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Data Warehouse Architectures Research

  • Why Data Modeling Is Still Relevant

    Sponsored by: Quest

    There are those who think data modeling is passé or irrelevant. But data modeling offers a unique value not found in other techniques. Inside, learn what data modeling offers, how you can use it, and why it's still very much a relevant technique today.

  • The Enterprise-Wide Benefits of Real-Time Data

    Sponsored by: Tealium

    Organizations that support real-time customer data enable marketers and data analysts to work with the same source, closing enterprise-wide gaps in insights. Read on to learn more about real-time data enrichment tools.

  • Big Data Poses Weighty Challenges for Data Integration Best Practices

    Sponsored by: SearchDataManagement

    Organizations that deploy big data platforms are finding that they also need an upgraded data integration framework to meet rising demands. In this expert handbook, uncover advice on how to navigate the demands of big data architectures, high volume data warehousing, and data lakes by updating your data integration strategy.

  • Drive Deeper Insights across Data with SQL Server 2016

    Sponsored by: Microsoft

    Explore how SQL Server 2016 can help drive deeper insights into big data from both relational and non-relational sources, in the cloud and on-premise. Discover how SQL Server 2016 helps you store data in more formats at scale, open up data access, provide predictive analytics, and share insights on-premise, via the web, and through mobile devices.

  • Why Your Data Warehouse Needs a Data Lake and How to Make Them Work Together

    Sponsored by: Zaloni

    Complementing your DW with a data lake can be a smart first step. But before tackling a DW augmentation project, it's a good idea to review your current resources and understand what you'll need. Explore 2 reasons why companies augment their DW, tips for seamlessly integrating a data lake into a DW, and more.

  • An Analytical Approach to your Data Architecture

    Sponsored by: Zaloni

    With so much data available, managed data lakes are becoming crucial to the modern data architecture. Learn how they can give your enterprise the agility and scalability needed for timely discovery and valuable business insights from big data.

  • An Analytics Use Case for Unified Data

    Sponsored by: Zaloni

    In a time when the variety and volume of data is continuously growing, data lakes are an emerging and powerful architectural approach, providing much needed flexibility. Learn more about the differences between data lakes and EDWs, keys to data lake management, and common use cases.

  • Data Management Architectures for Advanced Business Use Cases

    Sponsored by: Zaloni

    Data lakes allow for all data to be stored regardless of source or format, including new types of social media and IoT data. Access this e-book for a comprehensive guide to data lakes and determine whether a data lake or EDW is right for your business.

  • Data Warehousing Is Improved By Integrating Big Data Capabilities

    Sponsored by: IBM

    Both big data and data warehousing systems experience increased performance when they work together. In this expert e-guide, uncover the importance of data warehouses' storage and organization abilities, and learn how they can be combined with the increased analytics power of big data systems to provide improved insights.

  • Improving Manufacturing with Advanced Data Analytics

    Sponsored by: Intel Corporation

    Increasing complexity in products, manufacturing processes, and the Internet of Things (IoT) means that massive amounts of data are being generated every day. Find out how to address 3 of the biggest challenges of data management with integrated analytics so you can reduce costs, improve process cycle time, and speed up time to market.

  • Integrated Analytics: Manage the Growing Diversity of Sales and Marketing Data Sources

    Sponsored by: Intel Corporation

    This white paper explores how to enable an integrated analytics approach by leveraging data lake models, visualizations, and self-service BI. Find out to simplify big data, overcome traditional data warehouse limitations, and increase efficiency with automation.

  • Database Explorations: Learn About Redis NoSQL Server Clusters

    Sponsored by: IBM

    This geek guide by web development expert Reuven Lerner highlights the factors that make Redis NoSQL server clusters worth considering. Explore the technical underpinnings that make Redis currently the most popular NoSQL technology.

  • An Integrated Approach: Analytics, Data Warehouses, Big Data and More

    Sponsored by: IBM

    Find out how you can leverage an integrated appliance to make data computing simpler. You'll discover how to bring analytics right into the database, add agility, and eliminate siloes. Additionally, you'll learn how to optimize the performance of data warehouse services for analytic apps, including those that require big data capabilities.

  • Hadoop Deployments: Maximizing Efficiency for Big Data Analytics

    Sponsored by: IBM

    Many businesses recognize Hadoop as a premier cloud app framework, but they still don't know how to deploy Hadoop properly or prepare for its pitfalls. In this e-guide, we identify and explore some of the biggest Hadoop challenges. Find out how to resolve these issues and unlock the benefits of big data analytics.

  • Maximizing Your Data Lake with a Cloud or Hybrid Approach

    Sponsored by: IBM

    Access this Aberdeen Research report to discover how a Hadoop-based data lake maintained in a cloud or hybrid environment can provide benefits such as greater access to data across the organization, enhanced accuracy in the analysis process, and more.

  • The Future of Data Warehousing Infrastructure and Architectures

    Sponsored by: IBM

    This e-guide examines how a shift from traditional data warehousing (DW) appliances to an infrastructure that combines traditional DWs with big data technologies offers new opportunities for businesses to address needs such as costs, flexibility and agility while eliminating key technology silos.

  • Augment and Extend the Life of Your Data Warehouse

    Sponsored by: IBM

    Learn how organizations currently using a high powered data warehouse appliance can augment and extend the life of their data warehouses with industry-leading cloud data services. Discover tips for "future-proofing" your data warehouse and more.

  • Cloud-based data warehousing as-a-service, built for analytics

    Sponsored by: IBM

    Discover how a cloud-based data warehouse service offers integrated analytics to rapidly deliver answers. IBM's dashDB offers unique in-database analytics, R predictive modeling, and business intelligence tools could free you to analyze your data and get precise insights, quicker.

  • 30-Day dashDB: Test Drive Data Warehousing and Analytics Functionalities

    Sponsored by: IBM

    A well-designed data warehouse system can help boost the corporate bottom line, but if you aren't careful, data warehouse performance can bog down in different places. Start a free 30-day trial to dashDB, a fully-managed cloud data warehouse built specifically for analytics. Take their data warehousing and analytics capabilities for a test drive.

  • Cloud-based data warehousing as-a-service, built for analytics

    Sponsored by: IBM

    Traditional data warehousing has long been a painstaking endeavor, but today you have other options. Read on to learn about a fully managed, cloud data warehouse solution and the benefits it could help you realize.

  • Preparing Your Data Architecture for Big Data Analytics

    Sponsored by: Hewlett-Packard Enterprise

    While big data can provide actionable insights to drive business value, your enterprise can't experience those benefits without the right data architecture in place first. In this e-guide, analytics expert Lyndsay Wise reveals the 4 crucial factors to keep in mind before embarking on a big data project.

  • TDWI Checklist Report: Data Warehousing in the Cloud

    Sponsored by: IBM

    Using a cloud-based data warehouse provides value. Uncover ways this approach to data warehousing can reduce startup costs, potentially eliminate ongoing maintenance costs, accelerate analysis, and more.

  • Enabling an Agile Data Warehouse with Automation

    Sponsored by: Attunity

    Watch this webinar to learn how to overcome the limitations of traditional data warehouses with modern technology, which can enable you to automate and accelerate routine IT tasks associated with designing, creating, populating, and managing data warehouses and data marts.

  • Making an Operational Data Store (ODS) the Center of Your Data Strategy

    Sponsored by: Attunity

    This white paper provides an overview of why organizations today need operational data stores, how to sell an ODS to 5 different stakeholder groups, and best practices for implementing an ODS.

  • Big Data Era: Embrace Speed & Simplicity in your Data Warehouse for a Competitive Edge

    Sponsored by: IBM

    This white paper provides insight into what modern data warehouses need to accomplish to be relevant. Learn how adapting old technologies to manage new challenges is inadequate, and how you can manage big data simply and at low cost, while simultaneously processing advanced analytics at the speed of business.

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