How can I merge the columns from two tables into one output?

Merge using full outer join

Need all records from both tables? Use a full outer join. We can use the COALESCE function (returns the first non-null value it receives) to blend columns that could contain NULL values:

SELECT COALESCE(T1.category_id, T2.category_id) AS category_id,
       T1.ColumnA, 
       T2.ColumnB
FROM Table1 T1
FULL OUTER JOIN Table2 T2 ON T1.category_id = T2.category_id;


This forms a united dataset, with category_id as an overlapping field.

Merging rows with UNION

When you're looking to append or stack rows from both tables, UNION is your SQL superhero:

SELECT 'Table1' AS Source, ColumnA AS CommonColumn
FROM Table1
UNION ALL
SELECT 'Table2' AS Source, ColumnB AS CommonColumn
FROM Table2;


The aliases play the role of disguises to match differing column names. Bear in mind, UNION demands the same number of columns and compatible data types.

Merging all data with a left outer join

In cases where you want to include every record from one table regardless of matching records in the other table, a left outer join comes to the rescue:

SELECT T1.category_id, T1.ColumnA, T2.ColumnB
FROM Table1 T1
LEFT OUTER JOIN Table2 T2 ON T1.category_id = T2.category_id; // LEFT OUTER JOIN, because right ones are too mainstream!


Check for data compatibility, 007 style!

Ensure your data types and formats are consistently "shaken, not stirred" to avoid any cocktail of errors:

-- Shaken, not stirred check 
SELECT COLUMN_NAME, DATA_TYPE
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'Table1' OR TABLE_NAME = 'Table2';


Once merged, ensure accuracy of the results by cross-referencing with a sample dataset or categories table.

Optimizing UNION: The fast and the query-ous

While UNION forms a single dataset out of multiple tables, performance can be tuned by:

• Limiting the columns chosen to only those under the spotlight.
• Indexing the join columns to have them ready for the stage.

Merging complex tables: The enigma machine

In dealing with complex merging scenarios involving several tables or intricate conditions, explore the world of Common Table Expressions (CTE) or subqueries:

WITH CombinedCTE AS (
  SELECT T1.category_id, T1.ColumnA
  FROM Table1 T1
  UNION ALL
  SELECT T2.category_id, T2.ColumnB
  FROM Table2 T2
)
SELECT C.category_name, ECC.*
FROM Categories C
JOIN CombinedCTE ECC ON C.category_id = ECC.category_id; // Joins... Joins everywhere!


CTEs simplify complex queries into digestible bite-sized queries improving readability faster than Speedy Gonzales.

Beware of imposters!

Anticipate potential imposters (duplicate data) and red herrings (inaccurate entries) due to different data sources:

• Use DISTINCT to vanish duplicates.
• Perform data cleansing to ensure a sanitary dataset.

Multiple join options: Choose your player

Remember, each type of join is a different character with its own abilities:

• INNER JOIN: Only matched data (The traditionalist)
• LEFT OUTER JOIN: All data from the left table, matched or not (The lefty)
• FULL OUTER JOIN: Data from both tables, matched or not (The compromiser)

Final checks: The SAS of data

Before taking the plunge, perform multiple test runs using various datasets and validate your joins' logic to avoid unexpected data ambushes:

• Create test cases for edge cases and anomalies.
• Review the results with data stakeholders or peers. Extra pairs of eyes are always beneficial!