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    Building Big Data Analytical Applications at Scale Using Existing ETL Skillsets

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    Delivering Operational Analytics using Spark and NoSQL Data Stores

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    Integrating Big Data, Hadoop, NoSQL and Spark in the Analytics Environment

    28-29 June, 2018 – Amsterdam, The Netherlands  

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    Organising the Data Lake – the Central Role of an Information Catalog

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    Smart Infrastructure and Smart Applications for the Smart Business – Infrastructure and Application Performance Monitoring

    Technology Brief
    Many companies now have a very complex landscape of IT hardware and software infrastructure spread across multiple geographies, applications and data. In addition their application portfolio is now increasingly likely to be running across both on-premises and cloud platforms. This can include multiple instances of the same application in some cases with each instance of an application subject to customisation. This makes, maintenance and consolidation of applications very challenging especially if there is pressure to reduce operational cost. It also means that resources needed to diagnose and isolate performance problems can be significant. Furthermore, IT is also obligated do everything possible to improve user experience especially now that many applications can be accessed from mobile devices.
    For more information please read Mike Ferguson's Analyst Brief.
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    Streaming Analytics & Embedded BI – the Route to Always On Smart Business Operations

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    The Challenge of Big Data

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    The Enterprise Data Refinery

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    The Hadoop Data Refinery & Enterprise Data Hub

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    Offloading & Accelerating Data Warehouse ETL Processing Using Hadoop

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    The Impact of Big Data on ETL

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    Architecting a Big Data Platform for Analytics

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    Big Data – Why Transaction Data is Mission Critical to Success

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    Enterprise Information Protection – The Impact of Big Data

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    Transforming Big Data into Insights in an IBM System z Environment

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