Study Notes for DB Design and Management Exam 1 (Chapters 1-2-3)
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Aptum Database Administration Program September 2019 Overview
SERVICE GUIDE APTUM DATABASE ADMINISTRATION PROGRAM SEPTEMBER 2019 OVERVIEW Aptum’s Database Administration (DBA) Program was designed to DBA Plan hours can be leveraged for all database-related activities help our customers reduce the cost of normal operation and the risk of detailed within this document, up to the allocation purchased. If you application and database downtime. require more hours than are purchased in a plan for a given month, any excess will be billed on an hourly basis at the standard Database Customers can work with Aptum’s Solutions Engineers and Certified Administration rate. Database Administrators to obtain assistance with everything from database engine configuration, performance optimization, clustering Included Database Platforms: administration, and replication administration. Microsoft SQL Server To help you achieve your performance goals, we offer our DBA plans in MySQL/MariaDB/Percona block-hour increments: 4 hours* * Hours are monthly and renew at the 1st of each calendar month and do not roll over. Overage rates are applicable only after block hours are consumed. All hours are 8 hours* applicable to the entire solution not per server. 12 hours* 20 hours* THE NEED FOR DATABASE ADMINISTRATION Most applications—especially those that support enterprise processes enable e-commerce, or facilitate collaboration—are database-intensive and demand optimal performance from the database. Applications and database queries may utilize very lean and elegant code, but the structure and configuration of your databases can be a detriment to high performance. There are dozens, sometimes hundreds, of configuration settings that need to be optimally tuned to enhance database performance. When the database is installed, most configuration defaults are applied automatically. -
Schema in Database Sql Server
Schema In Database Sql Server Normie waff her Creon stringendo, she ratten it compunctiously. If Afric or rostrate Jerrie usually files his terrenes shrives wordily or supernaturalized plenarily and quiet, how undistinguished is Sheffy? Warring and Mahdi Morry always roquet impenetrably and barbarizes his boskage. Schema compare tables just how the sys is a table continues to the most out longer function because of the connector will often want to. Roles namely actors in designer slow and target multiple teams together, so forth from sql management. You in sql server, should give you can learn, and execute this is a location of users: a database projects, or more than in. Your sql is that the view to view of my data sources with the correct. Dive into the host, which objects such a set of lock a server database schema in sql server instance of tables under the need? While viewing data in sql server database to use of microseconds past midnight. Is sql server is sql schema database server in normal circumstances but it to use. You effectively structure of the sql database objects have used to it allows our policy via js. Represents table schema in comparing new database. Dml statement as schema in database sql server functions, and so here! More in sql server books online schema of the database operator with sql server connector are not a new york, with that object you will need. This in schemas and history topic names are used to assist reporting from. Sql schema table as views should clarify log reading from synonyms in advance so that is to add this game reports are. -
ACS-3902 Ron Mcfadyen Slides Are Based on Chapter 5 (7Th Edition)
ACS-3902 Ron McFadyen Slides are based on chapter 5 (7th edition) (chapter 3 in 6th edition) ACS-3902 1 The Relational Data Model and Relational Database Constraints • Relational model – Ted Codd (IBM) 1970 – First commercial implementations available in early 1980s – Widely used ACS-3902 2 Relational Model Concepts • Database is a collection of relations • Implementation of relation: table comprising rows and columns • In practice a table/relation represents an entity type or relationship type (entity-relationship model … later) • At intersection of a row and column in a table there is a simple value • Row • Represents a collection of related data values • Formally called a tuple • Column names • Columns may be referred to as fields, or, formally as attributes • Values in a column are drawn from a domain of values associated with the column/field/attribute ACS-3902 3 Relational Model Concepts 7th edition Figure 5.1 ACS-3902 4 Domains • Domain – Atomic • A domain is a collection of values where each value is indivisible • Not meaningful to decompose further – Specifying a domain • Name, data type, rules – Examples • domain of department codes for UW is a list: {“ACS”, “MATH”, “ENGL”, “HIST”, etc} • domain of gender values for UW is the list (“male”, “female”) – Cardinality: number of values in a domain – Database implementation & support vary ACS-3902 5 Domain example - PostgreSQL CREATE DOMAIN posint AS integer CHECK (VALUE > 0); CREATE TABLE mytable (id posint); INSERT INTO mytable VALUES(1); -- works INSERT INTO mytable VALUES(-1); -- fails https://www.postgresql.org/docs/current/domains.html ACS-3902 6 Domain example - PostgreSQL CREATE DOMAIN domain_code_type AS character varying NOT NULL CONSTRAINT domain_code_type_check CHECK (VALUE IN ('ApprovedByAdmin', 'Unapproved', 'ApprovedByEmail')); CREATE TABLE codes__domain ( code_id integer NOT NULL, code_type domain_code_type NOT NULL, CONSTRAINT codes_domain_pk PRIMARY KEY (code_id) ) ACS-3902 7 Relation • Relation schema R – Name R and a list of attributes: • Denoted by R (A1, A2, ...,An) • E.g. -
Informal Data Transformation Considered Harmful
Informal Data Transformation Considered Harmful Eric Daimler, Ryan Wisnesky Conexus AI out the enterprise, so that data and programs that depend on that data need not constantly be re-validated for ev- ery particular use. Computer scientists have been develop- ing techniques for preserving data integrity during trans- formation since the 1970s (Doan, Halevy, and Ives 2012); however, we agree with the authors of (Breiner, Subrah- manian, and Jones 2018) and others that these techniques are insufficient for the practice of AI and modern IT sys- tems integration and we describe a modern mathematical approach based on category theory (Barr and Wells 1990; Awodey 2010), and the categorical query language CQL2, that is sufficient for today’s needs and also subsumes and unifies previous approaches. 1.1 Outline To help motivate our approach, we next briefly summarize an application of CQL to a data science project undertaken jointly with the Chemical Engineering department of Stan- ford University (Brown, Spivak, and Wisnesky 2019). Then, in Section 2 we review data integrity and in Section 3 we re- view category theory. Finally, we describe the mathematics of our approach in Section 4, and conclude in Section 5. We present no new results, instead citing a line of work summa- rized in (Schultz, Spivak, and Wisnesky 2017). Image used under a creative commons license; original 1.2 Motivating Case Study available at http://xkcd.com/1838/. In scientific practice, computer simulation is now a third pri- mary tool, alongside theory and experiment. Within -
(DDL) Reference Manual
Data Definition Language (DDL) Reference Manual Abstract This publication describes the DDL language syntax and the DDL dictionary database. The audience includes application programmers and database administrators. Product Version DDL D40 DDL H01 Supported Release Version Updates (RVUs) This publication supports J06.03 and all subsequent J-series RVUs, H06.03 and all subsequent H-series RVUs, and G06.26 and all subsequent G-series RVUs, until otherwise indicated by its replacement publications. Part Number Published 529431-003 May 2010 Document History Part Number Product Version Published 529431-002 DDL D40, DDL H01 July 2005 529431-003 DDL D40, DDL H01 May 2010 Legal Notices Copyright 2010 Hewlett-Packard Development Company L.P. Confidential computer software. Valid license from HP required for possession, use or copying. Consistent with FAR 12.211 and 12.212, Commercial Computer Software, Computer Software Documentation, and Technical Data for Commercial Items are licensed to the U.S. Government under vendor's standard commercial license. The information contained herein is subject to change without notice. The only warranties for HP products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein. Export of the information contained in this publication may require authorization from the U.S. Department of Commerce. Microsoft, Windows, and Windows NT are U.S. registered trademarks of Microsoft Corporation. Intel, Itanium, Pentium, and Celeron are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries. -
Database Administrator
Database Administrator Purpose: DGC is looking for a passionate Database Administrator to help support our platform, delivering the content of choice for casino operators and their players. You will be working in a small but high performing IT team to create something special. Online gaming is set to be one of the fastest growing industries in the US as regulation allows online gaming to expand into the various states. Our RGS platform is designed for fast and efficient deployments and seamless integration into a high performing library of online casino games. You will need to assist with the hosting, management and updating of this technology to ensure our success. As an DBA you will implement, design and improve processes relating to the administration of databases to ensure that they function correctly, perform optimally, preserve data and facilitate revenue generation. This role forms part of a rapidly expanding team which will require the ability to support a fast growing infrastructure and customer base. Duties include, but not limited to: • Set and maintain operational database standards on an ongoing basis • Develop and maintain OLAP environments • Develop and maintain OLTP environments • Develop and maintain ETL processes • Enforce and improve database integrity and performance using sound design principles and implementation of database design standards • Design and enforce data security policies to eliminate unauthorised access to data on managed data systems in accordance with IT Services technical specifications and business requirements • Ensure that effective data redundancy; archiving, backup and recovery mechanisms are in place to prevent the loss of data • Set up configurable pre-established jobs to automatically run daily in order to monitor and maintain the operational databases • Provide 24-hour standby support by being available on a 24/7 basis during specified periods This job description is not intended to be an exhaustive list of responsibilities. -
2. Creating a Database Designing the Database Schema
2. Creating a database Designing the database schema ..................................................................................... 1 Representing Classes, Attributes and Objects ............................................................. 2 Data types .......................................................................................................................... 5 Additional constraints ...................................................................................................... 6 Choosing the right fields ................................................................................................. 7 Implementing a table in SQL ........................................................................................... 7 Inserting data into a table ................................................................................................ 8 Primary keys .................................................................................................................... 10 Representing relationships ........................................................................................... 12 Altering a table ................................................................................................................ 22 Designing the database schema As you have seen, once the data model for a system has been designed, you need to work out how to represent that model in a relational database. This representation is sometimes referred to as the database schema. In a relational database, the schema defines -
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. -
Automating the Capture of Data Transformations from Statistical Scripts in Data Documentation Jie Song George Alter H
C2Metadata: Automating the Capture of Data Transformations from Statistical Scripts in Data Documentation Jie Song George Alter H. V. Jagadish University of Michigan University of Michigan University of Michigan Ann Arbor, Michigan Ann Arbor, Michigan Ann Arbor, Michigan [email protected] [email protected] [email protected] ABSTRACT CCS CONCEPTS Datasets are often derived by manipulating raw data with • Information systems → Data provenance; Extraction, statistical software packages. The derivation of a dataset transformation and loading. must be recorded in terms of both the raw input and the ma- nipulations applied to it. Statistics packages typically provide KEYWORDS limited help in documenting provenance for the resulting de- data transformation, data documentation, data provenance rived data. At best, the operations performed by the statistical ACM Reference Format: package are described in a script. Disparate representations Jie Song, George Alter, and H. V. Jagadish. 2019. C2Metadata: Au- make these scripts hard to understand for users. To address tomating the Capture of Data Transformations from Statistical these challenges, we created Continuous Capture of Meta- Scripts in Data Documentation. In 2019 International Conference data (C2Metadata), a system to capture data transformations on Management of Data (SIGMOD ’19), June 30-July 5, 2019, Am- in scripts for statistical packages and represent it as metadata sterdam, Netherlands. ACM, New York, NY, USA, 4 pages. https: in a standard format that is easy to understand. We do so by //doi.org/10.1145/3299869.3320241 devising a Structured Data Transformation Algebra (SDTA), which uses a small set of algebraic operators to express a 1 INTRODUCTION large fraction of data manipulation performed in practice. -
When Relational-Based Applications Go to Nosql Databases: a Survey
information Article When Relational-Based Applications Go to NoSQL Databases: A Survey Geomar A. Schreiner 1,* , Denio Duarte 2 and Ronaldo dos Santos Mello 1 1 Departamento de Informática e Estatística, Federal University of Santa Catarina, 88040-900 Florianópolis - SC, Brazil 2 Campus Chapecó, Federal University of Fronteira Sul, 89815-899 Chapecó - SC, Brazil * Correspondence: [email protected] Received: 22 May 2019; Accepted: 12 July 2019; Published: 16 July 2019 Abstract: Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution. Keywords: big data; data interoperability; NoSQL databases; relational-to-NoSQL mapping 1. -
Relational Database Design Chapter 7
Chapter 7: Relational Database Design Chapter 7: Relational Database Design First Normal Form Pitfalls in Relational Database Design Functional Dependencies Decomposition Boyce-Codd Normal Form Third Normal Form Multivalued Dependencies and Fourth Normal Form Overall Database Design Process Database System Concepts 7.2 ©Silberschatz, Korth and Sudarshan 1 First Normal Form Domain is atomic if its elements are considered to be indivisible units + Examples of non-atomic domains: Set of names, composite attributes Identification numbers like CS101 that can be broken up into parts A relational schema R is in first normal form if the domains of all attributes of R are atomic Non-atomic values complicate storage and encourage redundant (repeated) storage of data + E.g. Set of accounts stored with each customer, and set of owners stored with each account + We assume all relations are in first normal form (revisit this in Chapter 9 on Object Relational Databases) Database System Concepts 7.3 ©Silberschatz, Korth and Sudarshan First Normal Form (Contd.) Atomicity is actually a property of how the elements of the domain are used. + E.g. Strings would normally be considered indivisible + Suppose that students are given roll numbers which are strings of the form CS0012 or EE1127 + If the first two characters are extracted to find the department, the domain of roll numbers is not atomic. + Doing so is a bad idea: leads to encoding of information in application program rather than in the database. Database System Concepts 7.4 ©Silberschatz, Korth and Sudarshan 2 Pitfalls in Relational Database Design Relational database design requires that we find a “good” collection of relation schemas. -
MDA Process to Extract the Data Model from Document-Oriented Nosql Database
MDA Process to Extract the Data Model from Document-oriented NoSQL Database Amal Ait Brahim, Rabah Tighilt Ferhat and Gilles Zurfluh Toulouse Institute of Computer Science Research (IRIT), Toulouse Capitole University, Toulouse, France Keywords: Big Data, NoSQL, Model Extraction, Schema Less, MDA, QVT. Abstract: In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property "schema less" which means absence of the data model when creating a database. This property brings an undeniable flexibility by allowing the evolution of the model during the exploitation of the base. However, query expression requires a precise knowledge of the data model. In this article, we propose a process to automatically extract the physical model from a document- oriented NoSQL database. To do this, we use the Model Driven Architecture (MDA) that provides a formal framework for automatic model transformation. From a NoSQL database, we propose formal transformation rules with QVT to generate the physical model. An experimentation of the extraction process was performed on the case of a medical application. 1 INTRODUCTION however that it is absent in some systems such as Cassandra and Riak TS. The "schema less" property Recently, there has been an explosion of data offers undeniable flexibility by allowing the model to generated and accumulated by more and more evolve easily. For example, the addition of new numerous and diversified computing devices. attributes in an existing line is done without Databases thus constituted are designated by the modifying the other lines of the same type previously expression "Big Data" and are characterized by the stored; something that is not possible with relational so-called "3V" rule (Chen, 2014).