DEVELOPMENT, DEPLOYMENT & DELIVERY

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  • IoT’s Impact on Data Warehousing: Defining IoT in Terms of Its Data Requirements

    The Internet of Things (IoT) is a computing paradigm where a widening range of physical devices—including smartphones, vehicles, shipping pallets, kitchen appliances, manufacturing robots, and anything fitted with a sensor—can transmit data about their location, state, activity, and surroundings. Depending on the device type, some may also receive data and instructions that control device behavior. more

  • Making Multiplatform Data Architectures Work for You: Common Use Cases and Reference Architectures

    To leverage the new wave of advanced data sources available, users and architects are turning to a multiplatform data architecture (MDA), where numerous diverse data platforms and tools are integrated in a multiplatform, distributed architecture. An MDA is typified by an extreme diversity of platform types that may include multiple brands of relational databases, NoSQL platforms, in-memory functions, and tools for data integration, analytics, and stream processing. Any of these may be on premises, in the cloud, or in hybrid combinations of the two. more

  • Defining a Multiplatform Data Architecture and What It Means to You

    A revolution is occurring in modern analytics, driven by our ability to capture new sources of information at a detail previously too complex and costly to imagine. As more data comes from new sources (from machines to social media) and is applied to new applications, data is evolving into greater diversity, including every variation of data type from unstructured to multistructured. Even as new tools to analyze and manipulate this newly available resource come online, it is not enough to look at the data manipulation layer alone. more

  • Ask the Expert: Ask the Expert on Data Maturity
    TDWI Members Only

    An increase in data maturity correlates to an increase in business success. Yet though organizations gladly allocate budget to business projects, they neglect data maturity—even to the point of allowing it to deteriorate. more

  • Architecting a Hybrid Data Ecosystem: Achieving Technical Cohesion and Business Value in a Multi-platform Environment

    One of the strongest trends in data management today and into the future is the development of complex, multi-platform architectures that generate and integrate an eclectic mix of old and new data, in every structure imaginable, traveling in time frames from batch to real time. The data comes from legacy, mainstream enterprise, Web, and third-party systems, which may be home grown, vendor built, open source, or a mix of these. More sources are coming online from machines, social media, and the Internet of Things. These data environments are hybrid and diverse in the extreme, hence the name hybrid data ecosystems (HDEs). more

  • Ask the Expert: Should You Learn MapReduce or Spark?
    TDWI Members Only

    Want to become a data engineer but aren’t sure which technologies are the right fit for the job? People switching into big data are faced with a difficult decision—should you learn MapReduce or Spark? The answer seems simple, but requires more information and insight. Answering this and other questions correctly places you on the path to becoming a data engineer. more

  • Ask the Expert: Organizational Risk Factors for Achieving Data-Driven Success
    TDWI Members Only

    Modern organizations spend significant time and effort working to transform their operations into a data-driven culture enabled by big data and analytics. The true value offered from analytics should be measured by observed upticks in targeted and desired business outcomes. more

  • Data Management for Big Data, Hadoop, and Data Lakes

    A perfect storm of data management trends is converging. First, organizations across many industries are experiencing the big data phenomenon, which forces them to capture and leverage data from new sources, in structures and velocities that are new to them, in unprecedented volumes. Second, technical users are scrambling to learn new data platforms like Hadoop and their evolving best practices. Third, the data lake arose suddenly in 2016 as the preferred approach to managing very large repositories of raw source data. Fourth, business managers have attained a new level of sophistication in their use big data for business value and organizational advantage. more

  • Between a Rock and a Hard Place: How to Modernize Legacy Middleware for an Evolving, Data-driven World

    In support of daily operations, many organizations depend heavily on systems for enterprise application integration (EAI), enterprise service bus (ESB), and other approaches to middleware. Yet, these infrastructures are today legacy technologies that predate the rise of big data and unstructured data, as well as modern sources and targets for integration, such machines, devices, clouds, social media, and the Internet of Things (IoT). Furthermore, many middleware vendor tools are still optimized for the on-premises ERP-dominated applications world of twenty years ago; others are in legacy mode, with no future upgrades coming. more

  • Ask the Expert about Data Warehouse Modernization
    TDWI Members Only

    Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever, as they support operationalized analytics and wring business value from machine data and other new forms of big data. Hence, it’s important to modernize an existing DW environment, to keep it competitive and aligned with business goals, as well as to grow into new data-driven practices and technologies, while also keeping and improving the old ones. more

  • Maximizing the Value of Your IoT Data: How to Utilize Data Virtualization to Provide Value and Context to Your Sensor Data

    Sensor data from Internet of Things (IoT) devices is becoming more pervasive throughout the world of data management, but it can be both an opportunity and a challenge to existing platforms, integration, and best practices. Your organization needs to understand how its existing integration and data management tools can help with the introduction of sensor data, as well as how business stakeholders, in particular from operations teams, will be using that data to impact revenue and costs. In addition, your organization must enable the speed of performance required in operational and analytics use cases, including productivity to improve organizational performance, process efficiency to streamline company activities, new product development to better meet customer expectations and experiences, new business models for revenue generation and supply chain monitoring, and inventory and cost reduction. more

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    • Conference TDWI Anaheim Conference

      August 6-11, 2017
      Registration closes August 4

      All of the data in the world is useless unless you can effectively analyze and present the data to tell a story about what has happened, and what is likely to take place in the future. TDWI Anaheim brings together the experts in the big data and analysis space to share their insights about data analysis, visualization and storytelling in a proven learning environment.

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    • Leadership Summit TDWI Anaheim Leadership Summit

      August 7-8, 2017
      Registration closes August 4

      An interactive summit for business, IT, and analytics leaders who select and implement emerging technologies to solve new challenges and align with new business opportunities. Register and participate in intimate sessions, network with your peers and learn best practices from the leaders of the data revolution.