In-Memory Data Management: An Inflection Point for Enterprise ApplicationsIn the last 50 years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory computing is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. We describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes by leveraging in-memory computing. |
Contents
Introduction | 1 |
PART I An Inflection Point for Enterprise Applications | 5 |
1 Desirability Feasibility Viability The Impact of InMemory | 7 |
2 Why Are Enterprise Applications So Diverse? | 24 |
3 SanssouciDB Blueprint for an InMemory Enterprise Database System | 33 |
PART II SanssouciDB A Single Source of Truth through InMemory | 41 |
4 The Technical Foundations of SanssouciDB | 42 |
5 Organizing and Accessing Data in SanssouciDB | 89 |
7 Finally a Real Business Intelligence System Is at Hand | 171 |
8 Scaling SanssouciDB in the Cloud | 193 |
9 The InMemory Revolution Has Begun | 205 |
References | 210 |
About the Authors | 220 |
Glossary | 221 |
Abbreviations | 231 |
| 233 | |
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In-Memory Data Management: An Inflection Point for Enterprise Applications Hasso Plattner,Alexander Zeier No preview available - 2011 |
Common terms and phrases
additional aggregation algorithms allows amount analytical approach architecture attributes become benchmark blades block cache cache line cloud column combined complete compression computing consists contains cores cost created data structures database system described dictionary differential buffer discussed disk distributed enterprise applications example execution existing Figure given hardware hash implementation improve in-memory in-memory technology increase input insert introduced join layer layout lead load machine main memory main store merge multiple node object operations optimized parallel partition performance physical planning possible processing processor query reduce relational reports response result retrieved SanssouciDB scheduling schema SELECT server shared shows single storage structure tasks threads transaction tuples types updates users virtual workload write