Premium Content

searchDataManagement E-Handbooks

Featured E-Handbooks

  • Determine if NoSQL databases are right for your organization

    NoSQL databases offer more flexible alternatives to mainstream relational software, particularly for big data applications. But NoSQL offerings include a diverse set of technologies that can present prospective users with a bewildering array of choices. And those technologies have yet to secure a place in many organizations. In fact, in a survey of IT and business professionals conducted by The Data Warehousing Institute in November 2013, 65% of the respondents said they had no plans to incorporate NoSQL databases into their data warehouse architectures. Don't let that scare you off, though: There are companies successfully putting NoSQL products to work in applications they're suited for.

    In this three-part guide, readers will learn about the different types of NoSQL technologies and their potential uses. First, get details about the four primary NoSQL product categories, with deployment examples from experienced users and advice on how to avoid going down the wrong database path. Next, read about why it's a mistake to force-fit technologies into IT environments -- and why Gartner analyst Merv Adrian says it's a fruitless exercise to compare NoSQL offerings "that are so wildly different in structure and intent." And in our third story, find out why many organizations are creating what consultancy Enterprise Management Associates calls a hybrid data ecosystem -- a blend of old and new technologies, including NoSQL systems -- to support their big data environments.

    Download Now

  • Build today for tomorrow's big data architectures

    Developing a big data architecture today involves pulling together a lot of different technology pieces. The prevalence of unstructured data has forced the data warehouse as we knew it into a marriage of newer technologies -- Hadoop and NoSQL databases among them -- and older ones, such as columnar databases and in-memory processing tools.

    This three-part guide offers IT managers, enterprise architects, data management teams and other readers insight on architectural strategies and advice on how to manage the architecture design and technology evaluation processes in today's big data environment. First, Jack Vaughan takes a look at one of the technology options in big data architectures: cloud computing. Vaughan uses one supermarket cooperative's success story to illustrate the potential benefits of a cloud-based big data platform. Next, Rick van der Lans digs deep into the steps required in designing, implementing and managing a big data architecture. While many IT professionals fear the death of the traditional data warehouse, van der Lans believes there is still a place for it in the world of big data. To close, Colin White and Claudia Imhoff identify the three components necessary for enterprise data warehouses to support next-generation analytics: an investigative computing platform, a data refinery and real-time analytics capabilities.

    Download Now

Other E-Handbooks available for free to our members

    • Page 1 of 1
      • Big data and data warehousing: Where's the relationship headed?

        Spearheaded by big Internet companies such as Google and Amazon.com, big data technology has started to catch fire with large rank-and-file organizations in every industry. In most cases, it plays a complementary role to established data warehouses, at least for now. In short, big data opens up new ways to benefit from and monetize valuable corporate data assets.

        This handbook, by business intelligence consultant Wayne Eckerson, explains the dimensions of the big data market today. It starts by looking at the evolving market for big data software and ends by describing the new analytical ecosystem that big data has created. Along the way, it explores the pros and cons and provides recommendations about how to position and select data management products.

        View E-Handbook
      • In-memory databases: The golden ticket to deeper analyses?

        Historically, in-memory databases have been seen as a niche technology. As in-memory processing becomes less expensive and more mainstream, however, potential uses are expanding. And, with the promise of deeper data analysis -- and, better business benefits -- IT professionals' ears are pricking up. Still, for many organizations it's going to be a matter of if, and not when, to proceed with implementation.

        To that end, SearchDataManagement editors have compiled a three-part guide to in-memory database trends, serving up expert advice on evaluating, deploying and managing the technology. Readers can expect an in-depth look at whether in-memory appliances such as SAP's HANA and Oracle's Exalytics devices are changing the nature of what in-memory database technology can be used for. Next, News and Site Editor Jack Vaughan offers a rundown of the kinds of applications that are a good fit for in-memory databases, with tips on how to decide if the technology is right for your organization. We close with key insight into the capabilities and potential uses of new in-memory options being released for mainstream relational databases.

        View E-Handbook
      • How to be successful with enterprise data governance

        Successful data governance programs can help ensure that organizations have consistent policies and processes for defining, managing and using corporate data. But many data governance efforts miss the mark and fall short of those goals. This three-part guide, written for IT and data management professionals as well as business executives, will provide readers with tips and advice on how to avoid the missteps that can lead to enterprise data governance failures. Learn about data governance best practices and not-so-best practices, and get real-world guidance from organizations that are succeeding with data governance.

        View E-Handbook
      • Taking stock of Hadoop 2

        The release of Hadoop 2 seems to be all that prospective users and industry analysts can talk about. If you think all that chatter has translated into widespread adoption, you're wrong. The release -- a possible answer to calls for a "hardened" Hadoop -- adds features that enable users to expand the open source distributed processing framework beyond its original core configuration of MapReduce and the Hadoop Distributed File System.

        In this handbook, IT managers, data management staffers and other readers will benefit from in-depth analysis of the new capabilities, potential uses and limitations of Hadoop 2 and related technologies. For readers still unsure about what to do with the open source software, this handbook offers insight into Hadoop 2 -- complete with a close look at YARN, Hadoop's redesigned resource manager.

        View E-Handbook
      • Putting a data stewardship and data governance plan into play

        Confused about the data stewardship processes for big data, cloud computing and other IT trends? Don’t be. In this handbook is valuable information on the pivotal role technology plays in governance initiatives -- as well as other useful tips for data management professionals and business executives.

        View E-Handbook
      • Strategies for kick-starting your data quality program

        In this handbook, readers will find quality advice on balancing manual and automated data quality processes as part of quality improvement efforts, as well as tips on building a business case for purchases of data quality tools. Readers can look forward to insider guidance on combining data quality improvement initiatives with data governance and master data management programs.

        View E-Handbook
      Page 1 of 1
    • Page 1 of 1
      • Taking stock of Hadoop 2

        The release of Hadoop 2 seems to be all that prospective users and industry analysts can talk about. If you think all that chatter has translated into widespread adoption, you're wrong. The release -- a possible answer to calls for a "hardened" Hadoop -- adds features that enable users to expand the open source distributed processing framework beyond its original core configuration of MapReduce and the Hadoop Distributed File System.

        In this handbook, IT managers, data management staffers and other readers will benefit from in-depth analysis of the new capabilities, potential uses and limitations of Hadoop 2 and related technologies. For readers still unsure about what to do with the open source software, this handbook offers insight into Hadoop 2 -- complete with a close look at YARN, Hadoop's redesigned resource manager.

        View E-Handbook
      •  
      Page 1 of 1
    • Page 1 of 1
      • Build today for tomorrow's big data architectures

        Developing a big data architecture today involves pulling together a lot of different technology pieces. The prevalence of unstructured data has forced the data warehouse as we knew it into a marriage of newer technologies -- Hadoop and NoSQL databases among them -- and older ones, such as columnar databases and in-memory processing tools.

        This three-part guide offers IT managers, enterprise architects, data management teams and other readers insight on architectural strategies and advice on how to manage the architecture design and technology evaluation processes in today's big data environment. First, Jack Vaughan takes a look at one of the technology options in big data architectures: cloud computing. Vaughan uses one supermarket cooperative's success story to illustrate the potential benefits of a cloud-based big data platform. Next, Rick van der Lans digs deep into the steps required in designing, implementing and managing a big data architecture. While many IT professionals fear the death of the traditional data warehouse, van der Lans believes there is still a place for it in the world of big data. To close, Colin White and Claudia Imhoff identify the three components necessary for enterprise data warehouses to support next-generation analytics: an investigative computing platform, a data refinery and real-time analytics capabilities.

        View E-Handbook
      • Big data and data warehousing: Where's the relationship headed?

        Spearheaded by big Internet companies such as Google and Amazon.com, big data technology has started to catch fire with large rank-and-file organizations in every industry. In most cases, it plays a complementary role to established data warehouses, at least for now. In short, big data opens up new ways to benefit from and monetize valuable corporate data assets.

        This handbook, by business intelligence consultant Wayne Eckerson, explains the dimensions of the big data market today. It starts by looking at the evolving market for big data software and ends by describing the new analytical ecosystem that big data has created. Along the way, it explores the pros and cons and provides recommendations about how to position and select data management products.

        View E-Handbook
      Page 1 of 1
    • Page 1 of 1
      • Putting a data stewardship and data governance plan into play

        Confused about the data stewardship processes for big data, cloud computing and other IT trends? Don’t be. In this handbook is valuable information on the pivotal role technology plays in governance initiatives -- as well as other useful tips for data management professionals and business executives.

        View E-Handbook
      •  
      Page 1 of 1
    • Page 1 of 1
      • How to be successful with enterprise data governance

        Successful data governance programs can help ensure that organizations have consistent policies and processes for defining, managing and using corporate data. But many data governance efforts miss the mark and fall short of those goals. This three-part guide, written for IT and data management professionals as well as business executives, will provide readers with tips and advice on how to avoid the missteps that can lead to enterprise data governance failures. Learn about data governance best practices and not-so-best practices, and get real-world guidance from organizations that are succeeding with data governance.

        View E-Handbook
      •  
      Page 1 of 1
    • Page 1 of 1
      • Strategies for kick-starting your data quality program

        In this handbook, readers will find quality advice on balancing manual and automated data quality processes as part of quality improvement efforts, as well as tips on building a business case for purchases of data quality tools. Readers can look forward to insider guidance on combining data quality improvement initiatives with data governance and master data management programs.

        View E-Handbook
      •  
      Page 1 of 1
    • Page 1 of 1
      • Determine if NoSQL databases are right for your organization

        NoSQL databases offer more flexible alternatives to mainstream relational software, particularly for big data applications. But NoSQL offerings include a diverse set of technologies that can present prospective users with a bewildering array of choices. And those technologies have yet to secure a place in many organizations. In fact, in a survey of IT and business professionals conducted by The Data Warehousing Institute in November 2013, 65% of the respondents said they had no plans to incorporate NoSQL databases into their data warehouse architectures. Don't let that scare you off, though: There are companies successfully putting NoSQL products to work in applications they're suited for.

        In this three-part guide, readers will learn about the different types of NoSQL technologies and their potential uses. First, get details about the four primary NoSQL product categories, with deployment examples from experienced users and advice on how to avoid going down the wrong database path. Next, read about why it's a mistake to force-fit technologies into IT environments -- and why Gartner analyst Merv Adrian says it's a fruitless exercise to compare NoSQL offerings "that are so wildly different in structure and intent." And in our third story, find out why many organizations are creating what consultancy Enterprise Management Associates calls a hybrid data ecosystem -- a blend of old and new technologies, including NoSQL systems -- to support their big data environments.

        View E-Handbook
      • In-memory databases: The golden ticket to deeper analyses?

        Historically, in-memory databases have been seen as a niche technology. As in-memory processing becomes less expensive and more mainstream, however, potential uses are expanding. And, with the promise of deeper data analysis -- and, better business benefits -- IT professionals' ears are pricking up. Still, for many organizations it's going to be a matter of if, and not when, to proceed with implementation.

        To that end, SearchDataManagement editors have compiled a three-part guide to in-memory database trends, serving up expert advice on evaluating, deploying and managing the technology. Readers can expect an in-depth look at whether in-memory appliances such as SAP's HANA and Oracle's Exalytics devices are changing the nature of what in-memory database technology can be used for. Next, News and Site Editor Jack Vaughan offers a rundown of the kinds of applications that are a good fit for in-memory databases, with tips on how to decide if the technology is right for your organization. We close with key insight into the capabilities and potential uses of new in-memory options being released for mainstream relational databases.

        View E-Handbook
      Page 1 of 1