Data Warehouse

White Paper: Quantum

More and more enterprises are faced with the complex challenge of managing the exponential data growth. Digital data hoarding is one of the biggest IT concerns for businesses globally. The plethora of new and improved tools for capturing, generating, analyzing and otherwise leveraging stored data has resulted in explosive data growth—and managing that data growth is a primary challenge for IT organizations in all industries. This whitepaper helps you develop a deeper appreciation for the dynamics of data hoarding. In this whitepaper you will learn why data hoarders behave as they do, and gain insights into strategies and tools you can use to meet their needs—all while adhering to your budget requirements. It highlights: -Common attributes of some example data hoarders -Use case requirements and storage technologies -Storage components and best fit uses -Best practices for the hoarders in your organization

White Paper: Stone Bond Technologies

The Data Warehouse (DW) has been around some thirty years as essentially a repository forstoring corporate data. The effort to define, design, and implement new data sets in a data warehouse results in backlogs that are prohibitive to support the fast pace of today’s data needs. Most companies will continue to use their Data Warehouse as they move to the more agile approach, but they will rely on it mostly as a historical data repository for reporting and analytics. The Logical Data Warehouse (LDW) is a new data management architecture for analytics which combines the strengths of traditional repository warehouses with alternative data management and access strategy. Read in this White Paper on Data Warehouse: An overview on Data Virtualization for Logical Data Warehouse Challenges with Classical Data Warehouse A case for a new generation of data access (LDW) Logical Data Warehouse for Business Intelligence Leveraging Data Virtualization for next generation Data Warehouses Data Virtualization for Logical Data Warehouse

White Paper: Inspur Group Co. Ltd

Apache Spark is a fast and general engine for large-scale data processing. To handle increasing data rates and demanding user expectations, big data processing platforms like Apache Spark have emerged and quickly gained popularity. This whitepaper on “Optimizing Apache Spark with Memory1”demonstrates that by leveraging Memory1 to maximize the available memory, the servers can do more work (75% efficiency improvement in Spark performance), unleashing the full potential of real-time, big data processing. This Technology Whitepaper Covers: How Spark works? What are the issues that subvert the full potential of Apache Spark’s disaggregated approach? How to simulate the critical demands of a typical Spark operations workload? How to eliminate the hardware cost concerns traditionally faced in multi-server Spark deployments? What efficiency metrics are involved in the Spark operations? Key issues faced by Spark in traditional, DRAM-only deployments How to Avoid the high cost of DRAM-only implementations in Apache Spark architects

White Paper: Load DynamiX

Let’s face it- Storage Performance Evaluation is an arduous task!! To profile and analyze storage performance the testing tools should not only be “good enough” for smaller, on-off tests, but should also accurately measure when requirements scale and the workload becomes more complex. Key take-away from this whitepaper on Storage Performance: Storage Performance Analysis: Learn how to conduct storage network performance analysis to troubleshoot and fix storage network problems Storage Performance Testing Tools: Learn how to accurately measure storage performance metrics in your data storage system Storage Architecture: Learn how to evaluate the performance of customers’ storage systems with a proper storage architecture Download this informative whitepaper on “Evaluating storage performance: Using the right tools for the job” to understand how proper storage performance testing tools can evaluate the efficiency of your enterprise data storage resources in terms of capacity utilization, storage performance and data protection. 

White Paper: Decision Management Solution

Standards play a central role in creating an ecosystem that supports current and future needs for broad, real-time use of predictive analytics in an era of Big Data. Just a few years ago it was common to develop a predictive analytic model using a single proprietary tool against a sample of structured data. This would then be applied in batch, storing scores for future use in a database or data warehouse. Recently this model has been disrupted. There is a move to real-time scoring, calculating the value of predictive analytic models when they are needed rather than looking for them in a database. At the same time the variety of model execution platforms has expanded with in-database execution, columnar and in-memory databases as well as MapReduce-based execution becoming increasingly common.

White Paper: STEALTHbits Technologies

Data access governance forces companies to look internally and figure out the quintessential building blocks that may have gone unturned for a long, long time. Access governance across File Systems is now achievable, and it should be expected that each and every company achieve it. This insightful paper shows the steps to implement the ideal model of gaining visibility, zero impact migration and ongoing governance to mitigate the data access governance issue of your organization. This technical whitepaper on ‘’The Need for Data Access Governance’’ mainly focuses on the below three points to solve the problems in data access governance  : •To make data access governance simpler get open shares under control •Gain a complete and accurate understanding of how file system access is configured in your current data access governance model. •Integration of unstructured data management with Identity and Access Management (IAM) systems to alleviate the data access governance issue Download this whitepaper that describes an approach to implement a self-sustaining access governance system that will provide complete control over data access and a methodology to adopt this system with zero impact on business operations.