SQL Join Tables

3 Key excerpts on "SQL Join Tables"

• 

  eBook - ePub

  PHP and MySQL For Dummies

  • Janet Valade(Author)
  • 2009(Publication Date)
  • For Dummies

    (Publisher)

  The results table for the outer join contains all the rows for one table. If any of the rows for that table don’t exist in the second table, the columns for the second table are empty. Clearly, the contents of the results table are determined by which table contributes all its rows, requiring the second table to match it. Two kinds of outer joins control which table sets the rows and which match: a

  LEFT JOIN and a RIGHT JOIN .

  You use different SELECT queries for an inner join and the two types of outer joins. The following query is an inner join:

  SELECT columnnamelist FROM table1,table2 WHERE table1.col2 = table2.col2 And these queries are outer joins: SELECT columnnamelist FROM table1 LEFT JOIN table2 ON table1.col1=table2.col2 SELECT columnnamelist FROM table1 RIGHT JOIN table2 ON table1.col1=table2.col2

  In all three queries, table1 and table2 are the tables to be joined. You can join more than two tables. In both queries, col1 and col2 are

  the names of the columns being matched to join the tables. The tables are matched based on the data in these columns. These two columns can have the same name or different names. The two columns must contain the same type of data.

  As an example of inner and outer joins, consider a short form of the Pet Catalog . One table is Pet , with the two columns petName and petType holding the following data:

  petName petType Unicorn Horse Pegasus Horse Lion Cat

  The second table is Color , with two columns petName and petColor holding the following data:

  petName petColor Unicorn white Unicorn silver Fish Gold You need to ask a question that requires information from both tables. If you do an inner join with the following query:

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  eBook - ePub

  Learning Tableau 2020

  Create effective data visualizations, build interactive visual analytics, and transform your organization, 4th Edition

  • Joshua N. Milligan(Author)
  • 2020(Publication Date)
  • Packt Publishing

    (Publisher)

  For example, our join calculation might have code like [First Name] + " " + [Last Name] to return values that match with the Name field. Try to avoid joining on text fields, especially in larger datasets for performance reasons. Joining on integers is far more efficient. Also, it is entirely possible for two separate people to share first and last names, so a real-world dataset that followed the structure in this example would be subject to false matches and errors. You may also leverage the geospatial functions mentioned in Chapter 12, Exploring Mapping and Advanced Geospatial Features, to create a spatial join between two sources, even when one or both lack specific spatial objects on which to join. For example, if you have Latitude and Longitude, you might create a join calculation with the code MAKEPOINT([Latitude], [Longitude]) to find the intersection with another spatial object in another table. Join calculations can also help when you are missing a field for a join. What if the data you want to join is in another database or file completely? In this scenario, we would consider cross-database joins. Cross-database joins With Tableau, you have the ability to join (at the row level) across multiple different data connections. Joining across different data connections is referred to as a cross-database join. For example, you can join SQL Server tables with text files or Excel files, or join tables in one database with tables in another, even if they are on a different server. This opens up all kinds of possibilities for supplementing your data or analyzing data from disparate sources. Consider the hospital data. Though not part of the data included the Chapter 13 file set, it would not be uncommon for billing data to be in a separate system from patient care data. Let's say you had a file for patient billing that contained data you wanted to include in your analysis of hospital visits

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  eBook - ePub

  Data Analysis for Corporate Finance

  Building financial models using SQL, Python, and MS PowerBI

  • Mariano F. Scandizzo CFA CQF(Author)
  • 2021(Publication Date)
  • Fulton Books, Inc.

    (Publisher)

  Over time, another strength of the relational model emerged as developers began to use structured query language (SQL) to write and query data in a database. For many years, SQL has been widely used as the language for database queries. Based on relational algebra, SQL provides an internally consistent mathematical language that makes it easier to improve the performance of all database queries. In comparison, other approaches must define individual queries.

  Benefits of Relational Databases

  The simple yet powerful relational model is used by organizations of all types and sizes for a broad variety of information needs. Relational databases are used to track inventories, process e-commerce transactions, manage large amounts of mission-critical customer information, and much more. A relational database can be considered for any information need in which data points relate to each other and must be managed in a secure, rules-based, and consistent way.

  Relational databases have been around since the 1970s. The advantages of the relational model continue to make it the most widely accepted model for databases. How a Database Table Is Organized

  The model for a relational database states that data is stored in one or more tables. It also states that each table can be viewed as a two-dimensional matrix consisting of rows and columns. This is illustrated by the relational table, which contains information about AdventureWorks resellers. Each row in this table contains information about a single reseller.

  Fig. 7: Table Design & Layout

  In general, each table is modeled after a real-world entity, in this case, the information related to the bike shops reselling AdventureWorks products. Then, the columns of the table represent the attributes of the entity, such as name, address, phone number, etc. Each row of the table represents one instance of the table. Values are stored at the intersection of each row and column, sometimes referred to as a cell.

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