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Data Protection White Papers
Beyond Deduplication Technical WhitePaper
Gartner’s assessment that “”Backup is Broken” is indeed correct. Through thousands of end user interactions, the Gartner analysts concluded that enterprises, both large and small, are facing enormous challenges in protecting data. This WhitePaper explains why Data Deduplication, while important, is little more than a band-aid and doesn’t resolve the majority of problems created by the challenges of Data Lift, and how to address these issues with a simple, integrated and proven solution that solves all the challenges IT faces today from rapid data growth, accelerating server virtualization and the need for ever-more-rapid recovery times.
Recovery Without Compromise
Ever since data has been stored there has been the need for recovery, and while the storage devices used to store information have become infinitely more reliable, the data that is stored on those devices has become even more critical. Information also now seldom leaves its digital state. It is created, modified, archived and restored as a digital asset. In this all-digital environment, loss of the storage device that contains that information means permanent loss with no means of manual access. However, the expectations of the consumers of this data are that in the event of a failure the information should be quickly recovered with minimal, if any, data loss between data protection windows. In short, they’re not prepared to make any compromises when it comes to the recovery of their digital assets, and neither should the IT personnel who are asked to protect that data.
Virtualization, Deduplication and the Data Protection Connection
As data center resource consolidation gains traction, many organizations are missing the big picture. For reducing primary storage footprints, they are looking at virtualization solutions in isolation, and for reducing secondary backup storage, they are focusing on deduplication solutions in isolation. To consolidate optimally, they must consider the full landscape.
Four Critical Imperatives for Data Protection
Conventional models of backup and restore have become obsolete. They are being replaced by newer dynamic paradigms that involve disk-to-disk, virtual server provisioning, sophisticated data de-duplication, and appliance-based operations. This paper shows the reader how to navigate in this environment by addressing primary business imperatives.
ETL 2.0: Data Integration Comes of Age
In this white paper published by The Bloor Group, industry expert Robin Bloor examines the evolution of ETL from its inception to the next generation of ETL tools, known as ETL 2.0. The paper analyzes the stresses put on the first generation of ETL products and the limitations of these ETL tools to deal with the challenges of Big Data, as well as the characteristics and benefits of ETL 2.0 to meet the strategic needs of the business and IT organizations.
TDWI Checklist Report: Accelerating Data Integration
Part of the popular Checklist Report series published by TDWI Research, this paper is authored by industry expert and BI analyst Claudia Imhoff, who examines the most common indicators of data integration performance issues and offers a practical approach to overcoming these challenges by accelerating data integration environments.
TDWI Best Practices Report: Next Generation Data Integration (Second Quarter 2011)
Data integration has changed so quickly and completely in recent years that it scarcely resembles older definitions. The purpose of this report, published by The Data Warehousing Institute (TDWI) and co-sponsored by Syncsort, is to accelerate users’ understanding of the many new products and options that have entered DI practices in recent years. It also help readers map newly available technologies, products, and practices to real-world use cases.
For Data Integration, Performance is the New Mandate
BeyeNETWORK recently conducted a survey of over 350 IT executives, managers and staff to identify the top data integration performance challenges encountered today, as well as the most common ways to address them.
Mainframe Sort Acceleration
This white paper describes how Sort Acceleration helps organizations maintain their existing infrastructure and replace a subset of their mainframe sort jobs with a higher performance sort solution. This approach enables them to dramatically improve the performance of their most critical and resource intensive sort jobs, and in turn enables meaningful business benefits, including significantly reduced MIPS, higher throughput, and shortened batch processing windows.
Platform Acceleration Solutions for Data Warehousing Initiatives
This white paper examines the role of data integration processing in the context of data warehousing initiatives, and describes how Data Integration Acceleration technology can be incorporated in a complementary, non-disruptive manner into existing environments to radically boost performance and extract previously-unattainable value from the data warehousing infrastructure.
Mainframe Application Modernization
This white paper examines the issues surrounding the data migration and data integration aspects of mainframe application modernization initiatives, and provides specific functional recommendations that address these issues.
DI Acceleration Solution for Existing DI Environments
Data performance problems occur within even the most carefully planned and well-executed data integration environments. Large and growing data sets, combined with shortened operational timeframes, often create unacceptable bottlenecks and latencies in processing and accessing data. The business ramifications of these data bottlenecks can be severe, resulting in lost revenue opportunities, increased costs, impaired decision making, and customer attrition.
Extending Data Warehouse Performance: ETL Data Loading Benchmark Results
See how Syncsort’s DMExpress made data integration history with this white paper detailing how Syncsort, HP and Vertica systems combined to shatter the world record for the world’s fastest data integration — extracting, transforming and loading 5.65TB of raw data at a rate of 1.6GB/second.