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  • 1
    Online Resource
    Online Resource
    Sebastopol, CA : O'Reilly Media
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Electronic data processing ; Distributed processing ; Data mining ; Computing platforms ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Abstract: Companies are collecting more data than ever. But, given how difficult it is to unify the many internal and external data streams they've built, more data doesn't necessarily translate into better analytics. The real challenge is to provide deep and broad access to "a single source of truth" in their data that the typically slow ETL process for data warehousing cannot achieve. More than just fast access, analysts need the ability to explore data at a granular level. In this O'Reilly report, author Courtney Webster presents a roadmap to data centralization that will help your organization make data accessible, flexible, and actionable. Building a genuine data-driven culture depends on your company's ability to quickly act upon new findings. This report explains how. Identify stakeholders: build a culture of trust and awareness among decision makers, data analysts, and quality management Create a data plan: define your needs, specify your metrics, identify data sources, and standardize metric definitions Centralize the data: evaluate each data source for existing common fields and, if you can, minor variances, and standardize data references Find the right tool(s) for the job: choose from legacy architecture tools, managed and cloud-only services, and data visualization or data exploration platforms Courtney Webster is a reformed chemist in the Washington, D.C. metro area. She spent a few years after grad school programming robots to do chemistry and is now managing web and mobile applications for clinical research trials.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 18, 2019)
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  • 2
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Application software ; Development ; Business ; Data processing ; Data mining ; Business planning ; Electronic books ; Electronic books ; local
    Abstract: To satisfy end users who want easily accessible answers, many software vendors are looking to add analytics and reporting capabilities to their applications. Embedding analytics into applications can lead to wider adoption and product use, improved user experience, and differentiated products, but embedding analytics can also come with challenges and complexities. In this report, author Courtney Webster reviews several approaches and methods for embedding analytics capabilities into your applications. Should you implement a separate reporting portal, an in-application reporting tab, or go all in with a fully embedded in-page analytics solution? And do you build your own or buy a solution out of the box? To help you choose the right embedded analytics tool, Webster examines seven challenges-from customization, usability, and capabilities to scalability, performance, and data structure support-and presents best practice solutions for each.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 13, 2018)
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  • 3
    Online Resource
    Online Resource
    Sebastopol, CA : O'Reilly Media
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Apache Hadoop ; Electronic data processing ; Distributed processing ; Cloud computing ; Computer networks ; Management ; Electronic books ; Electronic books ; local
    Abstract: Hadoop was built to use local data storage on a dedicated group of commodity hardware, but many organizations are choosing to save money (and operational headaches) by running Hadoop in the cloud. This O'Reilly report focuses on the benefits of deploying Hadoop to a private cloud environment, and provides an overview of best practices to maximize performance. Private clouds provide lower capital expenses than on-site clusters and offer lower operating expenses than public cloud deployment. Author Courtney Webster shows you what's involved in Hadoop virtualization, and how you can efficiently plan a private cloud deployment. Topics include: How Hadoop virtualization offers scalable capability for future growth and minimal downtime Why a private cloud offers unique benefits with comparable (and even improved) performance How you can literally set up Hadoop in a private cloud in minutes How aggregation can be used on top of (or instead of) virtualization Which resources and practices are best for a private cloud deployment How cloud-based management tools lower the complexity of initial configuration and maintenance
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed December 6, 2018)
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  • 4
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Apache Hadoop ; Electronic data processing ; Distributed processing ; Cloud computing ; Computer networks ; Management ; Electronic books ; Electronic books ; local
    Abstract: The wish lists of many data-driven organizations seem reasonable enough. They'd like to capitalize on real-time data analysis, move beyond batch processing for time-critical insights, allow multiple users to share cluster resources, and provide predictable service levels. However, fundamental performance limitations of complex distributed systems such as Hadoop prevent much of this from happening. In this report, Courtney Webster examines the root cause of these performance problems and explains why best practices for mitigating them-cluster tuning, provisioning, and even cluster isolation for mission critical jobs-don't provide viable, scalable, or long-term solutions. Organizations have been pushing Hadoop and other distributed systems to their performance breaking points as they seek to use clusters as shared resources across multiple business units and individual users. Once they hit this performance wall, companies will find it difficult to deliver on the big data promise at scale. Read this report to find out what the implications are for your organization.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 11, 2018)
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