ERM to Relational Model Kathleen Durant CS 3200 Lesson 3 Outline for Today
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Not ACID, Not BASE, but SALT a Transaction Processing Perspective on Blockchains
Not ACID, not BASE, but SALT A Transaction Processing Perspective on Blockchains Stefan Tai, Jacob Eberhardt and Markus Klems Information Systems Engineering, Technische Universitat¨ Berlin fst, je, [email protected] Keywords: SALT, blockchain, decentralized, ACID, BASE, transaction processing Abstract: Traditional ACID transactions, typically supported by relational database management systems, emphasize database consistency. BASE provides a model that trades some consistency for availability, and is typically favored by cloud systems and NoSQL data stores. With the increasing popularity of blockchain technology, another alternative to both ACID and BASE is introduced: SALT. In this keynote paper, we present SALT as a model to explain blockchains and their use in application architecture. We take both, a transaction and a transaction processing systems perspective on the SALT model. From a transactions perspective, SALT is about Sequential, Agreed-on, Ledgered, and Tamper-resistant transaction processing. From a systems perspec- tive, SALT is about decentralized transaction processing systems being Symmetric, Admin-free, Ledgered and Time-consensual. We discuss the importance of these dual perspectives, both, when comparing SALT with ACID and BASE, and when engineering blockchain-based applications. We expect the next-generation of decentralized transactional applications to leverage combinations of all three transaction models. 1 INTRODUCTION against. Using the admittedly contrived acronym of SALT, we characterize blockchain-based transactions There is a common belief that blockchains have the – from a transactions perspective – as Sequential, potential to fundamentally disrupt entire industries. Agreed, Ledgered, and Tamper-resistant, and – from Whether we are talking about financial services, the a systems perspective – as Symmetric, Admin-free, sharing economy, the Internet of Things, or future en- Ledgered, and Time-consensual. -
Data Analysis Expressions (DAX) in Powerpivot for Excel 2010
Data Analysis Expressions (DAX) In PowerPivot for Excel 2010 A. Table of Contents B. Executive Summary ............................................................................................................................... 3 C. Background ........................................................................................................................................... 4 1. PowerPivot ...............................................................................................................................................4 2. PowerPivot for Excel ................................................................................................................................5 3. Samples – Contoso Database ...................................................................................................................8 D. Data Analysis Expressions (DAX) – The Basics ...................................................................................... 9 1. DAX Goals .................................................................................................................................................9 2. DAX Calculations - Calculated Columns and Measures ...........................................................................9 3. DAX Syntax ............................................................................................................................................ 13 4. DAX uses PowerPivot data types ......................................................................................................... -
(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. -
Database Systems Administrator FLSA: Exempt
CLOVIS UNIFIED SCHOOL DISTRICT POSITION DESCRIPTION Position: Database Systems Administrator FLSA: Exempt Department/Site: Technology Services Salary Grade: 127 Reports to/Evaluated by: Chief Technology Officer Salary Schedule: Classified Management SUMMARY Gathers requirements and provisions servers to meet the district’s need for database storage. Installs and configures SQL Server according to the specifications of outside software vendors and/or the development group. Designs and executes a security scheme to assure safety of confidential data. Implements and manages a backup and disaster recovery plan. Monitors the health and performance of database servers and database applications. Troubleshoots database application performance issues. Automates monitoring and maintenance tasks. Maintains service pack deployment and upgrades servers in consultation with the developer group and outside vendors. Deploys and schedules SQL Server Agent tasks. Implements and manages replication topologies. Designs and deploys datamarts under the supervision of the developer group. Deploys and manages cubes for SSAS implementations. DISTINGUISHING CAREER FEATURES The Database Systems Administrator is a senior level analyst position requiring specialized education and training in the field of study typically resulting in a degree. Advancement to this position would require competency in the design and administration of relational databases designated for business, enterprise, and student information. Advancement from this position is limited to supervisory management positions. ESSENTIAL DUTIES AND RESPONSIBILITIES Implements, maintains and administers all district supported versions of Microsoft SQL as well as other DBMS as needed. Responsible for database capacity planning and involvement in server capacity planning. Responsible for verification of backups and restoration of production and test databases. Designs, implements and maintains a disaster recovery plan for critical data resources. -
Keys Are, As Their Name Suggests, a Key Part of a Relational Database
The key is defined as the column or attribute of the database table. For example if a table has id, name and address as the column names then each one is known as the key for that table. We can also say that the table has 3 keys as id, name and address. The keys are also used to identify each record in the database table . Primary Key:- • Every database table should have one or more columns designated as the primary key . The value this key holds should be unique for each record in the database. For example, assume we have a table called Employees (SSN- social security No) that contains personnel information for every employee in our firm. We’ need to select an appropriate primary key that would uniquely identify each employee. Primary Key • The primary key must contain unique values, must never be null and uniquely identify each record in the table. • As an example, a student id might be a primary key in a student table, a department code in a table of all departments in an organisation. Unique Key • The UNIQUE constraint uniquely identifies each record in a database table. • Allows Null value. But only one Null value. • A table can have more than one UNIQUE Key Column[s] • A table can have multiple unique keys Differences between Primary Key and Unique Key: • Primary Key 1. A primary key cannot allow null (a primary key cannot be defined on columns that allow nulls). 2. Each table can have only one primary key. • Unique Key 1. A unique key can allow null (a unique key can be defined on columns that allow nulls.) 2. -
CHAPTER 3 - Relational Database Modeling
DATABASE SYSTEMS Introduction to Databases and Data Warehouses, Edition 2.0 CHAPTER 3 - Relational Database Modeling Copyright (c) 2020 Nenad Jukic and Prospect Press MAPPING ER DIAGRAMS INTO RELATIONAL SCHEMAS ▪ Once a conceptual ER diagram is constructed, a logical ER diagram is created, and then it is subsequently mapped into a relational schema (collection of relations) Conceptual Model Logical Model Schema Jukić, Vrbsky, Nestorov, Sharma – Database Systems Copyright (c) 2020 Nenad Jukic and Prospect Press Chapter 3 – Slide 2 INTRODUCTION ▪ Relational database model - logical database model that represents a database as a collection of related tables ▪ Relational schema - visual depiction of the relational database model – also called a logical model ▪ Most contemporary commercial DBMS software packages, are relational DBMS (RDBMS) software packages Jukić, Vrbsky, Nestorov, Sharma – Database Systems Copyright (c) 2020 Nenad Jukic and Prospect Press Chapter 3 – Slide 3 INTRODUCTION Terminology Jukić, Vrbsky, Nestorov, Sharma – Database Systems Copyright (c) 2020 Nenad Jukic and Prospect Press Chapter 3 – Slide 4 INTRODUCTION ▪ Relation - table in a relational database • A table containing rows and columns • The main construct in the relational database model • Every relation is a table, not every table is a relation Jukić, Vrbsky, Nestorov, Sharma – Database Systems Copyright (c) 2020 Nenad Jukic and Prospect Press Chapter 3 – Slide 5 INTRODUCTION ▪ Relation - table in a relational database • In order for a table to be a relation the following conditions must hold: o Within one table, each column must have a unique name. o Within one table, each row must be unique. o All values in each column must be from the same (predefined) domain. -
Relational Query Languages
Relational Query Languages Universidad de Concepcion,´ 2014 (Slides adapted from Loreto Bravo, who adapted from Werner Nutt who adapted them from Thomas Eiter and Leonid Libkin) Bases de Datos II 1 Databases A database is • a collection of structured data • along with a set of access and control mechanisms We deal with them every day: • back end of Web sites • telephone billing • bank account information • e-commerce • airline reservation systems, store inventories, library catalogs, . Relational Query Languages Bases de Datos II 2 Data Models: Ingredients • Formalisms to represent information (schemas and their instances), e.g., – relations containing tuples of values – trees with labeled nodes, where leaves contain values – collections of triples (subject, predicate, object) • Languages to query represented information, e.g., – relational algebra, first-order logic, Datalog, Datalog: – tree patterns – graph pattern expressions – SQL, XPath, SPARQL Bases de Datos II 3 • Languages to describe changes of data (updates) Relational Query Languages Questions About Data Models and Queries Given a schema S (of a fixed data model) • is a given structure (FOL interpretation, tree, triple collection) an instance of the schema S? • does S have an instance at all? Given queries Q, Q0 (over the same schema) • what are the answers of Q over a fixed instance I? • given a potential answer a, is a an answer to Q over I? • is there an instance I where Q has an answer? • do Q and Q0 return the same answers over all instances? Relational Query Languages Bases de Datos II 4 Questions About Query Languages Given query languages L, L0 • how difficult is it for queries in L – to evaluate such queries? – to check satisfiability? – to check equivalence? • for every query Q in L, is there a query Q0 in L0 that is equivalent to Q? Bases de Datos II 5 Research Questions About Databases Relational Query Languages • Incompleteness, uncertainty – How can we represent incomplete and uncertain information? – How can we query it? . -
The Relational Data Model and Relational Database Constraints
chapter 33 The Relational Data Model and Relational Database Constraints his chapter opens Part 2 of the book, which covers Trelational databases. The relational data model was first introduced by Ted Codd of IBM Research in 1970 in a classic paper (Codd 1970), and it attracted immediate attention due to its simplicity and mathematical foundation. The model uses the concept of a mathematical relation—which looks somewhat like a table of values—as its basic building block, and has its theoretical basis in set theory and first-order predicate logic. In this chapter we discuss the basic characteristics of the model and its constraints. The first commercial implementations of the relational model became available in the early 1980s, such as the SQL/DS system on the MVS operating system by IBM and the Oracle DBMS. Since then, the model has been implemented in a large num- ber of commercial systems. Current popular relational DBMSs (RDBMSs) include DB2 and Informix Dynamic Server (from IBM), Oracle and Rdb (from Oracle), Sybase DBMS (from Sybase) and SQLServer and Access (from Microsoft). In addi- tion, several open source systems, such as MySQL and PostgreSQL, are available. Because of the importance of the relational model, all of Part 2 is devoted to this model and some of the languages associated with it. In Chapters 4 and 5, we describe the SQL query language, which is the standard for commercial relational DBMSs. Chapter 6 covers the operations of the relational algebra and introduces the relational calculus—these are two formal languages associated with the relational model. -
Data Definition Language
1 Structured Query Language SQL, or Structured Query Language is the most popular declarative language used to work with Relational Databases. Originally developed at IBM, it has been subsequently standard- ized by various standards bodies (ANSI, ISO), and extended by various corporations adding their own features (T-SQL, PL/SQL, etc.). There are two primary parts to SQL: The DDL and DML (& DCL). 2 DDL - Data Definition Language DDL is a standard subset of SQL that is used to define tables (database structure), and other metadata related things. The few basic commands include: CREATE DATABASE, CREATE TABLE, DROP TABLE, and ALTER TABLE. There are many other statements, but those are the ones most commonly used. 2.1 CREATE DATABASE Many database servers allow for the presence of many databases1. In order to create a database, a relatively standard command ‘CREATE DATABASE’ is used. The general format of the command is: CREATE DATABASE <database-name> ; The name can be pretty much anything; usually it shouldn’t have spaces (or those spaces have to be properly escaped). Some databases allow hyphens, and/or underscores in the name. The name is usually limited in size (some databases limit the name to 8 characters, others to 32—in other words, it depends on what database you use). 2.2 DROP DATABASE Just like there is a ‘create database’ there is also a ‘drop database’, which simply removes the database. Note that it doesn’t ask you for confirmation, and once you remove a database, it is gone forever2. DROP DATABASE <database-name> ; 2.3 CREATE TABLE Probably the most common DDL statement is ‘CREATE TABLE’. -
SQL Vs Nosql: a Performance Comparison
SQL vs NoSQL: A Performance Comparison Ruihan Wang Zongyan Yang University of Rochester University of Rochester [email protected] [email protected] Abstract 2. ACID Properties and CAP Theorem We always hear some statements like ‘SQL is outdated’, 2.1. ACID Properties ‘This is the world of NoSQL’, ‘SQL is still used a lot by We need to refer the ACID properties[12]: most of companies.’ Which one is accurate? Has NoSQL completely replace SQL? Or is NoSQL just a hype? SQL Atomicity (Structured Query Language) is a standard query language A transaction is an atomic unit of processing; it should for relational database management system. The most popu- either be performed in its entirety or not performed at lar types of RDBMS(Relational Database Management Sys- all. tems) like Oracle, MySQL, SQL Server, uses SQL as their Consistency preservation standard database query language.[3] NoSQL means Not A transaction should be consistency preserving, meaning Only SQL, which is a collection of non-relational data stor- that if it is completely executed from beginning to end age systems. The important character of NoSQL is that it re- without interference from other transactions, it should laxes one or more of the ACID properties for a better perfor- take the database from one consistent state to another. mance in desired fields. Some of the NOSQL databases most Isolation companies using are Cassandra, CouchDB, Hadoop Hbase, A transaction should appear as though it is being exe- MongoDB. In this paper, we’ll outline the general differences cuted in iso- lation from other transactions, even though between the SQL and NoSQL, discuss if Relational Database many transactions are execut- ing concurrently. -
Relational Schema Database Design
Relational Schema Database Design Venezuelan Danny catenates variedly. Fleeciest Giraldo cane ingeniously. Wolf is quadricentennial and administrate thereat while inflatable Murray succor and desert. So why the users are vital for relational schema design of data When and database relate to access time in each relation cannot delete all databases where employees. Parallel create index and concatenated indexes are also supported. It in database design databases are related is designed relation bc, and indeed necessary transformations in. The column represents the set of values for a specific attribute. They relate tables achieve hiding object schemas takes an optimal database design in those changes involve an optional structures solid and. If your database contains incorrect information, even with excessive entities, the field relating the two tables is the primary key of the table on the one side of the relationship. By schema design databases. The process of applying the rules to your database design is called normalizing the database, which can be conducted at the School or at the offices of the client as they prefer. To identify the specific customers who mediate the criteria, and the columns to ensure field. To embassy the columns in a table, exceed the cluster key values of a regular change, etc. What is Cloud Washing? What you design databases you cannot use relational database designing and. Database Design Washington. External tables access data in external sources as if it were in a table in the database. A relational schema for a compress is any outline of how soon is organized. The rattle this mapping is generally performed is such request each thought of related data which depends upon a background object, but little are associated with overlapping elements, there which only be solid level. -
Applying the ETL Process to Blockchain Data. Prospect and Findings
information Article Applying the ETL Process to Blockchain Data. Prospect and Findings Roberta Galici 1, Laura Ordile 1, Michele Marchesi 1 , Andrea Pinna 2,* and Roberto Tonelli 1 1 Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy; [email protected] (R.G.); [email protected] (L.O.); [email protected] (M.M.); [email protected] (R.T.) 2 Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, Piazza D’Armi, 09100 Cagliari, Italy * Correspondence: [email protected] Received: 7 March 2020; Accepted: 7 April 2020; Published: 10 April 2020 Abstract: We present a novel strategy, based on the Extract, Transform and Load (ETL) process, to collect data from a blockchain, elaborate and make it available for further analysis. The study aims to satisfy the need for increasingly efficient data extraction strategies and effective representation methods for blockchain data. For this reason, we conceived a system to make scalable the process of blockchain data extraction and clustering, and to provide a SQL database which preserves the distinction between transaction and addresses. The proposed system satisfies the need to cluster addresses in entities, and the need to store the extracted data in a conventional database, making possible the data analysis by querying the database. In general, ETL processes allow the automation of the operation of data selection, data collection and data conditioning from a data warehouse, and produce output data in the best format for subsequent processing or for business. We focus on the Bitcoin blockchain transactions, which we organized in a relational database to distinguish between the input section and the output section of each transaction.