This business-oriented white paper summarizes the wide-ranging benefits of the Hadoop platform, highlights common data processing use cases and explores examples of specific use cases in vertical industries.
Published By: Altiscale
Published Date: Aug 25, 2015
Weren't able to attend Hadoop Summit 2015? No sweat. Learn more about the latest Big Data technologies in these technical presentations at this recent leading industry event. The Big Data experts at Altiscale - the leader in Big Data as a Service - have been busy at conferences.
To see all four presentations (in slides and youtube video), click here. https://www.altiscale.com/educational-slide-kit-2015-big-data-conferences-nf/
• Managing Growth in Production Hadoop Deployments
• Running Spark & MapReduce Together in Production
• YARN and the Docker Ecosystem
• 5 Tips for Building a Data Science Platform
SAS Institute is gearing up to make a self-service data preparation play with its new Data Loader for Hadoop offering. Designed for profiling, cleansing, transforming and preparing data to load it into the open source data processing framework for analysis, Data Loader for Hadoop is a lynchpin in SAS's data management strategy for 2015.
This strategy centers on three key themes: 'big data' management and governance involving Hadoop, the streamlining of access to information, and the use of its federation and integration offerings to enable the right data to be available, at the right time.
Published By: StreamSets
Published Date: Sep 24, 2018
The advent of Apache Hadoop™ has led many organizations to replatform their existing architectures to reduce data management costs and find new ways to unlock the value of their data. One area that benefits from replatforming is the data warehouse. According to research firm Gartner, “starting in 2018, data warehouse managers will benefit from hybrid architectures that eliminate data silos by blending current best practices with ‘big data’ and other emerging technology types.” There’s undoubtedly a lot to ain by modernizing data warehouse architectures to leverage new technologies, however the replatforming process itself can be harder than it would at first appear. Hadoop projects are often taking longer than they need to create the promised benefits, and often times problems can be avoided if you know what to avoid from the onset.
Published By: Altiscale
Published Date: Mar 30, 2015
Implementing and scaling Hadoop to analyze large quantities of data is enormously complicated. Unforeseen, very challenging problems are to be expected. However, if you can learn to recognize the problems before a fire starts, you can prevent your hair (and your Hadoop implementation) from igniting.
From the Hadoop experts at Altiscale, here are some of the danger signs and problems you should watch out for, as well as real-world lessons learned for heading them off.
Published By: Dell EMC
Published Date: Oct 08, 2015
Download this white paper to learn how the company deployed a Dell and Hadoop cluster based on Dell and Intel® technologies to support a new big data insight solution that gives clients a unified view of customer data.
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.
Published By: Altiscale
Published Date: Aug 25, 2015
Hype abounds about Big Data. And it's hard to know how to effectively exploit its potential. Learn how to separate fact from fiction in this new webinar + research note titled "Amazon EMR is Easy and 7 Other Myths." If you're considering launching a new Big Data initiative, or if you are currently struggling with Amazon EMR, view this 30 minute on-demand webinar + research note and dispel the most common untruths about Amazon EMR, as determined by leading Hadoop experts. Specifically, you'll learn:
• The differences between Hadoop-as-a-Service and Amazon EMR
• Why Hadoop on Amazon is not elastic
• Why EMR is not a "plug-n-play" application
• How costs get out of control with Amazon EMR
Should you modernize with Hadoop? If your goal is to catch, process and analyze more data at dramatically lower costs, the answer is yes. In this e-book, we interview two Hadoop early adopters and two Hadoop implementers to learn how businesses are managing their big data and how analytics projects are evolving with Hadoop. We also provide tips for big data management and share survey results to give a broader picture of Hadoop users. We hope this e-book gives you the information you need to understand the trends, benefits and best practices for Hadoop.
Published By: WANdisco
Published Date: Oct 15, 2014
In this Gigaom Research webinar, the panel will discuss how the multi-cluster approach can be implemented in real systems, and whether and how it can be made to work. The panel will also talk about best practices for implementing the approach in organizations.
These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse.
Download this ebook to learn the requirements for delivering trusted information to a modern data warehouse and the guiding principles for trusted information in next generation data warehouse environments.
Published By: BlueData
Published Date: Mar 13, 2018
In a benchmark study, Intel compared the performance of Big Data workloads running on a bare-metal deployment versus running in Docker containers with the BlueData software platform. This landmark benchmark study used unmodified Apache Hadoop* workloads
Published By: Snowflake
Published Date: Jan 25, 2018
Compared with implementing and managing Hadoop (a traditional on-premises data warehouse) a data warehouse built for the cloud can deliver a multitude of unique benefits. The question is, can enterprises get the processing potential of Hadoop and the best of traditional data warehousing, and still benefit from related emerging technologies?
Read this eBook to see how modern cloud data warehousing presents a dramatically simpler but more power approach than both Hadoop and traditional on-premises or “cloud-washed” data warehouse solutions.
There is a lot of discussion in the press about Big Data. Big Data is traditionally defined in terms of the three V’s of Volume, Velocity, and Variety. In other words, Big Data is often characterized as high-volume, streaming, and including semi-structured and unstructured formats.
Healthcare organizations have produced enormous volumes of unstructured data, such as the notes by physicians and nurses in electronic medical records (EMRs). In addition, healthcare organizations produce streaming data, such as from patient monitoring devices. Now, thanks to emerging technologies such as
Hadoop and streams, healthcare organizations are in a position to harness this Big Data to reduce costs and improve patient outcomes. However, this Big Data has profound implications from an Information Governance perspective. In this white paper, we discuss Big Data Governance from the standpoint of three case studies.
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage.
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage. An effective big data integration solution delivers simplicity, speed, scalability, functionality and governance to produce consumable data.
To cut through this misinformation and develop an adoption plan for your Hadoop big data project, you must follow a best practices approach that takes into account emerging technologies, scalability requirements, and current resources and skill levels.
Published By: Pentaho
Published Date: Jan 16, 2015
If you’re considering a big data project, this whitepaper provides an overview of current common use cases for big data, from entry-level to more complex. You’ll get an in-depth look at some of the most common, including data warehouse optimization, streamlined data refinery, monetizing your data, and getting a 360 degree view of your customer. For each, you’ll discover why companies are investing in them, what the projects look like, and key project considerations, including tools and platforms.
Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.