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  • Which Data Integration Solution Is Best for You? white paper Gartner

    Download the 2018 Gartner Magic Quadrant for Data Integration Tools Report to find out! more

  • How to Migrate Your Data to the Cloud DataFactZ white paper cover image

    Companies, engineers, architects, developers, and users across the world are embracing data on the cloud, because of its cost-effectiveness and the performance boost it provides. Throughout the migration of data from on-premises to the cloud, one can expect challenges, surprises, but also great rewards. An organization should strategize such moves and be prepared with a list of all actionable items before attempting to migrate any data. more

  • Implementing Cloud Migration of a BI Tool DataFactZ white paper cover image

    Organizations are always looking to improve their operations with the most technologically advanced, yet most cost-effective solutions. This applies to the area of business intelligence and reporting as well. It is quickly becoming a norm for companies to migrate their data to the cloud, and we can expect a similar trend for companies to use those cloud services for their BI and reporting needs. DataFactZ had the opportunity to oversee this type of implementation for one of the Midwest’s biggest food distribution companies. more

  • Ovum Enterprise Case Study: Building Flexible and Scalable Data Platforms Cloudera white paper cover image

    To further drive the IoT insurance market and build on its market position, Octo Telematics needed to develop an IoT and telematics platform with the functionality, flexibility, and scale to support the next evolution of IoT-based insurance propositions. more

  • Move Your Big Data Into The Public Cloud Cloudera white paper cover image

    Read this report to understand the speed at which the public cloud will unseat on-premises big data technology and solutions. This report will also help accelerate your plans, explain your options, and provide a four- step method for evolving your big data road map. more

  • Looking Before You Leap Into the Cloud Cloudera white paper cover image

    Learn EMA's best practices for driving toward analytic initiatives in the cloud and the key to cloud success. more

  • A Modern Data Platform for the Cloud Cloudera Modern Data Platform cover

    Download this white paper to explore common application patterns companies are employing to get more value from their data in the cloud. more

  • TDWI Checklist Report | Modernizing Data Warehouse Infrastructure Cloudera Checklist cover image

    Users ignore the modernization of deep warehouse infrastructure at their peril. Without it, they may achieve complete, clean, and beautifully modeled data, but without the ability to scale to big data, iterate data models on the fly, enable flexible self-service access, operate continuously and in real-time (as warehouses must in global businesses), and handle new data types and workflows for advanced analytics. more

  • 2018 TDWI Salary, Roles, and Responsibilities Report 2018 TDWI BI and Analytics Salary Survey cover image

      This past year saw BI salaries continue their steady rise. Read more in the 2018 TDWI Salary, Roles, and Responsibilities Report. more

  • Data Warehouse Automation Tools: Product Categories and Positioning

    Data warehouse automation (DWA) tools eliminate the manual effort required to design, deploy, and operate a data warehouse. This turns data warehouse development from a laborious, time-consuming exercise into an agile one. This white paper describes how there are many other factors that customers should consider before purchasing a DWA product. more

  • The Changing World of Data Warehousing: Data Integration's Central Role in Transforming Information into an Enterprise Asset

    When enterprise information flows quickly to different consumers within an organiza­tion, the business ecosystem thrives. This white paper describes how organizations must take a closer look at data integration and create channels for information to flow quickly to business decision makers. more



  • Multiplatform Data Architectures

    A multiplatform data architecture (MDA) contains data distributed across multiple databases, open source or big data platforms, file systems, clouds, and other data platforms. An MDA is characterized by its large number and diversity of data persistence platforms, as well as its broad range of data structures, types, and containers. Equally important, however, is the MDA’s substantial data management infrastructure, which unifies the MDA’s architecture by integrating, synchronizing, cleansing, mastering, and documenting data across the MDA’s many platforms and beyond. more

  • Modern Metadata Management: Boosting BI capacity with automation and machine learning

    Metadata management continues to be a powerful enabler for mission-critical data-driven business activities, including operations, analytics, and compliance. That’s because metadata is the golden thread that stitches together enterprise-wide landscapes, even those that are heavily distributed and heterogeneous, with hybrid mixes of on-premises and cloud systems. more

  • New Practices in Data Cataloging

    Are you tired of starting with a blank slate every time you begin a new analytics assignment? That’s what happens when you spend your precious time researching the same data sources as last time and assembling yet another aggregated data set prior to doing what you really need to do. Creating a new analysis can have a positive business impact on your enterprise. more

  • Getting Started with Data Integration in the Cloud

    Cloud continues to rise in importance as a platform for many IT systems, including those for data integration. Many organizations have now achieved a maturity level where they are using multiple cloud-based applications and online data sources. These users now need data integration tool platforms that support hybrid data environments so they can unify on-premises and cloud-based data sources and targets. Similarly, users increasingly need data integration processing to run natively on clouds (not just on premises), so that data integration functions and related capabilities are closer to software-as-a-service (SaaS) applications, Web data sources, multiple clouds, and increasingly popular cloud-based databases, data lakes, and data warehouses. more

  • How to Design a Data Lake with Business Impact in Mind

    A quarter of organizations surveyed by TDWI in 2017 say they already have a data lake in production, while another quarter say their lake will be in production within 12 months. Although data lakes are still rather new, user organizations have adopted them briskly. Why has the data lake gotten so popular, so fast? more

  • Data Architecture for IoT Communications and Analytics

    The Internet of Things (IoT) is an architectural paradigm combining an exploding number of different types of connected sensors and devices continuously generating and broadcasting data. The data can be processed to create integrated analytics models that can enhance and optimize new business initiatives. more

  • Modernizing Data Analytics: Moving Beyond Hadoop

    As an open source platform that simplified the ability to develop distributed and parallel applications, Hadoop lowered the barrier to entry for many smaller organizations interested in big data analytics. Some people have gone as far to suggest that Hadoop be used to replace their existing data warehouse. more

  • The Automation and Optimization of Advanced Analytics based on Machine Learning

    However, embracing machine learning successfully is challenged by ML’s serious data requirements. In development, designing an analytic model depends on very large volumes of diverse data. In production, an analytic model created via machine learning again needs voluminous data, so it can learn and improve over time. In turn, managing big data for machine learning demands a substantial data management infrastructure and tool portfolio. more

  • Ask the Expert: Demystifying Semantics and Ontologies
    TDWI Members Only

    We hear more and more about semantics these days, but what does it mean? What is an ontology and how does it relate to a data model? Do semantics and ontologies have a role to play in data architecture and data modeling? more

  • Six Strategies for Balancing Risk with Data Value

    Managing data for value is a business-oriented focus on the potential of data. It complements the all-too-common obsession with data’s technical requirements. Data value recognizes that data is a valuable business asset and should be leveraged accordingly. If you are managing data for value, your asset portfolio of data should be protected, grown, and governed. more

  • Ask the Expert about the Role of Data Visualization on Data Validation
    TDWI Members Only

    Data visualization has become a standard part of the business intelligence fair. It is now expected that a business intelligence team include a rich set of graphics in the tooling used across the business. In the real-time world, we are faced with the challenge of handling data streams directly from operational tools. This real-time data when presented visually tend to immediately skew to highlight outliers and exceptions in the data. more

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    Upcoming TDWI Events

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    • Conference - TDWI Orlando Conference

      November 11-16, 2018
      Save 20% through September 14

      Look into the future of big data and analytics and end the year strong with TDWI Orlando. An annual fan-favorite, this event presents big data trends, data management at scale, and predictive analytics education so you can implement what’s new and what’s next in the industry.

    • Leadership Summit TDWI Orlando Leadership Summit TDWI Leadership Summit Orlando

      November 12-13, 2018
      Save 20% through September 14

      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 engaging sessions, network with your peers and learn best practices from the leaders of the data revolution.