This guide is designed to help your organization find the common ground needed to empower your Data Science and IT Teams to work together for the benefit of your data projects as a whole.
This 21 page ebook will teach you how to address, avoid, and fix the main challenges that come up in Data Science Laboratory Environments.
Download our free white paper to get a global understanding of how analytics are shaking up marketing and what you can do about that!
Going from small to big data requires understanding the new mindset. In 9 steps, this guide book can help you with that.
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.
By 2020, Gartner expects the IoT to have over 20 billion connected things. With that many connected devices transmitting information, there will be an enormous amount of processing to be done. To cope with this rapid expansion in the Internet of Things, successful IoT platforms will need a data architecture that can address significant challenges in terms of speed, scalability, variable workloads, and other issues. What type of data architecture can handle these challenges? Before discussing the technology needed
to tackle all of the issues around IoT, let’s take a closer look at some popular IoT use cases. To learn more download this white paper.
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.
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
Recent technology advances within the Apache Hadoop ecosystem have provided a big boost to Hadoop’s viability as an analytics environment.
You have a lot of data in Hadoop and you’re looking to analyze it. You don’t have to continue bumping up against the limits of the database you’re moving the data into—or how much of it you can afford to use. This whitepaper addresses how you can leverage the power of the cluster you already have in place, expanding and accelerating what you can do while saving you time and money. This is a big deal, it meets a huge demand, it shows how rapidly the technologies have evolved and it delivers on one of the most significant unmet promises of big data analytics.