Databases Course 1 PDF

DATABASES COURSE 1 Associate Professor ANDA BELCIU, PhD [email protected] Office: 2017 C  Additional information: bd.ase.ro -> Databases, online.ase.ro  About examination: - 50% seminar work (30% project - mandatory, 5...

DATABASES COURSE 1 Associate Professor ANDA BELCIU, PhD [email protected] Office: 2017 C  Additional information: bd.ase.ro -> Databases, online.ase.ro  About examination: - 50% seminar work (30% project - mandatory, 50% test, 20% activity) - 50% exam – multiple answers questions + SQL exercises - min. 5 at both of them About re-examination: -The re-examination exam can be taken, even if the seminar grade bonus for the seminar  Associate Professor Anda Belciu, PhD (groups 1072, 1076)  Laurențiu Dincă, PhD-candidate (groups 1074, 1075)  Alin Văduva, PhD-candidate (group 1073) Databases – teaching staff  C.J.Date , An introduction to database systems, Addison Wesley, 2004, USA.  R.Elmasri, S.Navathe, Fundamentals of database systems, 7th edition, Pearson, 2016, USA.  I. Lungu et al., Baze de date. Organizare, proiectare și implementare (Databases. Organization, design and implementation), ASE Publishing House, Second Edition, 2015, Romania.  A. Belciu, Introduction to Databases, ASE Publishing House, 2016, Romania. Bibliography  DB theory  Organizing data in external memory  Relational databases  SQL language  Methodology for developing a database  Indexing  Clusters management in relational databases  Queries optimization  NoSQL DB and other types of databases Course structure Data  Example: Information Employee Smith has 40 customers. Knowledge Employee Smith has 40 customers, while the other employees have an average of 20 customers. Insight: 40 vs 20 customers Wisdom Employee Smith will have a salary raise.  Volume – huge  Velocity – high speed  Variety – unstructured  Variability – the meaning of data is constantly changing  Veracity – uncertainty of data  Visualisation – through charts & graphs  Value – adds value to the business  The 17 V’s Of Big Data BIG DATA  Databases – Definitions  Database (DB) = a set of data that is organized, coherent, structured, in terms of minimum redundancy and control, accessible to multiple users in a useful time.  Database = a set of data collection that are in interdependence, along with the data description and the links between them. Chapter I – Database theory  ◦ Evolution  In a computer, data is stored in the internal memory (temporarily) and in the external memory (persistent).  In external memory, the way the data storage evolved has been established by: access to data can be made as quickly and as easily as possible; storing a large amount of data; increasing data complexity; improving the equipment for collecting, storing, transmitting and processing the data.  Databases, seen as a way of organizing data in external memory, have evolved from files through a process of their integration (the files and links between them), and taking into account the related applications. ◦ Generations:  first = hierarchical and network DB (until the late 70s);  second = relational DB (late '70s until now);  third = object-oriented DB (late 80s and up to now). ◦ Advanced DB: spatial, MM, mobile, NoSQL (distributed DB, Cloud DB, Graph DB) ◦ Distributed DB vs Centralized DB ◦ Network DB (fixed record with a predefined set of fields) vs Graph DB (arbitrary key/value pairs on nodes/vertices and relations/edges).  For this network structure a set type is: the owner node DEPARTMENT and PERSON as a member node.  A realization of this set type can be:  DEPARTMENT 100 IT 001  PERSON 005 POPA ANDA MOŞILOR, 22  PERSON 010 PETRE ION EMINESCU, 10 Network  … DB The correspondence between a row in Students table and the specific node (vertex) from the graph, which has properties and values indicated by standard key-value pairs. Graph DB representation  Type of data organizing Data structure Data Files -> is kept in programs -> is kept in the main file (on disk) -> C example: -> extension example:.txt File * f1; Struct employee { int id; char name; …}; DB -> is kept in DB dictionary -> is kept separately in a (on disk) through metadata disk file -> Oracle example: -> extension example:.dbf create database db1; create table employees (id number (5), name varchar2(20)…); DB components Range of values ​= the set of possible values ​for a feature (e.g. possible colors for a car). Feature (attribute) = defining and describing a particular aspect (property) of a real- world entity (e.g. car brand). Family of features = all characteristics that refer to the same real-world entity (e.g. all characteristics that can describe a car {number, brand, capacity, color}). Data collection (entity) = a family of characteristics on which a predicate applies (leading to an order relation between the characteristics and to obtaining information with a certain meaning) which is affecting some connections. Database vs. file system