Big data is set to offer companies tremendous insight. Data visualization is becoming an increasingly important component of analytics in the age of big data. access to information in a form they can easily understand and share with others. This begs the question: How do you present big data in a way that business leaders can quickly understand and use? Learn more by reading this white paper.
In this Guide, we take a look at what Lustre on infrastructure AWS delivers for a broad community of business and commercial organizations
struggling with the challenge of big data and demanding storage growth. Learn more by downloading this Guide.
StreamSets Launches Embed Program to Help Technology Innovators Integrate Big Data Flows Into Their Products
StreamSets, the dataflow performance management company, announced the StreamSets Embed Program, which provides support and services to technology companies needing to embed world-class data ingestion capabilities into their products and services.
This paper outlines the features available today in Spectrum Scale that organizations can use to manage file data. This functionality includes core Spectrum Scale concepts such as striped data storage, cluster configuration options such as direct storage access and network-based block I/O, storage automation technologies such as information lifecycle management (ILM) tools, and more.
Big data isn’t about technology; it’s about business outcomes and performance. Therefore, it’s essential that you have a reasonable assurance that your business needs and demands will be met— before investing your first dime. The logical starting point for any new initiative should stem from a set of business needs, questions or opportunities that have measurable results, such as improved customer satisfaction, increased profit margins or faster decision making.
WatersTechnology surveyed insurers to understand the progress they have made
in the past few years in better managing their risk, especially their liquidity and solvency risk. Since the global liquidity crisis of 2008, we have seen calamities (Superstorm Sandy and floods in the US, tsunamis in the southern Pacific, and countless weather events around the globe) and regulation impact how insurers manage their risk. Insurers that were branded “too big to fail” continue to operate as-is despite initial outcries from politicians and pundits for changes. Could a too- big-to-fail firm fail today? Or if too-big-to-fail firms collapsed today, would they bring down entire economies with them?
Addressing Liquidity and Solvency Risk
Today’s enterprise datacenters are dealing with new challenges that are far more demanding than ever before. Foremost is the exponential growth of data, most of it unstructured data. Big data and analytics implementations are also quickly becoming a strategic priority in many enterprises, demanding online access to more data, which is retained for longer periods of time. Legacy storage solutions with fixed design characteristics and a cost structure that doesn’t scale are proving to be ill-suited for these new needs. This Technology Spotlight examines the issues that are driving organizations to replace older archive and backup-and-restore systems with business continuity and always-available solutions that can scale to handle extreme data growth while leveraging a cloud- based pricing model. The paper also looks at the role of Storiant and its long-term storage services solution in the strategically important long-term storage market.
Financial Risk Analytics has evolved beyond a secondary regulatory and cost-focused perspective, to become a core part of businesses. Today, Risk Analytics provides a competitive edge by enabling businesses to efficiently use capital and manage risk exposures with higher confidence to make better informed and timelier business decisions. Hence CEOs increasingly rely on their CFOs and their Chief Risk Officers (CROs) for strategic advice and active risk management.
Data and the way that data is used have changed, but data warehousing has not. Today’s premises-based data warehouses are based on technology that is, at its core, two decades old. To meet the demands and opportunities of today, data warehouses have to fundamentally change.
Today’s data, and how that data is used, have changed dramatically in the past few years. Data now comes from everywhere—not just enterprise applications, but also websites, log files, social media, sensors, web services, and more. Organizations want to make that data available to all of their analysts as quickly as possible, not limit access […]