DEVELOPMENT, DEPLOYMENT & DELIVERY

Languages and software environments for analytics development as well as analytics deployment and delivery models to help organizations achieve their goals.

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  • 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

  • Emerging Best Practices for Data Lakes

    It’s no surprise that data warehouse professionals are quickly adopting Hadoop. According to a recent TDWI survey, the number of deployed Hadoop clusters is up 60% over two years. While Hadoop is an effective design pattern for capturing and quickly ingesting a wide range of raw data types, there have been a number of challenges organizations have faced in realizing the true business value from their Hadoop-based data lakes. more

  • SQL for Hadoop: When to Use Which Approach

    In a 2015 survey by TDWI, 69% of respondents identified SQL on Hadoop as a must-have for making Hadoop ready for enterprise use. This is not surprising because both technical and business users know and love SQL, plus have portfolios of tools that rely on it. The catch is that early versions of Hadoop were devoid of ANSI-standard SQL. more

  • The Modern Data Warehouse: What Enterprises Must Have Today and What They’ll Need in the Future

    Many organizations need a more modern data warehouse platform to address a number of new and future business and technology requirements. Most of the new requirements relate to big data and advanced analytics, so the data warehouse of the future must support these in multiple ways, while still supporting older data types, technologies, and business practices. Hence, a leading goal of the modern data warehouse is to enable more and bigger data management solutions and analytic applications, which in turn help the organization automate more business processes, operate closer to real time, and through analytics learn valuable new facts about business operations, customers, products, and so on. more

  • Peering Under the Hood: Fine-Tuning Solutions for Hard Operational Data Governance

    Many organizations are responding to their raised awareness of the need for data governance by introducing data governance programs, hiring Chief Data Officers, and forming a data governance council. And while there are numerous guidelines and methods for the operating models for a data governance practice, recommendations regarding its day-to-day operationalization are much harder to come by. Specifically, how does an organization design an operational environment for instituting business data policies for usability and enforcing those policies consistently across the enterprise? Answering this question is necessary for achieving the data governance discipline without getting in the way of the business. more

  • Big Data Management Best Practices for Data Lakes

    Organizations are pursuing data lakes in a fury. Organizations in many industries are attempting to deploydata lakes for a variety of purposes, including the persistence of raw detailed source data, data landing and staging, continuous ingestion, archiving analytic data, broad exploration of data, data prep, the capture of big data, and the augmentation of data warehouse environments. These general design patterns are being applied to industry and departmental domain specific solutions, namely marketing data lakes, sales performance data lakes, healthcare data lakes, and financial fraud data lakes. more

  • The What, Why, When, and How of Data Warehouse Modernization

    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. In the age of big data analytics, it’s important to modernize a DW environment to keep it competitive and aligned with business goals. more

  • Combat Rising Integration Complexity with dPaaS

    Today's integration complexities are supersized. Businesses must contend with unprecedented volumes and varieties of data at a time of growing IT resource scarcity and aging integration software. Throw into the mix the high demands—and even higher expectations—placed on analytics as a way of driving business performance, and it's easy to see why many integration environments are overwhelmed and underperforming. more

  • Governing Big Data and Hadoop

    Big data presents significant business opportunities, when leveraged properly. And yet, big data also presents significant business and technology risks, when it is poorly governed or managed. more

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    • Accelerate TDWI Boston Accelerate TDWI Accelerate Boston

      April 3-5, 2017
      REGISTRATION CLOSES MAR 31

      ACCELERATE brings together the brightest minds in data to share their expertise and insight on the future of data science and analytics. From sessions on core data science skills, to learning how to use new big data tools such as R, Python, and Spark, to talks on the latest trends in machine learning, predictive analytics and artificial intelligence, attendees will learn from industry experts, receive valuable training, and network and share ideas with their data peers in an exciting and collaborative environment.

    • Conference TDWI Chicago Conference

      May 7-12, 2017
      EARLY BIRD DEADLINE APR 7

      TDWI Chicago addresses our greatest data challenges head-on: Data streaming, enriching your data lake with new information sources, and connecting to spectrum of IoT. You will leave TDWI Chicago’s 6-day in-depth conference with the skills and insights to design, build and analyze your organization’s data.