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
A big data integration platform that is flexible and scalable is needed to keep up with today’s ever-increasing big data volume. Download this infographic to find out how to build a strong foundation with big data integration.
This report provides an overview of the Oracle Cloud at Customer portfolio (this includes Oracle Exadata Cloud at Customer, Oracle Big Data Cloud at Customer, Oracle SaaS at Customer and Oracle Cloud at Customer) and analyzes its capabilities to satisfy the need of enterprises for a next-generation computing platform. A next-generation computing platform allows enterprises to deploy workloads across the premises and the public cloud.
For enterprises running their next-generation applications on a next-generation computing platform, Oracle Cloud at Customer does very well because of Oracle’s vision of the “chip-to-click” integrated technology stack (i.e., from the CPU silicon, across all OSI layers and all the way to the end-user mouse click). With Oracle using the same technology stack and machines both in its cloud and on premises, it has the highest degree of identicality across these offerings from all vendors that are part of Constellation Research’s Market Overview on next-genera
Watch this on-demand webcast to learn how you can accelerate your security transformation from traditional SIEM to a unified platform for incident detection, investigation and advanced security analysis. Understand why organizations are moving to a true big data security platform where compliance is a byproduct of security, not the other way around.
The life of an enterprise architect is becoming busy and difficult.
Before the era of big data, the enterprise architect “only” had to
worry about the data and systems within their own data center.
However, over the past decade there were revolutionary changes
to the way information is used by businesses and how data
management platforms support the information available from
modern data sources.
John Schwarz founded Visier to address what he saw as the major failing of business intelligence and big data analytics.
He had a front row seat in this market while leading Business Objects, the largest global business intelligence provider (acquired by SAP). John and co-founder Ryan Wong’s vision was to completely reinvent the approach to analytics, providing instant and complete, domain-specific applications to business leaders, answering their important strategic questions and leading them to adopt best management practices. Their applied business analytics project is working. Today, more than a hundred blue chip companies have selected Visier as their people strategy platform and are achieving incredible results. And that’s just the beginning.
Since SAP introduced its in-memory database, SAP HANA, customers have significantly accelerated everything from their core business operations to big data analytics. But capitalizing on SAP HANA’s full potential requires computational power and memory capacity beyond the capabilities of many existing data center platforms.
To ensure that deployments in the AWS Cloud could meet the most stringent SAP HANA demands, AWS collaborated with SAP and Intel to deliver the Amazon EC2 X1 and X1e instances, part of the Amazon EC2 Memory-Optimized instance family. With four Intel® Xeon® E7 8880 v3 processors (which can power 128 virtual CPUs), X1 offers more memory than any other SAP-certified cloud native instance available today.
Published By: MarkLogic
Published Date: May 07, 2018
Learn how Life Sciences organizations can accelerate Real World Evidence by achieving faster time to insight with a metadata-driven, semantically enriched operational platform.
Real World Evidence (RWE) is today’s big data challenge in Life Sciences. Medical records, registries, consultation reports, insurance claims, pharmacy data, social media, and patient surveys all contain valuable insights that Life Sciences organizations need to ascertain and prove the safety, efficacy, and value of their drugs and medical devices.
Learn how Life Sciences organizations can accelerate RWE with a metadata-driven, semantically enriched operational platform that enables them to:
• Unify, harmonize and ensure governance of information from diverse data sources
• Transform information into evidence that proves product efficacy and safety
• Identify data patterns, connections, and relationships for faster time to insight
The advent of big data revolutionized analytics and data science and created the concept of new data platforms, allowing enterprises to store, access and analyze vast amounts of historical data. The world of big data was born. But existing data platforms need to evolve to deal with the tsunami of data-in-motion being generated by the Internet of Anything (IoAT).
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: 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.
In this book, we are going to look at the key trends driving the modernization of data infrastructure. We’ll see how organizations are adapting and flourishing in a data-driven world. For some time, headlines have been around the internet of things (IoT),
big data and data analytics. While these developments are important, the reality is that you cannot take full advantage of them without modernization. We’re going to look at these trends and priorities in detail, then look at the three key drivers of modernization: governance, mobilization and analytics. We’ll also consider the technologies that make up modern data infrastructure including artificial intelligence (AI), flash storage, converged and hyperconverged platforms and software-defined infrastructures. By making sense of data, we make sense of the world. With more data than ever before, we have the tools to turn all that information into intelligent innovation and change the way the world works.
Published By: Pentaho
Published Date: Mar 08, 2016
If you’re evaluating big data integration platforms, you know that with the increasing number of tools and technologies out there, it can be difficult to separate meaningful information from the hype, and identify the right technology to solve your unique big data problem. This analyst research provides a concise overview of big data integration technologies, and reviews key things to consider when creating an integrated big data environment that blends new technologies with existing BI systems to meet your business goals.
Read the Buyer’s Guide to Big Data Integration by CITO Research to learn:
• What tools are most useful for working with Big Data, Hadoop, and existing transactional databases
• How to create an effective “data supply chain”
• How to succeed with complex data on-boarding using automation for more reliable data ingestion
• The best ways to connect, transport, and transform data for data exploration, analytics and compliance
Published By: Pentaho
Published Date: Apr 28, 2016
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
Published By: LogRhythm
Published Date: Jan 24, 2013
A SANS functional product review of LogRhythm version 6.1, conducted by senior SANS Analyst Dave Shackleford. It shows LogRhythm's SIEM toolset capable of analyzing and reporting on security data in many differed ways with easy-to-use features.
Published By: Teradata
Published Date: Feb 26, 2013
This report explores the evolution of big data analytics and its maturity within the enterprise. It discusses the approaches and economics to using a Discovery platform and Apache Hadoop within the same unified analytical architecture.
Published By: BlueData
Published Date: Aug 19, 2015
Big Data is on virtually every enterprise’s to-do list these days. Recognizing both its potential and competitive advantage, companies are aligning a vast array of resources to access and analyze this strategic asset. However, despite best intentions, the majority of these Big Data initiatives are either extremely slow in their implementation or are not yielding the results and benefits that enterprises expect. Download this white paper to learn how to solve the Big Data intention-deployment gap and see how you can make your infrastructure in a flexible, easy-to-use platform that will provide in-depth analytics.
The increasing reliance on big data platforms for all functions of the organization has been transformative. As these environments mature and data volumes increase, organizations face infrastructure and management scalability challenges.
Gemini Enterprise Manager simplifies deployment and management with a turnkey, NoOps appliance, providing simplicity, security, and speed to accelerate the time to value for any analysis use case. Manager allows you to control your Splunk deployment as a single, unified solution deployed on premises, in the cloud or both.
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time.
This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
Understanding the data you have and put it to effective use is now more crucial than ever. The key differentiator between the leaders and laggards in financial services today is how skillfully they turn data into useful information and fuel success.
Armed with their opinions and social media accounts, your customers have the kind of clout to influence your brand that last century’s ad men only dreamed about. Learn how your organization can securely capture, analyze, and act upon 100 percent of the data available to you.
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