Published By: Datastax
Published Date: Sep 27, 2019
In this white paper, you’ll learn about the three most important issues surrounding data management in the age of IoT. You'll also learn how DataStax Enterprise, built on Apache Cassandra™ and native to hybrid cloud environments, is paving the way for the future of data management. Download the white paper to learn more now.
Published By: Datastax
Published Date: Oct 11, 2019
The first and most important step to building a successful, scalable application is getting the data model right.
In this white paper, you’ll get a detailed, straightforward, five-step approach to creating the right data model right out of the gate—from mapping workflows, to practicing query-first design thinking, to using Cassandra data types effectively.
Published By: DataStax
Published Date: Mar 10, 2017
Netflix, Intuit and Clear Capital. These 3 innovative companies have one thing in common. They are altering their business landscape and transforming the way people live and work through highly personalized applications. And they're doing this with Apache Cassandra™ and DataStax.
Download this white paper and learn why relational technologies failed to meet the demands of Netflix, Mint Bills and Clear Capital and how these enterprises modernize their Web and Mobile applications with DataStax to drive customer engagement, loyalty and lifetime value.
Published By: Red Hat
Published Date: Jan 01, 2009
The number 1 independent tire retailer tried, unsuccessfully, to build an online platform based on Windows. Ultimately, the company succeeded instead with a solution built with Red Hat Enterprise Linux, Red Hat Network Satellite, Apache, WebLogic, and IBM Lotus Domino Server.
University of California-Berkeley researchers employ the latest tools built on Apache Spark to accelerate DNA sequencing in pursuit of precision medicine. Adding Pure FlashBlade™
from Pure Storage® has significantly reduced the time needed to sequence data-intensive DNA samples and analyze results.
Apache® Spark™ has become one of the most rapidly adopted open source platforms in history. Demand is predicted to grow at a compound annual rate of 67% per year between 2017 and 2020, with the cumulative Spark market valued at more than $9 billion during that period, according to the research firm MarketAnalysis.com.
Pure Storage, a pioneer in block-based flash arrays, has developed a technology called FlashBlade, designed specifically for file and object storage environments. With FlashBlade, IT teams now have a simple-to-manage shared storage solution that delivers the performance and capacity needed to bring Spark deployments on premise.
To help gain a deeper understanding of the storage challenges related to Spark and how FlashBlade addresses them, Brian Gold of Pure Storage sat down with veteran technology journalist Al Perlman of TechTarget for a far-reaching discussion on Spark trends and opportunities.
Apache Spark has become a critical tool for all types of businesses across all industries. It is enabling organizations to leverage the power of analytics to drive innovation and create new business models.
The availability of public cloud services, particularly Amazon Web Services, has been an important factor in fueling the growth of Spark. However, IT organizations and Spark users are beginning to run up against limitations in relying on the public cloud—namely control, cost and performance.
Published By: Teradata
Published Date: Jan 30, 2015
This report is about two of those architectures: Apache™ Hadoop® YARN and Teradata® Aster® Seamless Network Analytical Processing (SNAP) Framework™. In the report, each architecture is described; the use of each in a business problem is illustrated; and the results are compared.
Published By: Teradata
Published Date: Jan 30, 2015
It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.
Published By: Lucidworks
Published Date: Dec 14, 2016
You feel you’ve got a pretty good handle on the following challenges—exponentially increasing amounts of data, ever-increasing user expectations, and limited IT resources—along with your technical requirements. That’s why you chose to build your search app with Apache Solr. Download now to learn more about Apache SoIr and Lucidworks Fusion.
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.
Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
IT organizations using machine data platforms like Splunk recognize the importance of consolidating disparate data types for top-down visibility, and to quickly respond to critical business needs. Machine data is often underused and undervalued, and is particularly useful when managing infrastructure data coming from AWS, sensors and server logs.
Download “The Essential Guide to Infrastructure Machine Data” for:
The benefits of machine data for network, remote, web, cloud and server monitoring
IT infrastructure monitoring data sources to include in your machine data platform
Machine data best practices
Published By: Attunity
Published Date: Feb 12, 2019
Read this technical whitepaper to learn how data architects and DBAs can avoid the struggle of complex scripting for Kafka in modern data environments. You’ll also gain tips on how to avoid the time-consuming hassle of manually configuring data producers and data type conversions. Specifically, this paper will guide you on how to overcome these challenges by leveraging innovative technology such as Attunity Replicate. The solution can easily integrate source metadata and schema changes for automated configuration real-time data feeds and best practices.
Red Hat® JBoss® Fuse is a lightweight integration platform that reduces the pain of connecting
applications, services, processes, and devices for comprehensive and efficient solutions. JBoss Fuse
includes the popular and versatile Apache Camel project, an implementation of the most commonlyused enterprise integration patterns. With integration patterns and more than 150 connectors
ready to use, JBoss Fuse supports integration across the extended enterprise—including applications and services on-premise, on mobile devices, or in the cloud. JBoss Fuse is complemented by
Red Hat JBoss Developer Studio for easier development of integration solutions and Red Hat JBoss
Operations Networkfor monitoring of deployed solutions.
The financial services industry has unique challenges that often prevent it from achieving its strategic goals. The keys to solving these issues are hidden in machine data—the largest category of big data—which is both untapped and full of potential.
Download this white paper to learn:
*How organizations can answer critical questions that have been impeding business success
*How the financial services industry can make great strides in security, compliance and IT
*Common machine data sources in financial services firms
One of the biggest challenges IT ops teams face is the lack of visibility across its infrastructure — physical, virtual and in the cloud. Making things even more complex, any infrastructure monitoring solution needs to not only meet the IT team’s needs, but also the needs of other stakeholders including line of business (LOB) owners and application developers.
For companies already using a monitoring platform like Splunk, monitoring blindspots arise from the need to prioritize across multiple departments. This report outlines a four-step approach for an effective IT operations monitoring (ITOM) strategy.
Download this report to learn:
How to reduce monitoring blind spots when creating an ITOM strategy
How to address ITOM requirements across IT and non-IT groups
Distinct layers across ITOM Potential functionality gaps with domain-specific products
Apache® Spark™ has become a vital technology for
development teams looking to leverage an ultrafast
in-memory data engine for big data analytics. Spark
is a flexible open-source platform, letting developers
write applications in Java, Scala, Python or R. With
Spark, development teams can accelerate analytics
applications by orders of magnitude
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.
This e-book highlights the benefits of Hadoop across several industries and explores how IBM® Biglnsights for Apache™ Hadoop® combines open source Hadoop with enterprise-grade management and analytic capabilities.
A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamlessly integrate with diverse technologies, from data marts to Apache Hadoop systems. And it must automatically discover, protect and monitor sensitive information as part of big data applications.
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.
Known by its iconic yellow elephant, Apache Hadoop is purpose-built to help companies manage and extract insight from complex and diverse data environments. The scalability and flexibility of Hadoop might be appealing to the typical CIO but Aberdeen's research shows a variety of enticing business-friendly benefits.
Published By: IBM APAC
Published Date: Aug 25, 2017
Machine learning automates the development of analytic models that can learn and make predictions on data. It has been one of the fastest growing disciplines within the world of statistics and data science, but the barrier to entry has been high, not only in cost, but also in the need for specialized talent.