From protecting customer experience to preserving lines of revenue, IT operations teams face increasingly complex responsibilities and are responsible for preventing outages that could harm the organization. As a Splunk customer, your machine data platform empowers you to utilize machine learning to reduce MTTR. Discover how six companies utilize machine learning and AI to predict outages, protect business revenue and deliver exceptional customer experiences.
Download the e-book to learn how:
Micron Technology reduced number of IT incidents by more than 50%
Econocom provides better customer service by centralizing once-siloed analytics, improving SLA performance and significantly reducing the number of events
TransUnion combines machine data from multiple applications to create an end-to-end transaction flow
IT organizations struggle with numerous challenges — hybrid environments, lack of visibility during cloud migration, multiple infrastructure monitoring tools, and reliance on manual processes. Yet according to a 2018 global survey, less than half of IT practitioners are confident they can ensure performance and system availability with their current toolset.
As a Splunk customer, you understand the power of running your monitoring and logging environment in a machine data platform. Are you utilizing your machine data platform to effectively run APM, infrastructure monitoring and Network performance monitoring and diagnostics?
This guide outlines the 8 biggest mistakes IT practitioners make and provides solutions, key takeaways and real-world examples to help you improve IT monitoring and troubleshooting in your organization.
Download your copy to learn how to:
Achieve end-to-end-visibility throughout cloud migration
Find trends and root cause faster with automated investigations
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems.
Data Lakes are a new and increasingly popular way to store and analyse data that addresses many of these challenges. Data Lakes allow an organization to store all of their data, structured and unstructured, in one, centralized repository.
Published By: Teradata
Published Date: Jan 20, 2015
This Neil Raden and Teradata webinar explores: The business values gained from an integrated view of SAP® and non-SAP® data; Existing solutions and challenges; Requirements for the optimal BI and analytics platform, and; A new solution that simplifies and enhances BI analytics for SAP® ERP data.
Published By: Teradata
Published Date: May 02, 2017
A Great Use of the Cloud: Recent trends in information management see companies shifting their focus to, or entertaining a notion for the first time of a cloud-based solution. In the past, the only clear choice for most organizations has been on-premises data—oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all or some of a company’s analytical needs.
This paper, written by McKnight Consulting analysts William McKnight and Jake Dolezal, describes two organizations with mature enterprise data warehouse capabilities, that have pivoted components of their architecture to accommodate the cloud.
Published By: Pure Storage
Published Date: Jul 26, 2017
The big breakthrough is coming from one of the leading innovators in the all-flash market, Pure Storage. Pure has scaled its architecture to make all of its key enterprise-class features available in an entry-level array that “democratizes” all-flash storage by making it affordable to just about any business. The new product is called the Pure FlashArray//m10.
Published By: Pure Storage
Published Date: Oct 09, 2018
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
Published By: Pure Storage
Published Date: Oct 09, 2018
Massive amounts of data are being created driven by
billions of sensors all around us such as cameras, smart
phones, cars as well as the large amounts of data across
enterprises, education systems and organizations. In
the age of big data, artificial intelligence (AI), machine
learning and deep learning deliver unprecedented
insights in the massive amounts of data.
Published By: Google Apigee
Published Date: Jun 08, 2018
A must-read for IT professionals that provides a comprehensive analysis of the API management marketplace and evaluates 22 vendors across 15 essential criteria.
APIs are the de-facto standard for building and connecting modern applications. But securely delivering, managing and analyzing APIs, data and services, both inside and outside an organization, is complex. And it’s getting even more challenging as enterprise IT environments grow dependent on combinations of public, private and hybrid cloud infrastructures.
Choosing the right APIs can be critical to a platform’s success. Likewise, full lifecycle API management can be a key ingredient in running a successful API-based program. Tools like Gartner’s Magic Quadrant for Full Life Cycle API Management help enterprises evaluate these platforms so they can find the right one to fit their strategy and planning.
Apigee is pleased to offer you a complimentary copy of the Gartner report. Access in-depth evaluations of API managemen
Published By: Redstor UK
Published Date: Mar 12, 2018
The General Data Protection Regulation, is a piece of legislation that was approved and put in to place by the European Parliament in April 2016. As European Law, it will fully take effect after a 2-year transition ending May 25th 2018.
GDPR, replaces the previous Data Protection Directive (DPD), adopted in 1995, and will in the UK, replace and strengthen the Data Protection Act 1998 (DPA). One of the initial differences between GDPR and DPD, is that GDPR is a regulation not a directive; as a regulation, no additional enabling legislation will have to be passed by governments of member states.
Redstor have the ability to give insight into the data organisations have on their networks, advise on best practice to protect data and then implement strategies around backup, archiving and disaster recovery. Data is searchable through an intuitive console making compliance simple and achievable for all organisations protecting data through the Backup Pro Platform.
This paper explores why your business needs the latest operational decision management (ODM) solutions to help turn data insights into action. Discover how IBM Operational Decision Manager software and the IBM Business Process Manager platform work together.
This paper explores why your business needs the latest operational decision management (ODM) solutions to help turn data insights into action. Discover how IBM Operational Decision Manager software and the IBM Business Process Manager platform work together to: *Recognize patterns that suggest opportunity or risk *Create and shape business events by automating decisions *Bring more dimension and precision to decision making by applying analytics to big data *Help you implement the right business processes by understanding data in context.
As with most innovations in business information technology, the ultimate truth about cloud lies somewhere in between. There is little doubt that cloud-based infrastructures offer an immediate opportunity for smaller organizations to avoid the costly investment needed for a robust on-premises computing environment. Data can be found, processed and managed on the cloud without investing in any local hardware. Large organizations with mature on-premises computing infrastructures are looking to Hadoop platforms to help them benefit from the vast array of structured and unstructured data from cloud-based sources. Organizations have feet in both cloud and on-premises worlds. In fact, one could easily argue that we already live in a “hybrid” world.
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.
In the era of always-on business, enterprises need reliable, secure and consistently fast access at all times. Overlay that with the reality of how organizations combine on-premises systems with cloud-based solutions, and it’s clear that a robust, agile and flexible database platform is mandatory.
In the era of always-on business, enterprises need reliable, secure and consistently fast access at all times.
Overlay that with the reality of how organizations combine on-premises systems with cloud-based
solutions, and it’s clear that a robust, agile and flexible database platform is mandatory.
Here are the 6 reasons to change your database:
Lower total cost of ownership
Increased scalability and availability
Flexibility for hybrid environments
A platform for rapid reporting and analytics
Support for new and emerging applications
Greater simplicity
Download now to learn more!
For increasing numbers of organizations, the new reality
for development, deployment and delivery of applications
and services is hybrid cloud. Few, if any, organizations are
going to move all their strategic workloads to the cloud,
but virtually every enterprise is embracing cloud for a wide
variety of requirements.
This paper outlines what readers should consider when
making a strategic commitment to a database platform that
will act as a bridge from legacy environments to the cloud.
For increasing numbers of organizations, the new reality for development, deployment and delivery of applications and services is hybrid cloud. Few, if any, organizations are going to move all their strategic workloads to the cloud, but virtually every enterprise is embracing cloud for a wide variety of requirements.
To accelerate innovation, improve the IT delivery economic model and reduce risk, organizations need to combine data and experience in a cognitive model that yields deeper and more meaningful insights for smarter decisionmaking. Whether the user needs a data set maintained in house for customer analytics or access to a cloud-based data store for assessing marketing program results — or any other business need — a high-performance, highly available, mixed-load database platform is required.
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
The Kofax enterprise capture platform offers unmatched scalability from centralized to highly distributed environments, from individual desktops to enterprise deployments and from basic archival scanning to powerful document classification and separation and data extraction. The company's market leading technology supports a wide variety of input devices and line of business applications, providing a strong enterprise-wide platform on which to standardize document driven processes.