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SQL for Humanities

Databases using SQL


Teaching: 60 min
Exercises: 5 min
  • What is a relational database and why should I use it?

  • What is SQL?

  • Understand the benefits of using a relational database

  • Set up a small database from csv files using SQLite

  • Understand SQLite data types


Note: this should have been done by participants before the start of the workshop.

We use DB Browser for SQLite and the

EEBO/TCP Database throughout this lesson. See Setup for instructions on how to download the data, and also how to install and open DB Browser for SQLite.


To start, let’s orient ourselves in our project workflow. Previously, we used Excel and OpenRefine to go from messy, human created data to cleaned, computer-readable data. Now we’re going to move to the next piece of the data workflow, using the computer to read in our data, and then use it for analysis and visualization.

Dataset Description

The data we will be using is a time-series for the texts collected and encoded by the Text Creation Partnership. This is a real dataset.


First, let’s download and look at some of the cleaned spreadsheets from the Humanities dataset.
We’ll need the following four files:


Open each of these csv files and explore them. What information is contained in each file? Specifically, if I had the following research questions:

  • How has the length and dates of Martin Luther title attributions changed over time?
  • What is the average number of each titles, per year?
  • What information can I learn about Martin Luther species in the 1500s, over time?

What would I need to answer these questions? Which files have the data I need? What operations would I need to perform if I were doing these analyses by hand?


In order to answer the questions described above, we’ll need to do the following basic data operations:

In addition, we don’t want to do this manually! Instead of searching for the right pieces of data ourselves, or clicking between spreadsheets, or manually sorting columns, we want to make the computer do the work.

In particular, we want to use a tool where it’s easy to repeat our analysis in case our data changes. We also want to do all this searching without actually modifying our source data.

Putting our data into a relational database and using SQL will help us achieve these goals.

Definition: Relational Database

A relational database stores data in relations made up of records with fields. The relations are usually represented as tables; each record is usually shown as a row, and the fields as columns. In most cases, each record will have a unique identifier, called a key, which is stored as one of its fields. Records may also contain keys that refer to records in other tables, which enables us to combine information from two or more sources.


Why use relational databases

Using a relational database serves several purposes.

Database Management Systems

There are a number of different database management systems for working with relational data. We’re going to use SQLite today, but basically everything we teach you will apply to the other database systems as well (e.g. MySQL, PostgreSQL, MS Access, MS SQL Server, Oracle Database and Filemaker Pro). The only things that will differ are the details of exactly how to import and export data and the details of data types.

Relational databases

Let’s look at a pre-existing database, the eebo.db file from the Humanities dataset that we downloaded during Setup. Clicking on the “open file” icon, then find that file and clicking on it will open the database.

You can see the tables in the database by looking at the left hand side of the screen under Tables, where each table corresponds to one of the csv files we were exploring earlier. To see the contents of any table, click on it, and then click the “Browse and Search” tab in the right panel. This will give us a view that we’re used to - just a copy of the table. Hopefully this helps to show that a database is, in some sense, just a collection of tables, where there’s some value in the tables that allows them to be connected to each other (the “related” part of “relational database”).

The leftmost tab, “Structure”, provides some metadata about each table. It describes the columns, often called fields. (The rows of a database table are called records.) If you scroll down in the Structure view, you’ll see a list of fields, their labels, and their data type. Each field contains one variety or type of data, often numbers or text. You can see in the dates table that most fields contain numbers (integers) while the titles table is nearly all text.

The “Execute SQL” tab is blank now - this is where we’ll be typing our queries to retrieve information from the database tables.

To summarize:

Database Design


Before we get started with writing our own queries, we’ll create our own database. We’ll be creating this database from the three csv files we downloaded earlier. Close the currently open database and then follow these instructions:

  1. Start a New Database
    • New Database
    • Give a name Save As. Creates the database in the opened folder
    • Press Cancel
  2. Select File -> Import -> Table from CSV File.
  3. Select the authors.csv file to import
  4. Give the table a name that matches the file name (authors), or use the default
  5. If the first row has column headings, check the appropriate box
  6. Make sure the delimiter and quotation options are appropriate for the CSV files.
  7. Press OK


  • Import the places, pages and dates tables

You can also use this same approach to append new data to an existing table.

Adding data to existing tables

  1. “File” tab -> Import -> Table from CSV file
  2. Enter data into a csv file and append

Data types

Data type Description
CHARACTER(n) Character string. Fixed-length n
VARCHAR(n) or CHARACTER VARYING(n) Character string. Variable length. Maximum length n
BINARY(n) Binary string. Fixed-length n
BOOLEAN Stores TRUE or FALSE values
VARBINARY(n) or BINARY VARYING(n) Binary string. Variable length. Maximum length n
INTEGER(p) Integer numerical (no decimal).
SMALLINT Integer numerical (no decimal).
INTEGER Integer numerical (no decimal).
BIGINT Integer numerical (no decimal).
DECIMAL(p,s) Exact numerical, precision p, scale s.
NUMERIC(p,s) Exact numerical, precision p, scale s. (Same as DECIMAL)
FLOAT(p) Approximate numerical, mantissa precision p. A floating number in base 10 exponential notation.
REAL Approximate numerical
FLOAT Approximate numerical
DOUBLE PRECISION Approximate numerical
DATE Stores year, month, and day values
TIME Stores hour, minute, and second values
TIMESTAMP Stores year, month, day, hour, minute, and second values
INTERVAL Composed of a number of integer fields, representing a period of time, depending on the type of interval
ARRAY A set-length and ordered collection of elements
MULTISET A variable-length and unordered collection of elements
XML Stores XML data

SQL Data Type Quick Reference

Different databases offer different choices for the data type definition.

The following table shows some of the common names of data types between the various database platforms:

Data type Access SQLServer Oracle MySQL PostgreSQL
boolean Yes/No Bit Byte N/A Boolean
integer Number (integer) Int Number Int / Integer Int / Integer
float Number (single) Float / Real Number Float Numeric
currency Currency Money N/A N/A Money
string (fixed) N/A Char Char Char Char
string (variable) Text (<256) / Memo (65k+) Varchar Varchar2 Varchar Varchar
binary object OLE Object Memo Binary (fixed up to 8K) Varbinary (<8K) Image (<2GB) Long Raw Blob Text Binary Varbinary

Key Points

  • SQL allows us to select and group subsets of data, do math and other calculations, and combine data.

  • A relational database is made up of tables which are related to each other by shared keys.

  • Different database management systems (DBMS) use slightly different vocabulary, but they are all based on the same ideas.

Basic Queries


Teaching: 30 min
Exercises: 5 min
  • How do I write a basic query in SQL?

  • Write and build queries.

  • Filter data given various criteria.

  • Sort the results of a query.

Writing my first query

Let’s start by using the eebo table. Here we have data on every title that is included in the catalogue.

Let’s write an SQL query that selects only the year column from the surveys table. SQL queries can be written in the box located under the “Execute SQL” tab. Click ‘Run SQL’ to execute the query in the box.

FROM eebo;

We have capitalized the words SELECT and FROM because they are SQL keywords. SQL is case insensitive, but it helps for readability, and is good style.

If we want more information, we can just add a new column to the list of fields, right after SELECT:

FROM eebo;

Or we can select all of the columns in a table using the wildcard *

FROM eebo;

Limiting results

Sometimes you don’t want to see all the results you just want to get a sense of of what’s being returned. In that case you can use the LIMIT command. In particular you would want to do this if you were working with large databases.

FROM eebo
LIMIT 10; 

Unique values

If we want only the unique values so that we can quickly see what authors have been cataloged we use DISTINCT

FROM eebo;

If we select more than one column, then the distinct pairs of values are returned

FROM eebo;

Calculated values

We can also do calculations with the values in a query. For example, if we wanted to look at the number of pages associated with different dates, but we needed to know how many tens of pages we would use

SELECT Date, PageCount/10
FROM eebo;

When we run the query, the expression pages / 10 is evaluated for each row and appended to that row, in a new column. If we used the INTEGER data type for the pages field then integer division would have been done, to obtain the correct results in that case divide by 10.0. Expressions can use any fields, any arithmetic operators (+, -, *, and /) and a variety of built-in functions. For example, we could round the values to make them easier to read.

SELECT TCP, title, ROUND(pages / 10, 2)
FROM eebo;


  • Write a query that returns the year, EEBO and page length


Databases can also filter data – selecting only the data meeting certain criteria. For example, let’s say we only want data for the titles that have a free status. We need to add a WHERE clause to our query:

FROM eebo
WHERE status='Free';

We can do the same thing with numbers. Here, we only want the data since 1640:

FROM eebo
WHERE date >= '1600';

If we used the TEXT data type for the year the WHERE clause should be Date >= '1600'. We can use more sophisticated conditions by combining tests with AND and OR. For example, suppose we want the data on Free status starting in the year 1600:

FROM catalogue
WHERE (date >= '1600') AND (status = 'Free');

Note that the parentheses are not needed, but again, they help with readability. They also ensure that the computer combines AND and OR in the way that we intend.


  • Produce a table listing the data for all titles in the catalogue with a page length more than 75, telling us the date, TCP id code, and page.

Building more complex queries

Now, lets combine the above queries to get data for 2 authors from the year 1580 on. This time, let’s use IN as one way to make the query easier to understand. It is equivalent to saying `WHERE (author = ‘Aylett, Robert, 1583-1655?’) OR (author = ‘Bacon, Francis, 1561-1626.’), but reads more neatly:

FROM eebo
WHERE (date >= '1580') AND (author IN ('Aylett, Robert, 1583-1655?', 'Bacon, Francis, 1561-1626.'));

We started with something simple, then added more clauses one by one, testing their effects as we went along. For complex queries, this is a good strategy, to make sure you are getting what you want. Sometimes it might help to take a subset of the data that you can easily see in a temporary database to practice your queries on before working on a larger or more complicated database.

When the queries become more complex, it can be useful to add comments. In SQL, comments are started by --, and end at the end of the line. For example, a commented version of the above query can be written as:

-- Get post 1580 data on authors
-- These are in the catalogue table, and we are interested in all columns
-- Sampling year is in the column `Date`, and we want to include after 1580
WHERE (date >= '1580')
-- Author names
AND (author IN ('Aylett, Robert, 1583-1655?', 'Bacon, Francis, 1561-1626.'));

Although SQL queries often read like plain English, it is always useful to add comments; this is especially true of more complex queries.


We can also sort the results of our queries by using ORDER BY. For simplicity, let’s go back to the catalogue table and order by publication date.

First, let’s look at what’s in the catalogue table. It’s a table of the eebo catalogue id and the information for each id.

FROM eebo;

Now let’s order it by date.

FROM eebo

The keyword ASC tells us to order it in Ascending order. We could alternately use DESC to get descending order.

FROM eebo

ASC is the default.

We can also sort on several fields at once. To truly be alphabetical, we might want to order by date then author.

FROM eebo
ORDER BY Date ASC, Author ASC;


  • Write a query that returns year, TCP id, and page from the catalogue table, sorted with the largest page lengths at the top.

Order of execution

Another note for ordering. We don’t actually have to display a column to sort by it. For example, let’s say we want to order an author by the EEBO index, but we only want to see genus and species.

SELECT Title, Terms
FROM eebo
WHERE author = 'Bacon, Francis, 1561-1626.'

We can do this because sorting occurs earlier in the computational pipeline than field selection.

The computer is basically doing this:

  1. Filtering rows according to WHERE
  2. Sorting results according to ORDER BY
  3. Displaying requested columns or expressions.

Clauses are written in a fixed order: SELECT, FROM, WHERE, then ORDER BY. It is possible to write a query as a single line, but for readability, we recommend to put each clause on its own line.


  • Let’s try to combine what we’ve learned so far in a single query. Using the catalogue table write a query to display the title, terms field and the page length (rounded to two decimal places), for titles published in 1550, ordered alphabetically by the author.
  • Write the query as a single line, then put each clause on its own line, and see how more legible the query becomes!

Key Points

  • It is useful to apply conventions when writing SQL queries to aid readability.

  • Use logical connectors such as AND or OR to create more complex queries.

  • Calculations using mathematical symbols can also be performed on SQL queries.

  • Adding comments in SQL helps keep complex queries understandable.

SQL Aggregation


Teaching: 30 min
Exercises: 5 min
  • How can I summarize my data by aggregating, filtering, or ordering query results?

  • Apply aggregation to group records in SQL.

  • Filter and order results of a query based on aggregate functions.

  • Save a query to make a new table.

  • Apply filters to find missing values in SQL.


Aggregation allows us to combine results by grouping records based on value and calculating combined values in groups.

Let’s go to the eebo table and find out how many individual titles there are. Using the wildcard simply counts the number of records (rows):

FROM eebo;

We can also find out the total page length:

FROM eebo;

There are many other aggregate functions included in SQL including MAX, MIN, and AVG.


Write a query that returns: total page length, average page length, and the min and max page lengths for all titles encoded over the duration of the project. Can you modify it so that it outputs these values only for page lengths between 1 and 10?

Now, let’s see how many individuals were counted in each species. We do this using a GROUP BY clause

FROM eebo
GROUP BY Status;

GROUP BY tells SQL what field or fields we want to use to aggregate the data. If we want to group by multiple fields, we give GROUP BY a comma separated list.


Write queries that return:

  1. How many groups of terms were created in each year
    • in total
    • per author
  2. Average number of each term groupings in each year.

Can you modify the above queries combining them into one?

The HAVING keyword

In the previous lesson, we have seen the keywords WHERE, allowing to filter the results according to some criteria. SQL offers a mechanism to filter the results based on aggregate functions, through the HAVING keyword.

For example, we can adapt the last request we wrote to only return information about page length with a count higher than 100:

FROM eebo
GROUP BY PageCount
HAVING PageCount > 10;

The HAVING keyword works exactly like the WHERE keyword, but uses aggregate functions instead of database fields.

If you use AS in your query to rename a column, HAVING can use this information to make the query more readable. For example, in the above query, we can call the COUNT(pages) by another name, like page. This can be written this way:

SELECT TCP, author, PageCount AS page
FROM eebo
HAVING PageCount > 10;

Note that in both queries, HAVING comes after GROUP BY. One way to think about this is: the data are retrieved (SELECT), can be filtered (WHERE), then joined in groups (GROUP BY); finally, we only select some of these groups (HAVING).


Write a query that returns, from the authors table, the eebo IDs in each authors, only for the authors with more than 5 works.

Ordering Aggregated Results

We can order the results of our aggregation by a specific column, including the aggregated column. Let’s count the number of individuals of each species captured, ordered by the count:

SELECT author, COUNT(*)
FROM eebo
GROUP BY author

Saving Queries for Future Use

It is not uncommon to repeat the same operation more than once, for example for monitoring or reporting purposes. SQL comes with a very powerful mechanism to do this: views. Views are a form of query that is saved in the database, and can be used to look at, filter, and even update information. One way to think of views is as a table, that can read, aggregate, and filter information from several places before showing it to you.

Creating a view from a query requires to add CREATE VIEW viewname AS before the query itself. For example, imagine that my project only covers the data gathered of books published between 1642 - 1651. That query would look like:

FROM eebo
WHERE (date > '1558' AND date < '1603');

But we don’t want to have to type that every time we want to ask a question about that particular subset of data. Let’s create a view:

CREATE VIEW elizabethan AS
FROM eebo
WHERE (date > '1558' AND date < '1603');

You can also add a view using Create View in the View menu and see the results in the Views tab just like a table.

Now, we will be able to access these results with a much shorter notation:

FROM elizabethan;

There should only be 51 records. If you look at the PageCount column, it’s easy to see what the average page length would be. If we use SQL to find the average page count of books that are available, SQL behaves like we would hope, ignoring the NULL values:

FROM elizabethan
WHERE author != '';

But if we try to be extra clever, and find the average ourselves, we might get tripped up:

SELECT SUM(PageCount), COUNT(*), SUM(PageCount)/COUNT(*)
FROM elizabethan
WHERE author != '';

Here the COUNT command includes all 51 records (even those with NULL values), but the SUM only includes the 49 records with data in the weight field, giving us an incorrect average. However, our strategy will work if we modify the count command slightly:

SELECT SUM(PageCount), COUNT(PageCount), SUM(PageCount)/COUNT(PageCount)
FROM elizabethan
WHERE author != '';

When we count the pages field specifically, SQL ignores the records with data missing in that field. So here is one example where NULLs can be tricky: COUNT(*) and COUNT(field) can return different values.

Another case is when we use a “negative” query. Let’s count all the non-free titles:

FROM elizabethan
WHERE Author != '';

Now let’s count all the titles wth no author listed:

FROM elizabethan
WHERE Author != '';

But if we compare those two numbers with the total:

FROM elizabethan;

We’ll see that they don’t add up to the total! That’s because SQL doesn’t automatically include NULL values in a negative conditional statement. So if we are quering “not x”, then SQL divides our data into three categories: ‘x’, ‘not NULL, not x’ and NULL and returns the ‘not NULL, not x’ group. Sometimes this may be what we want - but sometimes we may want the missing values included as well! In that case, we’d need to change our query to:

FROM eebo
WHERE author != '' OR author IS NULL;

There is one more subtlety we need to be aware of. Suppose we run this query:

SELECT COUNT(*), PageCount
FROM elizabethen;

Key Points

  • Use the GROUP BY keyword to aggregate data.

  • Functions like MIN, MAX, AVERAGE, SUM, COUNT, etc. operate on aggregated data.

  • Use the HAVING keyword to filter on aggregate properties.

  • Use a VIEW to access the result of a query as though it was a new table.

Joins and aliases


Teaching: 30 min
Exercises: 5 min
  • How do I bring data together from separate tables?

  • How can I make sure column names from my queries make sense and aren’t too long?

  • Employ joins to combine data from two tables.

  • Apply functions to manipulate individual values.

  • Employ aliases to assign new names to items in a query.


To combine data from two tables we use the SQL JOIN command, which comes after the FROM command.

The JOIN command on its own will result in a cross product, where each row in first table is paired with each row in the second table. Usually this is not what is desired when combining two tables with data that is related in some way.

For that, we need to tell the computer which columns provide the link between the two tables using the word ON. What we want is to join the data with the same species codes.

FROM authors
JOIN dates
ON authors.TCP = dates.TCP;

ON is like WHERE, it filters things out according to a test condition. We use the table.colname format to tell the manager what column in which table we are referring to.

The output of the JOIN command will have columns from first table plus the columns from the second table. For the above command, the output will be a table that has the following column names:

TCP Authors TCP Date                  

Alternatively, we can use the word USING, as a short-hand. In this case we are telling the manager that we want to combine authors with titles and that the common column is eebo.

FROM authors
JOIN dates

The output will only have one TCP column

TCP Authors Date                    

We often won’t want all of the fields from both tables, so anywhere we would have used a field name in a non-join query, we can use table.colname.

For example, what if we wanted information authors and titles but not their TCP ids.

SELECT authors.Author, dates.Date
FROM dates
JOIN authors
ON dates.TCP = authors.TCP;
Authors Title      

Many databases, including SQLite, also support a join through the WHERE clause of a query.
For example, you may see the query above written without an explicit JOIN.

SELECT dates.Date, authors.Author
FROM authors, dates
WHERE authors.TCP = dates.TCP;

For the remainder of this lesson, we’ll stick with the explicit use of the JOIN keyword for joining tables in SQL.


  • Write a query that returns the authors and places of every TCP ID captured in the catalogue.

Different join types

We can count the number of records returned by our original join query.

FROM authors
JOIN places

Notice that this number is smaller than the number of records present in the catalogue data.

FROM eebo;

This is because, by default, SQL only returns records where the joining value is present in the join columns of both tables (i.e. it takes the intersection of the two join columns). This joining behaviour is known as an INNER JOIN. In fact the JOIN command is simply shorthand for INNER JOIN and the two terms can be used interchangably as they will produce the same result.

We can also tell the computer that we wish to keep all the records in the first table by using the command LEFT OUTER JOIN, or LEFT JOIN for short.


  • Re-write the original query to keep all the entries present in the eebo table. How many records are returned by this query?


  • Count the number of records in the places table that have a NULL value in the eebo column.

In SQL a NULL value in one table can never be joined to a NULL value in a second table because NULL is not equal to anything, even itself.

Combining joins with sorting and aggregation

Joins can be combined with sorting, filtering, and aggregation. So, if we wanted the average number of pages for each author in the catalogue, we could do something like

SELECT, AVG(eebo.PageCount)
FROM eebo
JOIN authors
ON authors.TCP = eebo.TCP
GROUP BY eebo.PageCount;


  • Write a query that returns the number of authors of the titles published in each year in descending order.


  • Write a query that finds the average pages of each year of publication.


SQL includes numerous functions for manipulating data. You’ve already seen some of these being used for aggregation (SUM and COUNT) but there are functions that operate on individual values as well. Probably the most important of these are IFNULL and NULLIF. IFNULL allows us to specify a value to use in place of NULL.

We can represent unknown ids with “U” instead of NULL:

SELECT TCP, Place, IFNULL(Place, 'U')
FROM places;

The lone “Place” column is only included in the query above to illustrate where IFNULL has changed values; this isn’t a usage requirement.


  • Write a query that returns ‘NP’ instead of NULL for values in the Author column.


  • Write a query that calculates the average page length of each title, assuming that unknown lengths are 30 (as above).

IFNULL can be particularly useful in JOIN. When joining the authors and dates tables earlier, some results were excluded because the `` was NULL. We can use IFNULL to include them again, re-writing the NULL to a valid joining value:

SELECT eebo.Place, places.Place
FROM places
JOIN eebo
ON eebo.Place = IFNULL(places.Place, 'AB');


  • Write a query that returns the number of titles of the authors caught in each plot, using IFNULL to assume that unknown titles are all of the authors “Nemo”.

The inverse of IFNULL is NULLIF. This returns NULL if the first argument is equal to the second argument. If the two are not equal, the first argument is returned. This is useful for “nulling out” specific values.

We can “null out” vid:

SELECT TCP, Place, NULLIF(Place, 'London')
FROM eebo;

Some more functions which are common to SQL databases are listed in the table below:

Function Description
ABS(n) Returns the absolute (positive) value of the numeric expression n
LENGTH(s) Returns the length of the string expression s
LOWER(s) Returns the string expression s converted to lowercase
NULLIF(x, y) Returns NULL if x is equal to y, otherwise returns x
ROUND(n) or ROUND(n, x) Returns the numeric expression n rounded to x digits after the decimal point (0 by default)
TRIM(s) Returns the string expression s without leading and trailing whitespace characters
UPPER(s) Returns the string expression s converted to uppercase

Finally, some useful functions which are particular to SQLite are listed in the table below:

Function Description
IFNULL(x, y) Returns x if it is non-NULL, otherwise returns y
RANDOM() Returns a random integer between -9223372036854775808 and +9223372036854775807.
REPLACE(s, f, r) Returns the string expression s in which every occurrence of f has been replaced with r
SUBSTR(s, x, y) or SUBSTR(s, x) Returns the portion of the string expression s starting at the character position x (leftmost position is 1), y characters long (or to the end of s if y is omitted)


Write a query that returns author names, sorted from longest titles name down to shortest.


As queries get more complex names can get long and unwieldy (as we saw before). To help make things clearer we can use aliases to assign new names to things in the query.

We can alias both table names:

SELECT dt.Date, auth.Author
FROM dates AS dt
JOIN authors AS auth
ON dt.TCP = auth.TCP;

And column names:

SELECT dt.Date AS yr, auth.Author AS author
FROM dates AS dt
JOIN authors AS auth
ON dt.TCP = auth.TCP;

The AS isn’t technically required, so you could do

SELECT dt.Date yr
FROM dates dt;

but using AS is much clearer so it is good style to include it.

Challenge (optional):

SQL queries help us ask specific questions which we want to answer about our data. The real skill with SQL is to know how to translate our humanities questions into a sensible SQL query (and subsequently visualize and interpret our results).

Have a look at the following questions; these questions are written in plain English. Can you translate them to SQL queries and give a suitable answer?

  1. How many entries from each year are there per year?

  2. How many years have similar amounts of books published?

    Proposed solutions:

    1. Solution: SELECT date as year, count(*) FROM eebo GROUP BY year ORDER BY year DESC

    2. Solution: SELECT date as year, count(*) AS volumes FROM eebo GROUP BY year ORDER BY volumes DESC

Key Points

  • Use the JOIN command to combine data from two tables—the ON or USING keywords specify which columns link the tables.

  • Regular JOIN returns only matching rows. Other join commands provide different behavior, e.g., LEFT JOIN retains all rows of the table on the left side of the command.

  • IFNULL allows you to specify a value to use in place of NULL, which can help in joins

  • NULLIF can be used to replace certain values with NULL in results

  • Many other functions like IFNULL and NULLIF can operate on individual values.

  • Aliases can help shorten long queries. To write clear and readible queries, use the AS keyword when creating aliases.