By Hasso Plattner
Recent achievements in and software program improvement, resembling multi-core CPUs and DRAM capacities of a number of terabytes in line with server, enabled the advent of a innovative know-how: in-memory facts administration. This know-how helps the versatile and intensely speedy research of big quantities of company facts. Professor Hasso Plattner and his learn workforce on the Hasso Plattner Institute in Potsdam, Germany, were investigating and instructing the corresponding thoughts and their adoption within the software program for years.
This e-book is predicated on an internet direction that used to be first introduced in autumn 2012 with greater than 13,000 enrolled scholars and marked the winning place to begin of the openHPI e-learning platform. The path is principally designed for college students of computing device technological know-how, software program engineering, and IT comparable matters, yet addresses company specialists, software program builders, know-how specialists, and IT analysts alike. Plattner and his workforce specialize in exploring the internal mechanics of a column-oriented dictionary-encoded in-memory database. lined themes contain - among others - actual information 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 changes and benefits of the recent expertise over conventional row-oriented, disk-based databases.
In this thoroughly revised 2nd variation, we comprise the suggestions of hundreds of thousands in fact contributors on openHPI and take into consideration most modern developments in demanding- and software program. enhanced figures, reasons, and examples extra ease the certainty of the recommendations provided. We introduce complicated information administration recommendations comparable to obvious mixture caches and supply new showcases that reveal the possibility of in-memory databases for 2 diversified industries: retail and lifestyles sciences.
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Additional resources for A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases
L. Brodie (Morgan Kaufmann, San Francisco, 1999), pp. P. N. Khoshafian, A decomposition storage model. SIGMOD Rec. 14(4), 268–279 (1985) [HAMS08] S. J. Abadi, S. Madden, M. Stonebraker, Oltp through the looking glass, and what we found there, in SIGMOD Conference, ed. -L. Wang, (ACM, 2008), pp. 981–992 Part II Foundations of Database Storage Techniques Chapter 6 Dictionary Encoding Since memory is the new bottleneck, it is required to minimize access to it. Accessing a smaller number of columns by only querying required attributes can do this on the one hand.
For years enterprise data management focused on structured data only. Structured data is stored in a relational database format using tables with specific attributes. However, many documents, papers, reports, web sites, etc. g. text documents. Information within these documents is typically identified via the document’s meta data. A detailed search within the content of these documents or the extraction of specific facts is however not possible by using the meta data. As a result, there is a need to harvest information buried within unstructured enterprise data.
They analyze the structured data of ERP systems for real-time transactional reporting, classical analytics, planning, and simulation. The data from other data sources can be taken into account as well, for example, a text analytics application can combine a customer sentiment analysis on social network data with sales numbers or a production planning application takes sensor data of the RFID sensors into account. 2 OLTP vs. OLAP An enterprise data management system should be able to handle transactional and analytical query types, which differ in several dimensions.
A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases by Hasso Plattner