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The 5 Key Challenges to Building a Successful Data Science Lab & Data Team

This 21 page ebook will teach you how to address, avoid, and fix the main challenges that come up in Data Science Laboratory Environments.

The GridGain In-Memory Data Grid

This white paper provides an overview of in-memory computing technology with a focus on in-memory data grids. It discusses the advantages and uses of in-memory data grids and introduces the GridGain In-Memory Data Fabric. Finally, it presents a deep dive on the capabilities of the GridGain solution. To learn more download this white paper.

In-Memory Computing: Leading the Fast Data Revolution

In this white paper you will learn more about the aspects of in-memory computing in more detail and describe how the society of the future will depend on the capabilities it provides. Over the next few years, in-memory computing technology will be an integral part of changes that are dramatically transforming the world.

Architecting for Access: Simplifying Analytics on Big Data Infrastructure

Whether you’re upgrading your current solution or rolling out a brand new platform, planning and executing an analytics workload today requires answering many tough questions.
This eBook from O’Reilly shares:
• How to choose between a data lake or analysis on the fly
• Tips on finding front-end tools that delight users
• Evaluations of hundreds of permutations of technology stacks
• Advice on how to make data your endgame, not opinion

Predictive maintenance: How data can increase production

Predictive maintenance involves gathering targeted data for analysis, the results of which will help anticipate potential failures before they occur. Companies opt for this type of maintenance to avoid predictable incidents and repair equipment, assembly lines, or machinery with minimum impact on their operations. “Having to repair a faulty product is disastrous for a manufacturer’s brand image. But shutting down […]

Data Warehouses Ascend to the Cloud

Should the data warehouse be deployed on the cloud? IDC addresses this question on a regular basis. As adoption of cloud software increases, organizations of all sizes across industries and geographic regions are evaluating and assessing the opportunities and challenges of deploying software on the cloud. Data warehousing solutions are no exception to this trend.

Big Data, Big Innovations

With a traditional data warehouse powering big data, it’s not unusual for data loads and complex queries to run for days, which hinders the analytical process. Plus, these data warehouse environments are often designed to analyze structured data only, and not valuable unstructured data generated from new external sources such as social media and mobile computing.

Data Lake: The Definitive Guide

This paper provides the definitive guide on the critical areas of importance to bring data lake organization, governance, and security to the forefront of the conversation.

Data Lakes Principles and Economics

This Checklist Report discusses what your enterprise should consider before diving into a data lake project, no matter if it’s your first or second or even third major data lake project. Presumably, adherence to these principles will become second nature to the data lake team and they will even improve upon them at some point.

Research of Data Lakes

This study was designed to document key perceptions, challenges, and successes by focusing on data organization, integration, security, and definitional clarification to address key areas of concern and interest in ongoing data lake adoption. The intent of the survey and this corresponding report is to understand and share the current and planned adoption of technologies in the Hadoop ecosystem, intended specifically for a data lake strategy, and to learn how adopting companies are addressing critical data lake success factors, including rethinking data for the long-term, establishing governance first, and tackling security needs upfront. The survey and report also identify emergent areas of concern and new areas of clarification needed for data lake maturity.