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Visual Representation of SQL Joins

Visual Representation of SQL Joins
Introduction This is just a simple article visually explaining SQL JOINs. Background I'm a pretty visual person. Things seem to make more sense as a picture. Using the code I am going to discuss seven different ways you can return data from two relational tables. For the sake of this article, I'll refer to 5, 6, and 7 as LEFT EXCLUDING JOIN, RIGHT EXCLUDING JOIN, and OUTER EXCLUDING JOIN, respectively. Inner JOIN This is the simplest, most understood Join and is the most common. Hide Copy Code SELECT <select_list> FROM Table_A A INNER JOIN Table_B B ON A.Key = B.Key Left JOIN This query will return all of the records in the left table (table A) regardless if any of those records have a match in the right table (table B). SELECT <select_list>FROM Table_A A LEFT JOIN Table_B B ON A.Key = B.Key Right JOIN This query will return all of the records in the right table (table B) regardless if any of those records have a match in the left table (table A). Outer JOIN Left Excluding JOIN Examples History Related:  Data Analysis

QuickRef.org - all your docs are belong to us - PHP, Perl, CSS, HTML, Java, JavaScript, MySQL, Ruby, Python, and more Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase comparison :: KKovacs (Yes it's a long title, since people kept asking me to write about this and that too :) I do when it has a point.) While SQL databases are insanely useful tools, their monopoly in the last decades is coming to an end. And it's just time: I can't even count the things that were forced into relational databases, but never really fitted them. (That being said, relational databases will always be the best for the stuff that has relations.) But, the differences between NoSQL databases are much bigger than ever was between one SQL database and another. In this light, here is a comparison of Open Source NOSQL databases Cassandra, Mongodb, CouchDB, Redis, Riak, RethinkDB, Couchbase (ex-Membase), Hypertable, ElasticSearch, Accumulo, VoltDB, Kyoto Tycoon, Scalaris, OrientDB, Aerospike, Neo4j and HBase: The most popular ones Redis (V3.2) Best used: For rapidly changing data with a foreseeable database size (should fit mostly in memory). For example: To store real-time stock prices. Cassandra (2.0)

Using MySQL - Data and Structures Workshop Requirements You should have completed Parts One and Two of this series. You should also have access to the MySQL command line client software. You should also have full permissions on a database. Introduction In the previous MySQL Virtual Workshops we have looked at logging into MySQL, creating tables, adding and selecting data using SQL statements. UPDATE-ing Records The UPDATE SQL statement is similar to the SELECT statement as there must be a WHERE condition to specify which record(s) to change. UPDATE <table_name> SET <column_name> = 'new_value' WHERE (<column_name> = 'some_value'); So to change the 'title' in the first row of our 'cds' table from: | 1 | jamiroquai | A Funk Odyssey | 2001 | Sony Soho2 | 2001-09-01 | to: | 1 | jamiroquai | Wrong Title | 2001 | Sony Soho2 | 2001-09-01 | We would use the following statement: mysql> UPDATE cds -> SET cds.title = 'Wrong Title' -> WHERE (cds.cdID = '1'); Query OK, 1 row affected (0.06 sec) Rows matched: 1 Changed: 1 Warnings: 0 Mini Exercise

Tutorial 2: Creating a Business Logic Layer Scott Mitchell June 2006 Download the ASPNET_Data_Tutorial_2_CS.exe sample code. Contents of Tutorial 2 (Visual C#) Introduction Step 1: Creating the BLL Classes Step 2: Accessing the Typed DataSets Through the BLL Classes Step 3: Adding Field-Level Validation to the DataRow Classes Step 4: Adding Custom Business Rules to the BLL's Classes Summary Introduction The Data Access Layer (DAL) created in the first tutorial cleanly separates the data access logic from the presentation logic. In this tutorial we'll see how to centralize these business rules into a Business Logic Layer (BLL) that serves as an intermediary for data exchange between the presentation layer and the DAL. Figure 1. Step 1: Creating the BLL Classes Our BLL will be composed of four classes, one for each TableAdapter in the DAL; each of these BLL classes will have methods for retrieving, inserting, updating, and deleting from the respective TableAdapter in the DAL, applying the appropriate business rules. Figure 2. Figure 3.

Assessing Linear Models in R | Connor Johnson In this post I will look at several techniques for assessing linear models in R, via the IPython Notebook interface. I find the notebook interface to be more convenient for development and debugging because it allows one to evaluate cells instead of going back and forth between a script and a terminal. If you do not have the IPython Notebook, then you can check it out here. If you do not already have it, you will also need to install the rpy2 module. Once all of that is squared away, you should be able to open an IPython notebook from the terminal using, and load the rmagic extension using, We will be using the rock data set that comes with R. In the rock data set, twelve core samples were sampled by four cross-sections, making a total of 48 samples. The data collection was performed by BP, and the image analysis done by Ronit Katx, of the University of Oxford. A linear model attempts to describe an output variable in terms of a linear combination of predictor variables. Here, Residuals

How RDF Databases Differ from Other NoSQL Solutions - The Datagraph Blog This started out as an answer at Semantic Overflow on how RDF database systems differ from other currently available NoSQL solutions. I've here expanded the answer somewhat and added some general-audience context. RDF database systems are the only standardized NoSQL solutions available at the moment, being built on a simple, uniform data model and a powerful, declarative query language. In case you're not familiar with the term, NoSQL ("Not only SQL") is a loosely-defined umbrella moniker for describing the new generation of non-relational database systems that have sprung up in the last several years. Key-value databases are familiar to anyone who has worked with the likes of the venerable Berkeley DB. RDF database systems form the largest subset of this last NoSQL category. A simple and uniform standard data model. From the preceding points it follows that RDF-based NoSQL solutions enjoy some very concrete advantages such as: Data portability.

MySQL - Advanced Queries Workshop Requirements You should have completed Parts One, Two, Three, Four and Five of this series. You should also have access to the MySQL command line client software. You should also have full permissions on a database. Introduction Earlier in this series when we looked at SQL statements we were primarily concerned with either retrieving, changing or deleting values within the database and also manipulating the database structures. As One of the simplest manipulations is to define a new structural element (table or column) by aliasing an existing value. SELECT <columns> FROM <existing_table_name> AS <new_table_name> It is important to remember that the table hasn't actually been renamed, but instead the <new_table_name> is simply a reference that exists for the duration of the SQL statement. mysql> SELECT t1.name -> FROM artists -> AS t1; +----------------------+ | name | +----------------------+ | Jamiroquai | | Various | | westlife | | Various | | Abba | The existing statement is:

Hidden Features of C# Concepts for Fourier Transforms A signal can be viewed from two different standpoints: The frequency domain The time domain In astronomy the frequency domain is perhaps the most familiar, because a spectrometer, e.g. a prism or a diffraction grating, splits light into its component color or frequencies and permits us to record its spectral content. This is like the trace on a spectrum analyzer, where the horizontal deflection is the frequency variable and the vertical deflection is the signals amplitude at that frequency. In the lab we are also familiar with the time domain. Any signal can be fully described in either of these domains. Depending on what we want to do with the signal, one domain tends to be more useful than the other, so rather than getting tied up in mathematics with a time domain signal we might convert it to the frequency domain where the mathematics are simpler. Back to Contents or on to Applications

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