fresh waves of productivity growth, innovation, and consumer surplus. Leaders in every sector – from government to healthcare to finance – will have to grapple with the implications of big data, as data growth continues unabated for the foreseeable future. The quest to make sense of all this big data begins with breaking down data silos within organizations using the cost appropriate, shared infrastructure to ensure optimal extraction and analysis of data, knowledge and insight.
As the leading global e-commerce service, PayPal has transformed the way the company leverages big data storage and hyper-scale analytics to help improve both the safety and purchasing experiences of its online customers. In this discussion, using real-world customer examples such as PayPal, we will explore what Big Data Storage is from high performance file sharing to long-term archiving, as well as ways to break down data silos to reduce the cost and storage complexity of managing demanding workflows and data environments. We will demonstrate how hyperscale storage can enable near-real-time, stream analytics processing for behavioral and situational modeling, as well as for fraud detection, marketing and systems intelligence. We will ask what the greatest barriers to effective business analytics are and how today’s data analytics platforms, including Hadoop, Vertica, Python and Java, can be optimized to enable machine learning, event streaming, forecasting, and reduce overhead associated with human intervention. You’ll come away from this session understanding the infrastructure implications and options for organizations looking to maximize their big data for competitive advantage.
Accelerate Digital Transformation with Data Virtualization in Banking, Financ...Denodo
Watch full webinar here: https://bit.ly/38uCCUB
Banking, Financial Services and Insurance (BFSI) organizations are globally accelerating their digital journey, making rapid strides with their digitization efforts, and adding key capabilities to adapt and innovate in the new normal.
Many companies find digital transformation challenging as they rely on established systems that are often not only poorly integrated, but also highly resistant to modernization without downtime. Hear how the BFSI industry is leveraging data virtualization that facilitates digital transformation via a modern data integration / data delivery approach to gain greater agility, flexibility, and efficiency.
In this joint live webinar session from Denodo and Wipro, you will learn:
- Industry key trends and challenges driving the digital transformation mandate and platform modernization initiatives
- Key concepts of Data Virtualization, and how it can enable BFSI customers to develop critical capabilities for real-time / near real-time data integration
- Success Stories on organizations who already use data virtualization to differentiate themselves from the competition
- Wipro’s role in helping enterprises define the business case, end-to-end services and operating model for the successful data virtualization implementations
Schedule a Discovery Session to learn more about Wipro and Denodo joint solutions for Banking, Financial Services, and Insurance.
The document discusses how big data is driving the need for new database technologies that can handle large, unstructured datasets and provide real-time analytics capabilities that traditional relational databases cannot support. It outlines the limitations of relational databases for big data and analyzes emerging technologies like Hadoop, NoSQL databases, and cloud computing that are better suited for storing, processing, and analyzing large volumes of diverse data types. The document also examines the infrastructure, architectural, and market requirements for big data platforms and products.
Deutsche Telekom and T-Systems are large European telecommunications companies. Deutsche Telekom has revenue of $75 billion and over 230,000 employees, while T-Systems has revenue of $13 billion and over 52,000 employees providing data center, networking, and systems integration services. Hadoop is an open source platform that provides more cost effective storage, processing, and analysis of large amounts of structured and unstructured data compared to traditional data warehouse solutions. Hadoop can help companies gain value from all their data by allowing them to ask bigger questions.
This document provides information about IBM's Business Analytics software. It discusses how the volume, variety and velocity of data is growing exponentially creating opportunities and challenges for organizations. It highlights IBM's investments in analytics, big data, and acquisitions to help clients gain insights from both structured and unstructured data. Examples are given of how IBM is helping clients in industries like healthcare, retail, telecommunications, and government to solve complex problems and make smarter data-driven decisions.
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...Edureka!
This Edureka Big Data tutorial helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...Capgemini
Rip and replace isn't a good approach to IT change. When looking at Hadoop, MPP, in-memory and predictive analytics the challenge is making them co-exist with current solutions.
Learn how Capgemini’s Pivotal CoE utilizes Cloud Foundry and PivotalOne to help businesses adopt new technologies without losing the value of current investments.
Presented by Michael Wood of Pivotal and Steve Jones, Global Director, Strategy, Big Data and Analytics, Capgemini, at EMC World 2014.
Big data refers to the massive amounts of structured, semi-structured and unstructured data being created from sources like sensors, social media, digital pictures and videos, and transactional systems. This document discusses how the volume of data is growing exponentially from sources like RFID tags and smart meters. It also explores how insights can be extracted from big data through analyzing trends, correlations and other patterns in volumes, varieties and velocities of data beyond what was previously possible. However, as more data is created, the percentage of available data an organization can analyze is decreasing, making enterprises relatively "more naive" about their business over time.
The document provides an overview of IBM's big data and analytics capabilities. It discusses what big data is, the characteristics of big data including volume, velocity, variety and veracity. It then covers IBM's big data platform which includes products like InfoSphere Data Explorer, InfoSphere BigInsights, IBM PureData Systems and InfoSphere Streams. Example use cases of big data are also presented.
Top 10 ways BigInsights BigIntegrate and BigQuality will improve your lifeIBM Analytics
BigIntegrate and BigQuality offer 10 ways to improve an organization's ability to leverage Hadoop by providing cost-effective data integration and quality capabilities that eliminate hand coding, improve performance, ensure scalability and reliability, and increase productivity when working with Hadoop data.
Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...Cloudera, Inc.
This is the story of why and how Hadoop was integrated into the Disney data infrastructure. Providing data infrastructure for Disney’s, ABC’s and ESPN’s Internet presences is challenging. Doing so requires cost effective, performant, scalable and highly available solutions. Information requirements from the business add the need for these solutions work together; providing consistent acquisition, storage and access to data. Burdened with a heavily laden commercial RDBMS infrastructure, Hadoop provided an opportunity to solve some challenging use cases at Disney. The deployment of Hadoop helped Disney to address growing costs, scalability, and data availability. In addition, it provids our businesses with new data driven business to consumer opportunities.
Korean No. 1 company which has expertise in total data management and big data processing. Provide high performance software products and professional consulting services for data integration & migration, data governance, data warehouse and big data processing & storage.
Introduction to Modern Data Virtualization 2021 (APAC)Denodo
Watch full webinar here: https://bit.ly/2XXyc3R
“Through 2022, 60% of all organisations will implement data virtualization as one key delivery style in their data integration architecture," according to Gartner. What is data virtualization and why is its adoption growing so quickly? Modern data virtualization accelerates that time to insights and data services without copying or moving data.
Watch on-demand this webinar to learn:
- Why organizations across the world are adopting data virtualization
- What is modern data virtualization
- How data virtualization works and how it compares to alternative approaches to data integration and management
- How modern data virtualization can significantly increase agility while reducing costs
This document proposes a big data infrastructure and analytics solution using Hadoop. It discusses (1) constructing a Hadoop cluster on two physical machines, (2) transmitting both structured and unstructured data to HDFS, and (3) performing reporting, analysis, monitoring, and prediction using Hive, HBase, and Mahout. Experimental results show the Hadoop components running and sample queries executing successfully. Future work involves validating the infrastructure with real-world data and further predictive analytics research.
This document discusses big data and analytics. It notes that digital data is growing exponentially and will reach 35 zettabytes by 2020, with 80% coming from enterprise systems. Big data is being driven by increased transaction data, interaction data from mobile and social media, and improved processing capabilities. Major players in big data include Google, Amazon, IBM and Microsoft. Traditional analytics struggle due to batch processing and lack of business context. The document introduces OpTier's approach of capturing real-time business context across interactions to enable insights with low costs and flexibility. Potential use cases for financial services are discussed.
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
The document discusses big data and the open source big data stack. It defines big data as large datasets that are difficult to store, manage and analyze. Everyday, 2.5 trillion bytes of data are created, with 90% created in the last two years. The open source big data stack includes tools like Hadoop, HBase, Hive and Pig that can handle large datasets through distributed computing across multiple servers. The stack provides flexibility, reliability, auditability and fast deployment at low cost compared to proprietary solutions.
Value proposition for big data isv partners 0714Niu Bai
This document discusses IBM's Big Data value proposition for ISV partners. It highlights that IBM's Watson Foundations platform provides a complete set of tools to help organizations harness big data and analytics. The platform includes capabilities for data management, analytics, security, and governance. It also notes that IBM InfoSphere BigInsights provides an enterprise-grade Hadoop distribution with additional features for workload optimization, connectors, accelerators, and administration.
8.0Transforming records management for Information Governance
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4Frazer Clement
MySQL Cluster 7.4 has been benchmarked executing over 200 million queries per second on commodity hardware. This presentation from Oracle OpenWorld 2015 describes MySQL Cluster's architecture and gives some detail on how this benchmark was achieved, as well as some tips on getting started with MySQL Cluster 7.4.
This document discusses the growing demand for data storage and how it has transformed traditional data centers into mega data centers. It notes how organizations are relying more on their own large data centers rather than many smaller ones. Data center service providers are working to improve energy efficiency through strategies like locating in cold areas to reduce cooling needs and adopting new technologies. Looking ahead, global data center electricity demand is expected to increase substantially and account for 13% of total global electricity consumption by 2030 if growth continues, highlighting the importance of sustainability in the industry. The cover story profiles DigiPlex, a Nordic leader in innovative and sustainable data centers that aims to deliver high quality services while using only renewable energy sources.
Watch full webinar here: https://bit.ly/2vN59VK
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
- What data virtualization really is.
- How it differs from other enterprise data integration technologies.
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations.
Big data? No. Big Decisions are What You WantStuart Miniman
This document summarizes a presentation about big data. It discusses what big data is, how it is transforming business intelligence, who is using big data, and how practitioners should proceed. It provides examples of how companies in different industries like media, retail, and healthcare are using big data to drive new revenue opportunities, improve customer experience, and predict equipment failures. The presentation recommends developing a big data strategy that involves evaluating opportunities, engaging stakeholders, planning projects, and continually executing and repeating the process.
The document discusses how big data is driving the need for new database technologies that can handle large, unstructured datasets and provide real-time analytics capabilities that traditional relational databases cannot support. It outlines the limitations of relational databases for big data and analyzes emerging technologies like Hadoop, NoSQL databases, and cloud computing that are better suited for storing, processing, and analyzing large volumes of diverse data types. The document also examines the infrastructure, architectural, and market requirements for big data platforms and products.
Deutsche Telekom and T-Systems are large European telecommunications companies. Deutsche Telekom has revenue of $75 billion and over 230,000 employees, while T-Systems has revenue of $13 billion and over 52,000 employees providing data center, networking, and systems integration services. Hadoop is an open source platform that provides more cost effective storage, processing, and analysis of large amounts of structured and unstructured data compared to traditional data warehouse solutions. Hadoop can help companies gain value from all their data by allowing them to ask bigger questions.
This document provides information about IBM's Business Analytics software. It discusses how the volume, variety and velocity of data is growing exponentially creating opportunities and challenges for organizations. It highlights IBM's investments in analytics, big data, and acquisitions to help clients gain insights from both structured and unstructured data. Examples are given of how IBM is helping clients in industries like healthcare, retail, telecommunications, and government to solve complex problems and make smarter data-driven decisions.
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...Edureka!
This Edureka Big Data tutorial helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
1) Evolution of Data
2) What is Big Data?
3) Big Data as an Opportunity
4) Problems in Encasing Big Data Opportunity
5) Hadoop as a Solution
6) Hadoop Ecosystem
7) Edureka Big Data & Hadoop Training
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...Capgemini
Rip and replace isn't a good approach to IT change. When looking at Hadoop, MPP, in-memory and predictive analytics the challenge is making them co-exist with current solutions.
Learn how Capgemini’s Pivotal CoE utilizes Cloud Foundry and PivotalOne to help businesses adopt new technologies without losing the value of current investments.
Presented by Michael Wood of Pivotal and Steve Jones, Global Director, Strategy, Big Data and Analytics, Capgemini, at EMC World 2014.
Big data refers to the massive amounts of structured, semi-structured and unstructured data being created from sources like sensors, social media, digital pictures and videos, and transactional systems. This document discusses how the volume of data is growing exponentially from sources like RFID tags and smart meters. It also explores how insights can be extracted from big data through analyzing trends, correlations and other patterns in volumes, varieties and velocities of data beyond what was previously possible. However, as more data is created, the percentage of available data an organization can analyze is decreasing, making enterprises relatively "more naive" about their business over time.
The document provides an overview of IBM's big data and analytics capabilities. It discusses what big data is, the characteristics of big data including volume, velocity, variety and veracity. It then covers IBM's big data platform which includes products like InfoSphere Data Explorer, InfoSphere BigInsights, IBM PureData Systems and InfoSphere Streams. Example use cases of big data are also presented.
Top 10 ways BigInsights BigIntegrate and BigQuality will improve your lifeIBM Analytics
BigIntegrate and BigQuality offer 10 ways to improve an organization's ability to leverage Hadoop by providing cost-effective data integration and quality capabilities that eliminate hand coding, improve performance, ensure scalability and reliability, and increase productivity when working with Hadoop data.
Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...Cloudera, Inc.
This is the story of why and how Hadoop was integrated into the Disney data infrastructure. Providing data infrastructure for Disney’s, ABC’s and ESPN’s Internet presences is challenging. Doing so requires cost effective, performant, scalable and highly available solutions. Information requirements from the business add the need for these solutions work together; providing consistent acquisition, storage and access to data. Burdened with a heavily laden commercial RDBMS infrastructure, Hadoop provided an opportunity to solve some challenging use cases at Disney. The deployment of Hadoop helped Disney to address growing costs, scalability, and data availability. In addition, it provids our businesses with new data driven business to consumer opportunities.
Korean No. 1 company which has expertise in total data management and big data processing. Provide high performance software products and professional consulting services for data integration & migration, data governance, data warehouse and big data processing & storage.
Introduction to Modern Data Virtualization 2021 (APAC)Denodo
Watch full webinar here: https://bit.ly/2XXyc3R
“Through 2022, 60% of all organisations will implement data virtualization as one key delivery style in their data integration architecture," according to Gartner. What is data virtualization and why is its adoption growing so quickly? Modern data virtualization accelerates that time to insights and data services without copying or moving data.
Watch on-demand this webinar to learn:
- Why organizations across the world are adopting data virtualization
- What is modern data virtualization
- How data virtualization works and how it compares to alternative approaches to data integration and management
- How modern data virtualization can significantly increase agility while reducing costs
This document proposes a big data infrastructure and analytics solution using Hadoop. It discusses (1) constructing a Hadoop cluster on two physical machines, (2) transmitting both structured and unstructured data to HDFS, and (3) performing reporting, analysis, monitoring, and prediction using Hive, HBase, and Mahout. Experimental results show the Hadoop components running and sample queries executing successfully. Future work involves validating the infrastructure with real-world data and further predictive analytics research.
This document discusses big data and analytics. It notes that digital data is growing exponentially and will reach 35 zettabytes by 2020, with 80% coming from enterprise systems. Big data is being driven by increased transaction data, interaction data from mobile and social media, and improved processing capabilities. Major players in big data include Google, Amazon, IBM and Microsoft. Traditional analytics struggle due to batch processing and lack of business context. The document introduces OpTier's approach of capturing real-time business context across interactions to enable insights with low costs and flexibility. Potential use cases for financial services are discussed.
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
The document discusses big data and the open source big data stack. It defines big data as large datasets that are difficult to store, manage and analyze. Everyday, 2.5 trillion bytes of data are created, with 90% created in the last two years. The open source big data stack includes tools like Hadoop, HBase, Hive and Pig that can handle large datasets through distributed computing across multiple servers. The stack provides flexibility, reliability, auditability and fast deployment at low cost compared to proprietary solutions.
Value proposition for big data isv partners 0714Niu Bai
This document discusses IBM's Big Data value proposition for ISV partners. It highlights that IBM's Watson Foundations platform provides a complete set of tools to help organizations harness big data and analytics. The platform includes capabilities for data management, analytics, security, and governance. It also notes that IBM InfoSphere BigInsights provides an enterprise-grade Hadoop distribution with additional features for workload optimization, connectors, accelerators, and administration.
8.0Transforming records management for Information Governance
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4Frazer Clement
MySQL Cluster 7.4 has been benchmarked executing over 200 million queries per second on commodity hardware. This presentation from Oracle OpenWorld 2015 describes MySQL Cluster's architecture and gives some detail on how this benchmark was achieved, as well as some tips on getting started with MySQL Cluster 7.4.
This document discusses the growing demand for data storage and how it has transformed traditional data centers into mega data centers. It notes how organizations are relying more on their own large data centers rather than many smaller ones. Data center service providers are working to improve energy efficiency through strategies like locating in cold areas to reduce cooling needs and adopting new technologies. Looking ahead, global data center electricity demand is expected to increase substantially and account for 13% of total global electricity consumption by 2030 if growth continues, highlighting the importance of sustainability in the industry. The cover story profiles DigiPlex, a Nordic leader in innovative and sustainable data centers that aims to deliver high quality services while using only renewable energy sources.
Watch full webinar here: https://bit.ly/2vN59VK
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
- What data virtualization really is.
- How it differs from other enterprise data integration technologies.
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations.
Big data? No. Big Decisions are What You WantStuart Miniman
This document summarizes a presentation about big data. It discusses what big data is, how it is transforming business intelligence, who is using big data, and how practitioners should proceed. It provides examples of how companies in different industries like media, retail, and healthcare are using big data to drive new revenue opportunities, improve customer experience, and predict equipment failures. The presentation recommends developing a big data strategy that involves evaluating opportunities, engaging stakeholders, planning projects, and continually executing and repeating the process.
Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...Cloudera, Inc.
You will also learn how to understand key challenges when deploying a Hadoop cluster in production, manage the entire Hadoop lifecycle using a single management console, deliver integrated management of the entire cluster to maximize IT and business agility.
Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)Ajay Ohri
This document discusses IBM's vision for combining Hadoop and data warehousing (DW) platforms into a unified "Hadoop DW". It describes how big data is driving new use cases that require analyzing diverse data types at extreme scales. Hadoop provides a massively parallel processing framework for advanced analytics on polystructured data, while DW focuses on structured data. The emergence of Hadoop DW will provide a single platform for all data types and workloads through tight integration of Hadoop and DW capabilities.
About ActuateOne for Utility Analytics
Water and Energy Utilities are under tremendous pressure to demonstrate progress in asset optimization, grid optimization and performance gains across traditional business drivers such as customers, revenue protection, utility regulatory compliance and financials. ActuateOne for Utility Analytics provides a comprehensive portfolio of software and utility analytics industry expertise to ensure today’s utility leaders and customers always have access to the right information, insight and collaborative capabilities for accurate and informed decisions. Delivered through a single platform, ActuateOne for Utility Analytics ignites any utility or grid Analytics initiative with integrated asset optimization dashboards, grid optimization dashboards, utility compliance reports as well as Transformer Management Scorecards, Substation & Equipment Management Scorecards and Utility KPI Dashboards which help today’s Utility enhance performance and maximize grid performance.
Impulser la digitalisation et modernisation de la fonction Finance grâce à la...Denodo
Voir: https://bit.ly/2Oycfnn
À l’ère du numérique, la digitalisation et modernisation des services financiers sont plus que jamais requises compte tenu de leur rôle clé dans les processus de prise de décisions et le pilotage de la performance. Les directions financières nécessitent ainsi de fournir des informations fiables et vérifiées, tout en répondant aux exigences de gouvernance et de sécurité. À cela s’ajoute, l’étendue de leurs fonctions qui comprend désormais l’analyse prédictive des données. Cependant, ce pôle stratégique est souvent confronté à des défis tels que le difficile accès à la donnée ou la faible automatisation des tâches.
La Data Virtualization ou virtualisation des données permet d’accroître la valeur ajoutée de la fonction finance et il s’agit d’un levier qui permet de consacrer le plus du temps à l'analyse prédictive au détriment de la collecte et la consolidation depuis les différentes sources de données. Visionnez ce webinar pour découvrir comment la Data Virtualization permet de :
- Donner plus d'autonomie à la finance vis-à-vis de l'informatique, tant sur la modification des paramétrages, que sur la modélisation des règles de gestion, sur les éditions …
- Éviter la saisie multiple d'informations, de nombreux retraitements manuels, et procéder à différentes simulations.
- Effectuer des analyses multidimensionnelles
- Passer plus de temps sur les tâches à valeur ajoutée
- Utiliser, dans un seul outil, les données en provenance de plusieurs sources
- Se concentrer sur l’analyse plutôt que sur la consolidation des données
- Garantir la rigueur du reporting institutionnel
… et bien plus encore ! La séance comprend une démo live de cette technologie appliquée à l’analyse prédictive.
Ibm big dataibm marriage of hadoop and data warehousingDataWorks Summit
This document discusses IBM's Big Data platform and the marriage of Hadoop and data warehousing. It covers how Big Data is driving new use cases across enterprises due to the 3Vs of volume, velocity and variety. It also discusses how Hadoop and data warehousing complement each other by providing massively parallel processing for analytics on all types of data at scale. The emergence of the Hadoop data warehouse is examined as the next generation Big Data platform that can provide timely insights from both structured and unstructured data.
Cloud Migration Strategies that Ensure Greater Value for the BusinessDenodo
This document discusses data virtualization as a solution to challenges organizations face with data integration and accessibility. It summarizes the key benefits of data virtualization, including providing a single logical view of distributed data sources, reducing costs and time to access data, and improving data governance. The document also highlights findings from surveys that most organizations have significant amounts of data they cannot access or analyze due to data silos and complexity. It positions data virtualization and logical data fabrics as foundations for modern data architectures that can address these challenges.
This document discusses big data appliances and analytics on the cloud. It notes that big data refers to extremely large data sets that are difficult to manage and analyze using traditional databases and tools. Big data sizes range from dozens of terabytes to petabytes. The document outlines how cloud analytics can process vast amounts of data cost effectively and integrate cross-platform data to provide insights. It also discusses trends in data warehousing appliances, including a shift toward software over hardware and mixed workloads. The challenges of analyzing growing and diverse data sources are summarized.
The Briefing Room with Mark Madsen and Hortonworks
Slides from the Live Webcast on Oct. 16, 2012
The power of Hadoop cannot be denied, as evidenced by the fact that all the biggest closed-source vendors in the world of data management have embraced this open-source project with virtually open arms. But Hadoop is not a data warehouse, nor ever will it likely be. Rather, it's ideal role for now is to augment traditional data warehousing and business intelligence. As an adjunct, Hadoop provides an amazing mechanism for storing and analyzing Big Data. The key is to manage expectations and move forward carefully.
Check out this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature, who will explain how, where, when and why to leverage the open-source elephant in the enterprise. He'll be briefed by Jim Walker of Hortonworks who will tout his company's vision for the future of Big Data management. He'll provide details on their data platform and how it can be used to complete the picture of information management. He'll also discuss how the Hortonworks partner network can help companies get big value from Big Data.
Visit: http://www.insideanalysis.com
Hadoop was born out of the need to process Big Data.Today data is being generated liked never before and it is becoming difficult to store and process this enormous volume and large variety of data, In order to cope this Big Data technology comes in.Today Hadoop software stack is go-to framework for large scale,data intensive storage and compute solution for Big Data Analytics Applications.The beauty of Hadoop is that it is designed to process large volume of data in clustered commodity computers work in parallel.Distributing the data that is too large across the nodes in clusters solves the problem of having too large data sets to be processed onto the single machine.
This document discusses Intel's strategy to accelerate big data adoption in the Asia Pacific region over the next few years. It aims to deploy Apache Hadoop 2 years faster on Intel Xeon processors. Key opportunities mentioned include telecoms, financial services, government and healthcare. The strategy seeks to unlock value from data, support open platforms, and deliver software value from the edge to the cloud. Target sectors include OEMs, system integrators, independent software vendors and training partners.
APAC Big Data Strategy RadhaKrishna HiremaneIntelAPAC
This document discusses Intel's big data strategy in the Asia Pacific region in 2013. It aims to accelerate adoption of Apache Hadoop two years faster by deploying it on Intel Xeon processors. Key opportunities mentioned include telecommunications, financial services, government, and healthcare. The strategy seeks to unlock value from data, support open platforms, and deliver software value from the edge to the cloud. Case studies demonstrate how Hadoop has been applied in retail, genomics, telecommunications, traffic management, and other domains.
Data Ninja Webinar Series: Realizing the Promise of Data LakesDenodo
Watch the full webinar: Data Ninja Webinar Series by Denodo: https://goo.gl/QDVCjV
The expanding volume and variety of data originating from sources that are both internal and external to the enterprise are challenging businesses in harnessing their big data for actionable insights. In their attempts to overcome big data challenges, organizations are exploring data lakes as consolidated repositories of massive volumes of raw, detailed data of various types and formats. But creating a physical data lake presents its own hurdles.
Attend this session to learn how to effectively manage data lakes for improved agility in data access and enhanced governance.
This is session 5 of the Data Ninja Webinar Series organized by Denodo. If you want to learn more about some of the solutions enabled by data virtualization, click here to watch the entire series: https://goo.gl/8XFd1O
8.17.11 big data and hadoop with informatica slideshareJulianna DeLua
This presentation provides a briefing on Big Data and Hadoop and how Informatica's Big Data Integration plays a role to empower the data-centric enterprise.
This presentation on Open Source and Cloud Technologies was given by Vizuri SVP Joe Dickman at the 2012 Destination Marketing Technology Forum in Raleigh, NC. For more information please visit our website at www.vizuri.com or email solutions@vizuri.com.
The document discusses big data and Hadoop. It provides an introduction to Apache Hadoop, explaining that it is open source software that combines massively parallel computing and highly scalable distributed storage. It discusses how Hadoop can help businesses become more data-driven by enabling new business models and insights. Related projects like Hive, Pig, HBase, ZooKeeper and Oozie are also introduced.
Die Big Data Fabric als Enabler für Machine Learning & AIDenodo
This document discusses how a big data fabric can enable machine learning and artificial intelligence by providing a flexible and agile way for users to access and analyze large amounts of data from various sources. It explains that a big data fabric, powered by data virtualization, allows organizations to build a modern data ecosystem that provides governed access to both structured and unstructured data stored in different systems. This helps users develop new production analytics and insights. The document also provides an example of how Logitech used a big data fabric and data virtualization to improve their customer analytics.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
2. Turning Big Data Challenges into
Big Opportunities
Informatica for Big Data
Wei Zheng
Director, Product Management
2
3. A Little About Us – Informatica
The #1 Independent Leader in Data Integration
• Founded: 1993
• 2010 Revenue: $650 million $650
• 5-year Average Growth Rate: $600
20% per year $550
• Employees: 2,125+ $500
$450
• Partners: 400+
• Major SI, ISV, OEM and $400
On-Demand Leaders $350
• Customers: 4,280+ $300
• 84 of Fortune 100 $250
• 87%+ of Dow Jones $200
• Government Organizations in $150
20 countries
2005 2006 2007 2008 2009 2010
• # 1 in Customer Loyalty
Rankings (5 Years in a Row)
3
4. The Informatica Platform
Proven Value: Comprehensive, Unified, Open & Economical
Comprehensive Unified Open Economical
Supporting the Maximizing productivity Accessing transaction or Low Total Cost of
complete data with self‐service for interaction data from Ownership (TCO) ‐
integration lifecycle business & IT any source including leveraging
Hadoop
Adaptive data services Mitigating risk by
Enabling any data to deliver data as a working with what you Fast Return on
integration project service from any have now and in the Investment (ROI)
Delivering at any projects future Flexible deployment
latency from years to
scaled to your business
microseconds Consistent interfaces
Open to any domain, needs
across all processing
architectural styles
platforms
4
5. Big Data on Executive Agenda
MARCH 22, 2011, 11:30 A.M. ET.
Report to the President: Every Federal Agency Needs a 'Big Data'
Strategy
February 4, 2011
How Vendors Are Lowering Big Data
Barriers
March 26, 2011
A Model for the Big Data Era
Data-centric architecture is becoming
fashionable again
Today’s Leaders Are Racing to Uncover New Value and
Opportunities for Competitive Insights and Improved Operations
5
6. Defining Big Data
Definition: Big data is the confluence of the three trends consisting of
Big Transaction Data, Big Interaction Data and Big Data Processing
BIG TRANSACTION DATA BIG INTERACTION DATA
Online Online Analytical Social Other
Transaction Processing Media Data Interaction Data
Processing (OLAP) &
(OLTP) DW Appliances
Call detail
records, image,
click stream data
Scientific, genomic
BIG DATA INTEGRATION
Machine/Device
BIG DATA PROCESSING
6
7. Value of Big Data Integration
Unleash the full business potential of Big
Data to empower the data-centric
enterprise
7
8. Informatica 9.1: Harnessing the Power of Big
Transaction and Interaction Data
Big Data Integration
For All Data
Authoritative and
Trustworthy Data
For All Purposes
Self Service
For All Users
Adaptive
Data Services
For All Projects
8
10. Big Data Integration
Gain business value from Big Data
Big Data Processing Enabling Solutions
Extend Enterprise Near-universal connectivity
Environment with Other Interaction data to Big Transaction Data
Hadoop •Clickstream,
•Scientific/genomic,
Hadoop – Web Social Connectivity to Big
•Sensor –
processing, text mining, media Interaction Data including
machine/device,
fraud/risk analytics, •mobile, call detail social data
image processing,
records (CDR)
Hadoop - sandbox, •Image files, texts Connectivity to Hadoop
staging, archive
Large scale
processing –
OLTP, OLAP Your information Big Interaction Data
New type of DW management
appliances environment
Big Transaction Data
10
11. Big Transaction Data
Maximize availability and performance of big transaction data
Uncover
Better
Universal new areas
Actions &
Access for growth &
Operations
efficiency
All data including Reliable, complete Greater confidence
OLTP, OLAP and information Continuous
DW appliances No data discarded innovation
Near-Universal Connectivity
to Big Transaction Data
Database Warehouse Appliances
11
12. Big Interaction Data
Achieve a complete view with social and interaction data
Turn insights on relationships,
?
influences and behaviors Into
opportunities
Connectivity to Big Interaction
Data including social data
What What will she
How influence
Databases
do with this
connected does she Call Detailed Records,
merchandise?
Image Files, RFIDs
is she? have with her Any
Informatica MDM
family and …
Customer Product additional
External Data
Applications
friends? Providers
services?
12
13. Big Data Processing
Connectivity to Hadoop and Future Integration
Predictive Portfolio & Risk
Sentiment Fraud Detection Smart Devices
Analytics Analysis
Analysis
Hadoop Cluster
Graphical IDE for
Hadoop
Development
Future: Phase 2
Connectivity for
•• Codeless & metadata driven
Codeless & metadata driven
Hadoop (HDFS) development
development
•• Prepare & integrate data on Hadoop
Prepare & integrate data on Hadoop
9.1 HF1 – June 2011 •• Complete push down optimization
Complete push down optimization
•• Load data to Hadoop from any •• Metadata lineage
Metadata lineage
Load data to Hadoop from any
source
source
•• Extract data from Hadoop to any
Extract data from Hadoop to any
Weblogs, Mobile
target
target Databases, Semi-structured Cloud Applications,
Data, Sensor Data Data Warehouses Unstructured Enterprise Applications
Social Data
13
15. Multi-Style MDM
Single platform for all architectural styles and data domains
Multi-domain Multi-style
Customer Master Registry
Product Master Analytic
Chart of Accounts Co-existence
Location Master Universal MDM Transactional
… …
Multi-deployment Multi-use
Hub MDM Data Integration
Federated MDM Data Quality
MDM in the Cloud Data Services
MDM as a Service
…
…
15
16. Reusable Data Quality Policies
A Single Platform For Trusted Data
Business/IT Collaboration/Data Governance
Role-based, Unified, Process-driven
A Single Platform For Trusted Data
One Platform for Data Integration, Data Quality and MDM
Business Data Stewards Architects Developers
One One One
MDM Data Quality Data Integration
Re-usable/Consistent Data Quality Rules
16
18. Self Service Data Integration
SQL or Web
Service
BI Report
b
We
L or e
DI Analyst SQ ervic
S
Batch ETL
DI Developer Data Warehouse
Informatica’s self-service data integration doubles productivity by eliminating
manual steps and empowering analysts to do more on their own. Analysts can
define and validate source-to-target specifications in an intuitive browser-based
tool without a data architect or DBA. On top of that, once the analyst creates the
source-to-target specification, the mapping logic is automatically generated for a
developer to deploy to production.”
Sean Hickey, Manager Data Integration, T-Mobile
18
19. Self Service
Point-of-Use Data and Context for Business Users
SFDC
Account with
Data
Controls
Expand
Hierarchy
Account
Hierarchy
Data Control
19
20. Self Service
Pre-Built Application Accelerators to Jumpstart Projects
Tag content to
Develop mappings augment
based on business project
entities rather than metadata
individual tables
• Improved analyst & developer
productivity and collaboration
• Save project time and cut costs
Jumpstart mapping
20
22. Multi-Protocol Data Provisioning
Reusable Data Services
• Easily reuse DI logic/LDOs
for any mode/protocol
• Metadata-driven, visual,
graphical env (i.e., no-code)
• Execution & optimization
separate from design-time
• No re-development & re-
building of LDOs
• Reduce duplication of DI
development & maintenance
22
23. Integrated Data Quality
Apply Data Quality Rules at Point of Access, Dynamically
• Provision data quality
Data rules via data services–
Quality Integrated Data
v9.x.x Quality Read or Write
• Use library of templates
& data quality rules
• Auto generate data
quality transformations
• In real-time – no pre or
post-processing/staging
• Enforce data quality rules at
point of access
23
24. Informatica for Big Data Integration
Business Imperatives
Improve Mergers Increase
Deliver Increase Improve Acquire & Outsource Governance
Efficiency & Acquisitions Partner
Analytical Business Business Retain Non-core Risk
Reduce & Network
Insight Agility Processes Customers Functions Compliance
Costs Divestitures Efficiency
Big Data Real-Time Complex Big Data
Ultra Big Data Big Data Big Data Social /Big Data
Warehousing & Customer Event Collection &
messaging Services Archiving Consolidation Synchronization
Operational BI View Processing Aggregation
Deliver 5x Saved millions Rationalized Unite operations Increased Deliver cloud Turned human Reduce Time
25% savings in application monthly slot access to 177+ to Market by
faster & direct annually by across 200 brands review
data center portfolio and revenues by 4% million 90% by On-
access to improving over 100+ into automated
footprint ($1M+) saved $1 million while expanding businesses Boarding
customer, risk, trucking countries through alerts
reduce latency by with 6 month target customer worldwide and New Data
claims data in operations and migration of in seconds
83 percent to 340 payback. segments from 53 million Sources
variety of empowering business data for maritime
microseconds, Reduced age of 40 to 160 across contacts. D&B Faster and
sources – DW, business with from five systems security –
enabling a 580 data by 87% for 500 sources in 360 app enabling a
16 legacy, Hadoop-based to one through
percent increase service real-time with updates with wide variety
30000 data free-form geospatial and
in throughput over monitoring & social and linkedin and of Data
marts, 10M questions using video tracking
1B transactions pattern machine data twitter Formats
claims via data sensor, mobile
per day and identification of
feeds at 1/3 of and geospatial
growing large scale data
the cost data
24
25. Business Benefits of Informatica 9.1 for
Big Data
• Big Data Integration to gain business
value from Big Data
• Authoritative and Trustworthy Data to
increase business insight and consistency
by delivering trusted data for all purposes.
• Self-Service to empower all users to
obtain relevant information while IT
remains in control
• Adaptive Data Services to deliver relevant
data adapted to the business needs of all
projects
25