Sector Roadmap: Cloud Analytic Databases 2017

9 Cloud analytic database solutions were evaluated over six Disruption Vectors: Robustness of SQL, Built-in optimization, On-the-fly elasticity, Dynamic Environment Adaption, Separation of compute from storage, and Support for diverse data.

Key findings in our analysis include:

  • Due to the economics and functionality, use of the cloud can now be a given in most database selection in 2017 and beyond.
  • Several offerings have been able to leapfrog databases with much more history by being “born in the cloud” and tightly integrating with it through On-the-fly elasticity, Dynamic Environment Adaption, and Separation of compute from storage.
  • While traditional database functionality is still required, cloud dynamics are causing the need for more Robustness of SQL, Support for diverse data and other capabilities that may not be present in traditional databases.


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A Great Use of the Cloud

Recent trends in information management see companies shifting their focus to, or entertaining a notion for the first time of a cloud-based solution. In the past, the only clear choice for most organizations has been on-premises data—oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all or some of a company’s analytical needs.

This paper, written by McKnight Consulting analysts William McKnight and Jake Dolezal, describes two organizations with mature enterprise data warehouse capabilities, that have pivoted components of their architecture to accommodate the cloud.

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Database in the Cloud Benchmark

Read this cloud analysis paper for a benchmark of HPE Vertica and Amazon’s Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures.

Big data analytics platforms load, store, and analyze volumes of data at high speed, providing insight. This data is structured, semi-structured, or unstructured from a variety of sources. Data-driven organizations are leveraging this data analysis to market new promotions, for operational efficiency, and to evaluate risk and detect fraud.

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Data Integration Benchmark 2

The study evaluated RedPoint Data Management™ as part of its ongoing research into different approaches and architectures for Hadoop data integration.

The report contains:

  • Which technology approach completed the workload 1,900 percent faster.
  • Which technology approach excelled at managing high-volume data in demanding customer data applications.
  • What the primary performance advantages are of an architecture built on YARN.

Download this informative report today!

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Selecting a Platform for Big Data

Data leadership is a solid business strategy today and many companies have made strides towards adopting Hadoop, yet have trepidation in making the final leap. This report addresses considerations in adopting Hadoop, classifies the Hadoop ecosystem vendors, and provides selection criteria for elements of the Hadoop cluster.

This report cuts out all the non-value-added noise about Hadoop and presents a minimum viable product (MVP) for building a Hadoop cluster for the enterprise that is both cost-effective and scalable.

This approach encapsulates broad enterprise knowledge and foresight borne of numerous Hadoop lifecycles through production and iterations. It gets the Hadoop cluster up and running fast and will ensure that it is scalable to the enterprise’s needs.

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Ten Mistakes to Avoid in NoSQL

NoSQL is a perfect storm of need and solution for many, but it comes with challenges. When reviewing successes and failures, it is clear that some good practices have been overlooked or ignored in the areas of product selection, project formation, cluster setup, and project design.

In this Ten Mistakes to Avoid, we identify the mistakes with the biggest impact on NoSQL implementation success and recommend solutions you can apply to your implementations.

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EBook: Information: The Next Natural Resource

I’ve spent my career looking at how large quantities of complex information affects every part of our lives and this is the most exciting time to be doing that. Information affects finances. Information affects your health. It affects the life choices presented to you. It cannot be overstated how important the accumulation of enormous sums of detailed data about all of us and every aspect of business is.

This ebook looks at the rise of machine data, the future sources of digital data and the technologies for enterprises to deploy now to be ready for the data economy.

Hadoop Integration Benchmark

With its support for batch and real-time data processing, Spark is one of the most exciting new tools in the big data space. And data integration is still the key “ingredient” that brings a variety of sources together, including real-time big data.

A new report by MCG Global Services evaluates leading integration vendors, and benchmarks their performance against two key criteria:

  • The depth by which a tool leverages Hadoop
  • The performance of integration jobs

The report notes that these key differentiators “could spell the difference for a “just-in-time” answer to a business question and a “too-little-too-late” result.”

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Pushing Big Data to the Executive Edge

Fully utilized big data can give organizations a competitive advantage if they can get it to decision makers, or what we call the “executive edge.” But getting big data to that demographic requires the business intelligence (BI) community to help executives overcome their big-data dissonance.

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Embracing Analytics as a Competitive Strategy for a Midmarket Organization

Most organizations, regardless of size, struggle with how to best utilize and manage their important asset of data. However, a midmarket company can turn their data challenges into assets by matching the right technology and sidestep many of the information technology growing pains other companies have faced in years past. This white paper will discuss these challenges and suggest some strategies and solutions to turn midmarket data into a working asset.

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