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Beyond Relational Databases
EXPERT ANALYSIS BY MARCOS ALBE, SUPPORT ENGINEER, PERCONA Beyond Relational Databases: A Focus on Redis, MongoDB, and ClickHouse Many of us use and love relational databases… until we try and use them for purposes which aren’t their strong point. Queues, caches, catalogs, unstructured data, counters, and many other use cases, can be solved with relational databases, but are better served by alternative options. In this expert analysis, we examine the goals, pros and cons, and the good and bad use cases of the most popular alternatives on the market, and look into some modern open source implementations. Beyond Relational Databases Developers frequently choose the backend store for the applications they produce. Amidst dozens of options, buzzwords, industry preferences, and vendor offers, it’s not always easy to make the right choice… Even with a map! !# O# d# "# a# `# @R*7-# @94FA6)6 =F(*I-76#A4+)74/*2(:# ( JA$:+49>)# &-)6+16F-# (M#@E61>-#W6e6# &6EH#;)7-6<+# &6EH# J(7)(:X(78+# !"#$%&'( S-76I6)6#'4+)-:-7# A((E-N# ##@E61>-#;E678# ;)762(# .01.%2%+'.('.$%,3( @E61>-#;(F7# D((9F-#=F(*I## =(:c*-:)U@E61>-#W6e6# @F2+16F-# G*/(F-# @Q;# $%&## @R*7-## A6)6S(77-:)U@E61>-#@E-N# K4E-F4:-A%# A6)6E7(1# %49$:+49>)+# @E61>-#'*1-:-# @E61>-#;6<R6# L&H# A6)6#'68-# $%&#@:6F521+#M(7#@E61>-#;E678# .761F-#;)7-6<#LNEF(7-7# S-76I6)6#=F(*I# A6)6/7418+# @ !"#$%&'( ;H=JO# ;(\X67-#@D# M(7#J6I((E# .761F-#%49#A6)6#=F(*I# @ )*&+',"-.%/( S$%=.#;)7-6<%6+-# =F(*I-76# LF6+21+-671># ;G';)7-6<# LF6+21#[(*:I# @E61>-#;"# @E61>-#;)(7<# H618+E61-# *&'+,"#$%&'$#( .761F-#%49#A6)6#@EEF46:1-# -
(BI) Using MS Excel Powerpivot
2018 ASCUE Proceedings Developing an Introductory Class in Business Intelligence (BI) Using MS Excel Powerpivot Dr. Sam Hijazi Trevor Curtis Texas Lutheran University 1000 West Court Street Seguin, Texas 78130 [email protected] Abstract Asking questions about your data is a constant application of all business organizations. To facilitate decision making and improve business performance, a business intelligence application must be an in- tegral part of everyday management practices. Microsoft Excel added PowerPivot and PowerPivot offi- cially to facilitate this process with minimum cost, knowing that many business people are already fa- miliar with MS Excel. This paper will design an introductory class to business intelligence (BI) using Excel PowerPivot. If an educator decides to adopt this paper for teaching an introductory BI class, students should have previ- ous familiarity with Excel’s functions and formulas. This paper will focus on four significant phases all students need to complete in a three-credit class. First, students must understand the process of achiev- ing small database normalization and how to bring these tables to Excel or develop them directly within Excel PowerPivot. This paper will walk the reader through these steps to complete the task of creating the normalization, along with the linking and bringing the tables and their relationships to excel. Sec- ond, an introduction to Data Analysis Expression (DAX) will be discussed. Introduction It is not that difficult to realize the increase in the amount of data we have generated in the recent memory of our existence as a human race. To realize that more than 90% of the world’s data has been amassed in the past two years alone (Vidas M.) is to realize the need to manage such volume. -
Calculated Field in Pivot Table Data Model
Calculated Field In Pivot Table Data Model Frostbitten and unjaundiced Eddie always counteracts d'accord and cowl his tana. New-fashioned and goniometrically,anarchical Ronny however never swop potentiometric his belittlement! Torre enunciatedRevolved Gordan harmonically tunneling or beat-up. or gimlets some doxologies In regular Pivot Tables, you can group numeric, data or text fields. Product of Reliable Bioreactors on Site. Here are exclusive to model in pivot calculated field table data model that data model and used when creating pivot. Power Query, Data model, DAX, Filters, Slicers, Conditional formats and beautiful charts. Eg if you are counting customers that have purchased and have years on rows. Why is this last part important? Depending on the source of data, relationships may or may not be created when the model is initially set up. This data is provided by Microsoft for informational purposes only as an aid to illustrate a concept. To use and limitations and share some limitations of calculated field in pivot table data model. Yeah, good points Derek. Date field, and use it to show a count of orders. Ins menu in the model in pivot calculated field list table that i mentioned earlier, we shall not. Please start a new test to continue. Displays all of the values in each column or series as a percentage of the total for the column or series. This is used to present users with ads that are relevant to them according to the user profile. Note: use the Insert Item button to quickly insert items when you type a formula. -
Sharing Files with Microsoft Office Users
Sharing Files with Microsoft Office Users Title: Sharing Files with Microsoft Office Users: Version: 1.0 First edition: November 2004 Contents Overview.........................................................................................................................................iv Copyright and trademark information........................................................................................iv Feedback.................................................................................................................................... iv Acknowledgments......................................................................................................................iv Modifications and updates......................................................................................................... iv File formats...................................................................................................................................... 1 Bulk conversion............................................................................................................................... 1 Opening files....................................................................................................................................2 Opening text format files.............................................................................................................2 Opening spreadsheets..................................................................................................................2 Opening presentations.................................................................................................................2 -
Array Databases: Concepts, Standards, Implementations
Baumann et al. J Big Data (2021) 8:28 https://doi.org/10.1186/s40537-020-00399-2 SURVEY PAPER Open Access Array databases: concepts, standards, implementations Peter Baumann , Dimitar Misev, Vlad Merticariu and Bang Pham Huu* *Correspondence: b. Abstract phamhuu@jacobs-university. Multi-dimensional arrays (also known as raster data or gridded data) play a key role in de Large-Scale Scientifc many, if not all science and engineering domains where they typically represent spatio- Information Systems temporal sensor, image, simulation output, or statistics “datacubes”. As classic database Research Group, Jacobs technology does not support arrays adequately, such data today are maintained University, Bremen, Germany mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for fexible ad-hoc analytics on large n-D arrays, similar to what SQL ofers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison diferentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the feld; for example, subsetting tests were designed in a way that systems cannot be tuned to specifcally these queries. -
Benchmarking Distributed Data Warehouse Solutions for Storing Genomic Variant Information
Research Collection Journal Article Benchmarking distributed data warehouse solutions for storing genomic variant information Author(s): Wiewiórka, Marek S.; Wysakowicz, David P.; Okoniewski, Michał J.; Gambin, Tomasz Publication Date: 2017-07-11 Permanent Link: https://doi.org/10.3929/ethz-b-000237893 Originally published in: Database 2017, http://doi.org/10.1093/database/bax049 Rights / License: Creative Commons Attribution 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Database, 2017, 1–16 doi: 10.1093/database/bax049 Original article Original article Benchmarking distributed data warehouse solutions for storing genomic variant information Marek S. Wiewiorka 1, Dawid P. Wysakowicz1, Michał J. Okoniewski2 and Tomasz Gambin1,3,* 1Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, Warsaw 00-665, Poland, 2Scientific IT Services, ETH Zurich, Weinbergstrasse 11, Zurich 8092, Switzerland and 3Department of Medical Genetics, Institute of Mother and Child, Kasprzaka 17a, Warsaw 01-211, Poland *Corresponding author: Tel.: þ48693175804; Fax: þ48222346091; Email: [email protected] Citation details: Wiewiorka,M.S., Wysakowicz,D.P., Okoniewski,M.J. et al. Benchmarking distributed data warehouse so- lutions for storing genomic variant information. Database (2017) Vol. 2017: article ID bax049; doi:10.1093/database/bax049 Received 15 September 2016; Revised 4 April 2017; Accepted 29 May 2017 Abstract Genomic-based personalized medicine encompasses storing, analysing and interpreting genomic variants as its central issues. At a time when thousands of patientss sequenced exomes and genomes are becoming available, there is a growing need for efficient data- base storage and querying. -
Fast Foreign-Key Detection in Microsoft SQL
Fast Foreign-Key Detection in Microsoft SQL Server PowerPivot for Excel Zhimin Chen Vivek Narasayya Surajit Chaudhuri Microsoft Research Microsoft Research Microsoft Research [email protected] [email protected] [email protected] ABSTRACT stored in a relational database, which they can import into Excel. Microsoft SQL Server PowerPivot for Excel, or PowerPivot for Other sources of data are text files, web data feeds or in general any short, is an in-memory business intelligence (BI) engine that tabular data range imported into Excel. enables Excel users to interactively create pivot tables over large data sets imported from sources such as relational databases, text files and web data feeds. Unlike traditional pivot tables in Excel that are defined on a single table, PowerPivot allows analysis over multiple tables connected via foreign-key joins. In many cases however, these foreign-key relationships are not known a priori, and information workers are often not be sophisticated enough to define these relationships. Therefore, the ability to automatically discover foreign-key relationships in PowerPivot is valuable, if not essential. The key challenge is to perform this detection interactively and with high precision even when data sets scale to hundreds of millions of rows and the schema contains tens of tables and hundreds of columns. In this paper, we describe techniques for fast foreign-key detection in PowerPivot and experimentally evaluate its accuracy, performance and scale on both synthetic benchmarks and real-world data sets. These techniques have been incorporated into PowerPivot for Excel. Figure 1. Example of pivot table in Excel. It enables multi- dimensional analysis over a single table. -
Building an Effective Data Warehousing for Financial Sector
Automatic Control and Information Sciences, 2017, Vol. 3, No. 1, 16-25 Available online at http://pubs.sciepub.com/acis/3/1/4 ©Science and Education Publishing DOI:10.12691/acis-3-1-4 Building an Effective Data Warehousing for Financial Sector José Ferreira1, Fernando Almeida2, José Monteiro1,* 1Higher Polytechnic Institute of Gaya, V.N.Gaia, Portugal 2Faculty of Engineering of Oporto University, INESC TEC, Porto, Portugal *Corresponding author: [email protected] Abstract This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a business intelligence system that, in a simple, quick but also versatile way, allows the access to updated, aggregated, real and/or projected information, regarding bank account balances. The established system extracts and processes the operational database information which supports cash management information by using Integration Services and Analysis Services tools from Microsoft SQL Server. The end-user interface is a pivot table, properly arranged to explore the information available by the produced cube. The results have shown that the adoption of online analytical processing cubes offers better performance and provides a more automated and robust process to analyze current and provisional aggregated financial data balances compared to the current process based on static reports built from transactional databases. Keywords: data warehouse, OLAP cube, data analysis, information system, business intelligence, pivot tables Cite This Article: José Ferreira, Fernando Almeida, and José Monteiro, “Building an Effective Data Warehousing for Financial Sector.” Automatic Control and Information Sciences, vol. -
What Is OLAP (Online Analytical Processing): Cube, Operations & Types What Is Online Analytical Processing?
What is OLAP (Online Analytical Processing): Cube, Operations & Types What is Online Analytical Processing? OLAP is a category of software that allows users to analyze information from multiple database systems at the same time. It is a technology that enables analysts to extract and view business data from different points of view. OLAP stands for Online Analytical Processing. Analysts frequently need to group, aggregate and join data. These operations in relational databases are resource intensive. With OLAP data can be pre-calculated and pre-aggregated, making analysis faster. OLAP databases are divided into one or more cubes. The cubes are designed in such a way that creating and viewing reports become easy. OLAP cube: Ahmed Yasir Khan Page 1 of 12 At the core of the OLAP, concept is an OLAP Cube. The OLAP cube is a data structure optimized for very quick data analysis. The OLAP Cube consists of numeric facts called measures which are categorized by dimensions. OLAP Cube is also called the hypercube. Usually, data operations and analysis are performed using the simple spreadsheet, where data values are arranged in row and column format. This is ideal for two- dimensional data. However, OLAP contains multidimensional data, with data usually obtained from a different and unrelated source. Using a spreadsheet is not an optimal option. The cube can store and analyze multidimensional data in a logical and orderly manner. How does it work? A Data warehouse would extract information from multiple data sources and formats like text files, excel sheet, multimedia files, etc. The extracted data is cleaned and transformed. -
SAS 9.1 OLAP Server: Administrator’S Guide, Please Send Them to Us on a Photocopy of This Page, Or Send Us Electronic Mail
SAS® 9.1 OLAP Server Administrator’s Guide The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2004. SAS ® 9.1 OLAP Server: Administrator’s Guide. Cary, NC: SAS Institute Inc. SAS® 9.1 OLAP Server: Administrator’s Guide Copyright © 2004, SAS Institute Inc., Cary, NC, USA All rights reserved. Produced in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. U.S. Government Restricted Rights Notice. Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227–19 Commercial Computer Software-Restricted Rights (June 1987). SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513. 1st printing, January 2004 SAS Publishing provides a complete selection of books and electronic products to help customers use SAS software to its fullest potential. For more information about our e-books, e-learning products, CDs, and hard-copy books, visit the SAS Publishing Web site at support.sas.com/pubs or call 1-800-727-3228. SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are registered trademarks or trademarks -
Improving Traveling Habits Using an OLAP Cube
Improving traveling habits using an OLAP cube Development of a business intelligence system Marcus Hellman Marcus Hellman Spring 2016 Master Thesis in Computing Science, 30 hp Supervisor: Anders Broberg Extern Supervisor: Kim Nilsson Examiner: Henrik Bjorklund¨ Umea˚ University, Department of Computing Science Abstract The aim of this thesis is to improve the traveling habits of clients using the SpaceTime system when arranging their travels. The improvement of traveling habits refers to lowering costs and emissions generated by the travels. To do this, a business intelligence system, including an OLAP cube, were created to provide the clients with feedback on how they travel. This to make it possible to see if they are improving and how much they have saved, both in money and emissions. Since these kind of systems often are quite complex, studies on best practices and how to keep such systems agile were performed to be able to provide a system of high quality. During this project, it was found that the pre-study and design phase were just as challenging as the creation of the designed components. The result of this project was a business intelligence system, including ETL, a Data warehouse, and an OLAP cube that will be used in the SpaceTime system as well as mock-ups presenting how data from the OLAP cube could be presented in the SpaceTime web-application in the future. Acknowledgements First, I would like to thank SpaceTime and Dohi for giving me the opportunity to do this the- sis. Also, I would like to thank my supervisors Anders Broberg and Kim Nilsson for all the help during this thesis project, providing valuable feedback on my thesis report throughout the project as well as guiding me through the introduction and development of the system. -
Constructing OLAP Cubes Based on Queries
Constructing OLAP Cubes Based on Queries Tapio Niemi Jyrki Nummenmaa Peter Thanisch Department of Computer and Department of Computer and Department of Computer Science, Information Sciences Information Sciences University of Edinburgh FIN-33014 University of Tampere FIN-33014 University of Tampere Edinburgh, EH9 3JZ Finland Finland Scotland +358 32156595 +358 405277999 +44 7968401525 [email protected] [email protected] [email protected] ABSTRACT The design of a cube is based on knowledge of the application area and the types of queries the users are expected to pose. An On-Line Analytical Processing (OLAP) user often follows a Since constructing an OLAP cube can be a difficult task for the train of thought, posing a sequence of related queries against the end user, it is often seen as the duty of the data warehouse data warehouse. Although their details are not known in advance, administrator. This has led to researchers and vendors regarding the general form of those queries is apparent beforehand. Thus, the OLAP cube as a static storage structure for data warehouse the user can outline the relevant portion of the data posing data. This is problematic since the user often wants to make new generalised queries against a cube representing the data kinds of queries, which may also need new OLAP cubes. The warehouse. same cube is not always practical for different analysis tasks, Since existing OLAP design methods are not suitable for non- since the structure of the cube has a notable effect on efficiency professionals, we present a technique that automates cube design and ease of posing queries.