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Intro

This topic lists a number of data clean-up operations available through SQL and Magic ETL DataFlows and shows how data will look both before and after using an operation.

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Changing Your Data Type

Example: Changing the data type of an ID number from a numeric to a text field
In MySQL… In Magic ETL… Before After
CAST(id AS CHAR) AS id_cast_datatype Use the “Set Column Type” tile 1 1 (looks the same but behaves a text dimension)

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Concatenating Columns to Create a Compound Field

Example: Concatenating “First Name” and “Last Name” columns to create a “Full Name” column
In MySQL… In Magic ETL… Before After
CONCAT(first_name, ’ ’, last_name) AS full_name Use the “Combine Columns” tile ‘John’ | ‘Smith’ (first and last name in two separate columns) ‘John Smith’ (a single column containing the complete name)

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Extracting a Portion of a Text String

Example: Extracting the first part of an email address to use as a user ID
In MySQL… In Magic ETL… Before After
SUBSTRING_INDEX(email,’@’, 1) AS user_id Use the “Replace Text” tile on that column to specify which portion of the string should be replaced with an empty string: @.+ userid@email.com userid

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Reformatting a Date

Example: Formatting a non-standard date string into a date type format
In MySQL… In Magic ETL… Before After
STR_TO_DATE(send_date, ‘%d.%m.%Y’) AS date_formatted Use the “Set Column Type” tile. 23.01.2017 (string data type) 01/23/2017 (date data type)

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Deriving Date Attributes from a Date Column

Example: Extracting the day of the week from a date column
In MySQL… In Magic ETL… Before After
DAYNAME(receive_date) AS receive_date_name Use the “Date Operations” tile. 01/23/2017 Monday

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Splitting a Column into Two Columns Based on a Character in the Column

Example: Dividing a “Status Code” column into status code parts based on the / delimiter found within the column
In MySQL… In Magic ETL… Before After
SUBSTRING_INDEX(status_code, ’/’, 1) AS status_code_p1
, SUBSTRING_INDEX(status_code, ’/’, -1) AS status_code_p2 		Use the “Replace Text” tile on that column to specify which portion of the string should be replaced with an empty string: /.+ for the first part and .+/ for the second. SHI/DELV SHI | DELV

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Trimming Erroneous Spaces from a Column

Example: Trimming the leading and trailing spaces from the “Department” column
In MySQL… In Magic ETL… Before After
TRIM(department) AS department_trimmed Use a regular expression within the “Replace Text” tile to pinpoint the leading and training spaces and replace them with nothing. ’ department ’ ‘department’

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Changing the Case of an Entire Column

Example: Changing the “Category” column to uppercase letters
In MySQL… In Magic ETL… Before After
UPPER(category) AS category_change_case Use the All upper case option within the “Text Formatting” tile. health HEALTH

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Capitalizing the First Letter of a Column

Example: Capitalizing the first letter of the first word in the “Category” column
In MySQL… In Magic ETL… Before After
CONCAT(UPPER(LEFT(category, 1)), SUBSTRING(category, 2, LENGTH(category))) AS category_cap_first Use the Capitalize first letter option within the “Text Formatting” tile. health Health

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Categorizing Rows Based off the Value in a Specific Column

Example: Assigning a region to each row based on a store number
In MySQL… In Magic ETL… Before After
(CASE WHEN store_number IN (‘100’, ‘101’, ‘104’, ‘109’) THEN ‘region_1’
WHEN store_number IN (‘102’, ‘105’, ‘110’) THEN ‘region_2’
WHEN store_number IN (‘103’, ‘106’, ‘107’, ‘108’) THEN ‘region_3’
ELSE ‘no region’ END) AS region 		Use the Filter Rows tile to separate into distinct groups based on store number, use Add Constants to add a category to each group, then use Append Rows to stitch the rows back together. store_number = 100 store_number = 100 | region = ‘region_1’

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Categorizing Rows Based off the Value in a Specific Column

Example: Assigning a category to the animal name based on the first letter of the name
In MySQL… In Magic ETL… Before After
(CASE WHEN animal_names LIKE ‘H%’ THEN ‘Hs’
WHEN animal_names LIKE ‘B%’ THEN ‘Bs’
WHEN (animal_names LIKE ‘A%’ OR animal_names LIKE ‘E%’) THEN ‘AEs’
ELSE ‘another letter’ END) AS animal_names_categories 		Use a regular expression within the “Replace Text” tile to identify the patterns and assign a category. For example ^H.+ finds names starting with H, ^B.+ finds names starting with B, ^A.+|^E.+ finds names starting with either A or E, and ^[^H,B,A,E].+ finds names that do not start with H, B, A, or E. animal_names = ‘Horse’ animal_names = ‘Horse’ | animal_names_categories = ‘Hs’

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Categorizing Rows Based off a Numeric Value Threshold

Example: Categorizing rows based on salary amount
In MySQL… In Magic ETL… Before After
(CASE WHEN salary 50000 THEN ‘$50,000’
WHEN salary 100000 THEN ‘$50,000 - $99,999’
WHEN salary 150000 THEN ‘$100,000 - $149,999’
WHEN salary 200000 THEN ‘$150,000 - $199,999’
ELSE $200,000’ END) AS salary_bucket 		Use the “Filter Rows” tile to separate into distinct groups based on store number, use “Add Constants” to add a category to each group, then use “Append Rows” to stitch the rows back together. salary = $45,000 salary = $45,000 | salary_bucket = ‘$50,000’