Column Oriented Databases
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Cubes Documentation Release 1.0.1
Cubes Documentation Release 1.0.1 Stefan Urbanek April 07, 2015 Contents 1 Getting Started 3 1.1 Introduction.............................................3 1.2 Installation..............................................5 1.3 Tutorial................................................6 1.4 Credits................................................9 2 Data Modeling 11 2.1 Logical Model and Metadata..................................... 11 2.2 Schemas and Models......................................... 25 2.3 Localization............................................. 38 3 Aggregation, Slicing and Dicing 41 3.1 Slicing and Dicing.......................................... 41 3.2 Data Formatters........................................... 45 4 Analytical Workspace 47 4.1 Analytical Workspace........................................ 47 4.2 Authorization and Authentication.................................. 49 4.3 Configuration............................................. 50 5 Slicer Server and Tool 57 5.1 OLAP Server............................................. 57 5.2 Server Deployment.......................................... 70 5.3 slicer - Command Line Tool..................................... 71 6 Backends 77 6.1 SQL Backend............................................. 77 6.2 MongoDB Backend......................................... 89 6.3 Google Analytics Backend...................................... 90 6.4 Mixpanel Backend.......................................... 92 6.5 Slicer Server............................................. 94 7 Recipes 97 7.1 Recipes............................................... -
Query All Tables in a Schema
Query All Tables In A Schema Orchidaceous and unimpressionable Thor often air-dried some iceberg imperially or amortizing knee-high. Dotier Griffin smatter blindly. Zionism Danie industrializing her interlay so opaquely that Timmie exserts very fustily. Redshift Show Tables How your List Redshift Tables FlyData. How to query uses mutexes will only queried data but what is fine and built correctly: another advantage we can easily access a string. Exception to query below queries to list of. 1 Do amount of emergency following Select Tools List Tables On the toolbar click 2 In the. How can easily access their business. SQL to Search for her VALUE data all COLUMNS of all TABLES in. This system table has the user has a string value? Search path for improving our knowledge and dedicated professional with ai model for redshift list all constraints, views using schemas. Sqlite_temp_schema works without loop. True if you might cause all. Optional message bit after finishing an awesome blog. Easy way are a list all objects have logs all databases do you can be logged in lowercase, fully managed automatically by default description form. How do not running sap, and sizes of all object privileges granted, i understood you all redshift of how about data professional with sqlite? Any questions or. The following research will bowl the T-SQL needed to change every rule change the WHERE clause define the schema you need and replace. Lists all of schema name is there you can be specified on other roles held by email and systems still safe even following command? This data scientist, thanx for schemas that you learn from sysindexes as sqlite. -
A Platform for Networked Business Analytics BUSINESS INTELLIGENCE
BROCHURE A platform for networked business analytics BUSINESS INTELLIGENCE Infor® Birst's unique networked business analytics technology enables centralized and decentralized teams to work collaboratively by unifying Leveraging Birst with existing IT-managed enterprise data with user-owned data. Birst automates the enterprise BI platforms process of preparing data and adds an adaptive user experience for business users that works across any device. Birst networked business analytics technology also enables customers to This white paper will explain: leverage and extend their investment in ■ Birst’s primary design principles existing legacy business intelligence (BI) solutions. With the ability to directly connect ■ How Infor Birst® provides a complete unified data and analytics platform to Oracle Business Intelligence Enterprise ■ The key elements of Birst’s cloud architecture Edition (OBIEE) semantic layer, via ODBC, ■ An overview of Birst security and reliability. Birst can map the existing logical schema directly into Birst’s logical model, enabling Birst to join this Enterprise Data Tier with other data in the analytics fabric. Birst can also map to existing Business Objects Universes via web services and Microsoft Analysis Services Cubes and Hyperion Essbase cubes via MDX and extend those schemas, enabling true self-service for all users in the enterprise. 61% of Birst’s surveyed reference customers use Birst as their only analytics and BI standard.1 infor.com Contents Agile, governed analytics Birst high-performance in the era of -
Exasol User Manual Version 6.0.8
Exasol User Manual Version 6.0.8 Empowering analytics. Experience the world´s fastest, most intelligent, in-memory analytics database. Copyright © 2018 Exasol AG. All rights reserved. The information in this publication is subject to change without notice. EXASOL SHALL NOT BE HELD LIABLE FOR TECHNICAL OR EDITORIAL ERRORS OR OMISSIONS CONTAINED HEREIN NOR FOR ACCIDENTAL OR CONSEQUENTIAL DAMAGES RES- ULTING FROM THE FURNISHING, PERFORMANCE, OR USE OF. No part of this publication may be photocopied or reproduced in any form without prior written consent from Exasol. All named trademarks and registered trademarks are the property of their respective owners. Exasol User Manual Table of Contents Foreword ..................................................................................................................................... ix Conventions ................................................................................................................................. xi Changes in Version 6.0 ................................................................................................................. xiii 1. What is Exasol? .......................................................................................................................... 1 2. SQL reference ............................................................................................................................ 5 2.1. Basic language elements .................................................................................................... 5 2.1.1. Comments -
SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N
SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N. Hanson, Artem Oks, Susan L. Price, Srikumar Rangarajan, Aleksandras Surna, Qingqing Zhou Microsoft {palarson, ciprianc, ehans, artemoks, susanpr, srikumar, asurna, qizhou}@microsoft.com ABSTRACT SQL Server column store indexes are “pure” column stores, not a The SQL Server 11 release (code named “Denali”) introduces a hybrid, because they store all data for different columns on new data warehouse query acceleration feature based on a new separate pages. This improves I/O scan performance and makes index type called a column store index. The new index type more efficient use of memory. SQL Server is the first major combined with new query operators processing batches of rows database product to support a pure column store index. Others greatly improves data warehouse query performance: in some have claimed that it is impossible to fully incorporate pure column cases by hundreds of times and routinely a tenfold speedup for a store technology into an established database product with a broad broad range of decision support queries. Column store indexes are market. We’re happy to prove them wrong! fully integrated with the rest of the system, including query To improve performance of typical data warehousing queries, all a processing and optimization. This paper gives an overview of the user needs to do is build a column store index on the fact tables in design and implementation of column store indexes including the data warehouse. It may also be beneficial to build column enhancements to query processing and query optimization to take store indexes on extremely large dimension tables (say more than full advantage of the new indexes. -
Business Intelligence and Column-Oriented Databases
Central____________________________________________________________________________________________________ European Conference on Information and Intelligent Systems Page 12 of 344 Business Intelligence and Column-Oriented Databases Kornelije Rabuzin Nikola Modrušan Faculty of Organization and Informatics NTH Mobile, University of Zagreb Međimurska 28, 42000 Varaždin, Croatia Pavlinska 2, 42000 Varaždin, Croatia [email protected] [email protected] Abstract. In recent years, NoSQL databases are popular document-oriented database systems is becoming more and more popular. We distinguish MongoDB. several different types of such databases and column- oriented databases are very important in this context, for sure. The purpose of this paper is to see how column-oriented databases can be used for data warehousing purposes and what the benefits of such an approach are. HBase as a data management Figure 1. JSON object [15] system is used to store the data warehouse in a column-oriented format. Furthermore, we discuss Graph databases, on the other hand, rely on some how star schema can be modelled in HBase. segment of the graph theory. They are good to Moreover, we test the performances that such a represent nodes (entities) and relationships among solution can provide and we compare them to them. This is especially suitable to analyze social relational database management system Microsoft networks and some other scenarios. SQL Server. Key value databases are important as well for a certain key you store (assign) a certain value. Keywords. Business Intelligence, Data Warehouse, Document-oriented databases can be treated as key Column-Oriented Database, Big Data, NoSQL value as long as you know the document id. Here we skip the details as it would take too much time to discuss different systems [21]. -
Replication at the Speed of Change – a Fast, Scalable Replication Solution for Near Real-Time HTAP Processing
Replication at the Speed of Change – a Fast, Scalable Replication Solution for Near Real-Time HTAP Processing Dennis Butterstein Daniel Martin Jia Zhong Lingyun Wang Knut Stolze Felix Beier IBM Silicon Valley Lab IBM Research & Development GmbH 555 Bailey Ave Schonaicher¨ Strasse 220 San Jose, CA 95141, United States 71032 Boblingen,¨ Germany [email protected] [email protected] [email protected] fdanmartin,stolze,[email protected] ABSTRACT engine was first replaced with Netezza (IBM PureData Sys- 2 The IBM Db2 Analytics Accelerator (IDAA) is a state- tem for Analytics ). Netezza's design is to always use table of-the art hybrid database system that seamlessly extends scans on all of its disks in parallel, leveraging FPGAs to ap- the strong transactional capabilities of Db2 for z/OS with ply decompression, projection and filtering operations before the very fast column-store processing in Db2 Warehouse. the data hits the main processors of the cluster nodes. The The Accelerator maintains a copy of the data from Db2 for row-major organized tables were hash-distributed across all z/OS in its backend database. Data can be synchronized nodes (and disks) based on selected columns (to facilitate at a single point in time with a granularity of a table, one co-located joins) or in a random fashion. The engine itself or more of its partitions, or incrementally as rows changed is optimized for table scans; besides Zone Maps there are no using replication technology. other structures (e. g., indices) that optimize the processing IBM Change Data Capture (CDC) has been employed as of predicates. -
A Peek Under the Hood
White paper Technical A Peek under the hood White paper Technical Contents A peek under the hood 01 02 03 Introduction 3 Being really fast 5 Providing a Great User Experience 12 Massively Parallel Processing MPP 5 Self-Optimization 13 Large-Scale In-Memory Advanced Analytics 04 Architecture 9 and Data Science 14 Supporting Business Integration Filters, Joins and Sorting 10 and Day-to-Day Operation 16 Query Optimizer and Interfaces and Tool Query Cache 11 Integration 17 Data Ingestion and Data Integration 18 The Virtual Schema Framework for Data Virtualization & 05 Hybrid Clouds 20 Summary 13 Fail Safety, Dual Data Center Operation and Backup/Restore 24 SQL Preprocessor 25 01 White paper Technical 3 Introduction Exasol was founded in founders recognized that database designed specifically Nuremberg, Germany, in the year new opportunities were made for analytics. Exasol holds 2000 – a time when two trends possible by these trends. With performance records in the in hardware were starting to RAM falling in cost and rising in TPC-H online transaction emerge: capacity and cluster computing processing benchmark from being merely a commodity, it the Transaction Processing Major improvements in was now conceivable to apply Performance Council (TPC) for processing power were no the principles and architectures decision-support databases, longer coming from ever of high-performance computing outperforming competitors by increasing clock speeds of to database design. In the years orders of magnitudes and scaling central processing units (CPUs), that followed, the company up to hundreds of terabytes of but instead from parallel and exclusively focused on delivering data. distributed systems. -
EXASOL AG Our History – Inventing World’S Fastest In-Memory Database
The Most Powerful In-memory Analytic Database Introduction @ Sphinx IT in Vienna 25.11.2016 © 2016 EXASOL AG Our history – inventing world’s fastest in-memory database 2008 2012 Record in Inclusion in Gartner’s 2000 TPC-H Benchmark „Magic Quadrant for Company foundation („Oracle dethroned“) Data Management Systems“ 90ies 2006 2010 2014 early Research Success Pilot Customer Karstadt- Most Successful Vendor of Successful global expansion, (University Erlangen- Quelle uses EXASolution in analytical database systems in 400+ customers across 12 countries Nürnberg) Production Germany (BARC) 100TB TPC-H benchmark © 2016 EXASOL AG Great recognition in the market 2016 © 2016 EXASOL AG What Gartner says about EXASOL “EXASOL is a prime example of what Gartner considers to be the future of DBMS” Source: Gartner © 2016 EXASOL AG Why would you be looking for a new Database Performance/ Pricing Issues with New Requests for Existing DWH agile or predictive Analytics Changing Plattforms or Regulatory Issues growing DataSources © 2016 EXASOL AG King: leading interactive entertainment company . Analyzes customer behavior . Optimizes game revenues . Lots and lots of data . 100 million daily active users . 1 billion game plays per day . 10 billion events per day EXASOL database: 200TB © 2016 EXASOL AG Zalando: Rising star of e-commerce . Online fashion retailer with 14m+ customers across Europe . 150,000 products available online . EXASOL complements DWH to enable fast analytics and reporting . Database optimizes stock availability, returns process and targeted marketing EXASOL database: 15TB © 2016 EXASOL AG Adidas: CRM . Several projects in different regions (Europe, USA, Russia) . Agile BI -> flexible reporting functionality for quick projects . BW on HANA: “Cruise liner” . -
CDW: a Conceptual Overview 2017
CDW: A Conceptual Overview 2017 by Margaret Gonsoulin, PhD March 29, 2017 Thanks to: • Richard Pham, BISL/CDW for his mentorship • Heidi Scheuter and Hira Khan for organizing this session 3 Poll #1: Your CDW experience • How would you describe your level of experience with CDW data? ▫ 1- Not worked with it at all ▫ 2 ▫ 3 ▫ 4 ▫ 5- Very experienced with CDW data Agenda for Today • Get to the bottom of all of those acronyms! • Learn to think in “relational data” terms • Become familiar with the components of CDW ▫ Production and Raw Domains ▫ Fact and Dimension tables/views • Understand how to create an analytic dataset ▫ Primary and Foreign Keys ▫ Joining tables/views Agenda for Today • Get to the bottom of all of those acronyms! • Learn to think in “relational data” terms • Become familiar with the components of CDW ▫ Production and Raw Domains ▫ Fact and Dimension tables/views • Creating an analytic dataset ▫ Primary and Foreign Keys ▫ Joining tables/views “C”DW, “R”DW & “V”DW • Users will see documentation referring to xDW. • The “x” is a variable waiting to be filled in with either: ▫ “V” for VISN, ▫ “R” for region or ▫ “C” for corporate (meaning “national VHA”) • Each organizational level of the VA has its own data warehouse focusing on its own population. • This talk focuses on CDW only. 7 Flow of data into the warehouse VistA = Veterans Health Information Systems and Technology Architecture C“DW” • The “DW” in CDW stands for “Data Warehouse.” • Data Warehouse = a data delivery system intended to give users the information they need to support their business decisions. -
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. -
Integrating Compression and Execution in Column-Oriented Database Systems
Integrating Compression and Execution in Column-Oriented Database Systems Daniel J. Abadi Samuel R. Madden Miguel C. Ferreira MIT MIT MIT [email protected] [email protected] [email protected] ABSTRACT commercial arena [21, 1, 19], we believe the time is right to Column-oriented database system architectures invite a re- systematically revisit the topic of compression in the context evaluation of how and when data in databases is compressed. of these systems, particularly given that one of the oft-cited Storing data in a column-oriented fashion greatly increases advantages of column-stores is their compressibility. the similarity of adjacent records on disk and thus opportuni- Storing data in columns presents a number of opportuni- ties for compression. The ability to compress many adjacent ties for improved performance from compression algorithms tuples at once lowers the per-tuple cost of compression, both when compared to row-oriented architectures. In a column- in terms of CPU and space overheads. oriented database, compression schemes that encode multi- In this paper, we discuss how we extended C-Store (a ple values at once are natural. In a row-oriented database, column-oriented DBMS) with a compression sub-system. We such schemes do not work as well because an attribute is show how compression schemes not traditionally used in row- stored as a part of an entire tuple, so combining the same oriented DBMSs can be applied to column-oriented systems. attribute from different tuples together into one value would We then evaluate a set of compression schemes and show that require some way to \mix" tuples.