By Hasso Plattner
Recent achievements in and software program improvement, akin to multi-core CPUs and DRAM capacities of a number of terabytes in keeping with server, enabled the advent of a progressive expertise: in-memory information administration. This know-how helps the versatile and intensely quickly research of huge quantities of company info. Professor Hasso Plattner and his examine staff on the Hasso Plattner Institute in Potsdam, Germany, were investigating and instructing the corresponding suggestions and their adoption within the software program for years.
This booklet relies on a web path that was once first introduced in autumn 2012 with greater than 13,000 enrolled scholars and marked the profitable place to begin of the openHPI e-learning platform. The direction is especially designed for college students of laptop technology, software program engineering, and IT comparable matters, yet addresses company specialists, software program builders, expertise specialists, and IT analysts alike. Plattner and his team specialise in exploring the interior mechanics of a column-oriented dictionary-encoded in-memory database. lined issues contain - among others - actual info garage and entry, easy database operators, compression mechanisms, and parallel subscribe to algorithms. past that, implications for destiny firm purposes and their improvement are mentioned. step-by-step, readers will comprehend the novel variations and benefits of the recent know-how over conventional row-oriented, disk-based databases.
In this thoroughly revised 2nd variation, we comprise the suggestions of hundreds of thousands after all contributors on openHPI and take note of most up-to-date developments in tough- and software program. superior figures, factors, and examples extra ease the knowledge of the options provided. We introduce complicated info administration suggestions resembling obvious combination caches and supply new showcases that show the potential for in-memory databases for 2 assorted industries: retail and existence sciences.
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Extra resources for A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases
Proteomics research benefits from the integration of relational database operators and arbitrary mathematical operations in SanssouciDB, allowing the proteomics researcher to compose complex analysis pipelines in one environment using clinical patient data as well as raw spectrum data inside one database. Since every parameter and algorithm influences the accuracy of the resulting statistical model, iterative tuning of such pipelines in real-time is a requirement in this field: here the fast traversal of data and algorithm execution is crucial.
The more often identical values appear, the better dictionary encoding can compress a column. As we noted in Sect. 6, enterprise data has low entropy. Therefore, dictionary encoding is well suited and yields a good compression ratio in such scenarios. In the following, we calculate the possible savings for the first name and gender columns of our world-population example. H. 1007/978-3-642-55270-0__6, © Springer-Verlag Berlin Heidelberg 2014 39 40 6 Dictionary Encoding Fig. 1 Compression Example Given the world population table with 8 billion rows and 200 Byte per row: Attribute # of distinct values First name Last name Gender Country City Birthday 5 million 8 million 2 200 1 million 40,000 Sum Size (Byte) 49 50 1 49 49 2 200 The complete amount of data is: 8 billion rows 200 Byte per row D 1:6 TB Each column is split into a dictionary and an attribute vector.
Enterprise Resource Planning (ERP) systems, for example, typically create transactional data to capture the operations of a business. More and more event and stream data is created by modern manufacturing machines and sensors. At the same time, large amounts of unstructured data is captured from the web, social networks, log files, support systems, and others. Business users need to query these different data sources as fast as possible to derive business value from the data or coordinate the operations of the enterprise.
A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases by Hasso Plattner