❄️ Snowflake Lateral Joins – Unlocking the Power of Table Functions

In the world of modern analytics, semi-structured data like JSON and arrays have become the norm. But querying nested arrays or invoking table functions in SQL can be complex — unless you know Snowflake Lateral Joins.

The LATERAL JOIN (also called a Lateral View) is a powerful SQL feature that allows one table to reference the output of a table function — such as FLATTEN(), SPLIT_TO_TABLE(), or even custom user-defined functions (UDFs).

In short:

A Lateral Join lets you expand nested data or generate rows dynamically — all inside a SQL query.

🧩 What Is a Lateral Join in Snowflake?

A Lateral Join allows a subquery or table function to access columns from the table being joined. It’s often used with table functions that return multiple rows for each input row.

🧠 Syntax:

FROM <table1>, LATERAL <table_function>(<table1.column>)

or equivalently:

FROM <table1>
JOIN LATERAL <table_function>(<table1.column>) ON TRUE

🔍 Key Point:

The LATERAL keyword allows the function to “see” columns from the left-hand table — something a regular join cannot do.

🧭 ** – Lateral Join Workflow**

⚙️ Why Lateral Joins Matter

🧠 Understanding Table Functions

A table function in Snowflake is a function that returns a set of rows instead of a single value.

Common examples:

• FLATTEN() – expands JSON arrays
• SPLIT_TO_TABLE() – splits a string into multiple rows
• GENERATE_SERIES() – generates a range of numbers

You can use LATERAL JOIN to attach these results to other table data dynamically.

🧩 Example 1 – Flattening JSON Arrays Using LATERAL JOIN

Let’s start with the most common use case: expanding JSON arrays.

💾 Step 1: Create a table with JSON data

CREATE OR REPLACE TABLE orders (
  order_id INT,
  order_data VARIANT
);

INSERT INTO orders VALUES
(1, PARSE_JSON('{"items": ["Laptop", "Mouse", "Keyboard"]}')),
(2, PARSE_JSON('{"items": ["Monitor", "HDMI Cable"]}'));

💡 Step 2: Query with LATERAL JOIN

SELECT
  order_id,
  item.value::string AS product
FROM orders,
LATERAL FLATTEN(input => order_data:items) AS item;

✅ Output:

🧠 Explanation

• The FLATTEN() function expands JSON arrays into rows.
• The LATERAL keyword lets FLATTEN access the order_data column.
• Each element in the JSON array becomes a new row in the output.

✅ Use Case: Useful for flattening event logs, arrays, or JSON payloads for analysis.

🧩 Example 2 – Splitting Strings Using LATERAL JOIN

Let’s split a comma-separated string into rows.

CREATE OR REPLACE TABLE employees (
  emp_id INT,
  skills STRING
);

INSERT INTO employees VALUES
(1, 'SQL,Python,Java'),
(2, 'Snowflake,AWS,Terraform');

🧩 Query Using SPLIT_TO_TABLE()

SELECT
  emp_id,
  skill.value AS skill
FROM employees,
LATERAL SPLIT_TO_TABLE(skills, ',') AS skill;

✅ Output:

🧠 Explanation

• SPLIT_TO_TABLE() turns a string into multiple rows.
• Using LATERAL, we associate the generated rows back to the employee.
• Without LATERAL, the function wouldn’t know which skills value to reference.

✅ Use Case: Transforming CSV-like strings in data ingestion or ETL pipelines.

🧩 Example 3 – Using LATERAL JOIN with SEQUENCE() Function

We can use LATERAL JOIN to generate numeric sequences per row — perfect for generating IDs or partitions.

CREATE OR REPLACE TABLE projects (
  project_id INT,
  num_tasks INT
);

INSERT INTO projects VALUES
(101, 3),
(102, 2);

🧩 Query Using LATERAL SEQUENCE

SELECT
  project_id,
  seq.value::int AS task_number
FROM projects,
LATERAL TABLE(GENERATOR(ROWCOUNT => num_tasks)) AS seq;

✅ Output:

🧠 Explanation

• The GENERATOR() function creates a series of rows.
• LATERAL allows each project to use its num_tasks as a parameter.
• The result: each project expands into multiple task rows dynamically.

✅ Use Case: Generating dynamic iterations, sequences, or expanding metadata for automation.

🧭 ** – Lateral Join Data Flow**

🧠 When to Use Lateral Joins

⚙️ Why Lateral Joins Are Important

🧠 Mnemonic to Remember LATERAL JOIN

L.A.T.E.R.A.L = “Let Another Table Execute Repeatedly Across Left.”

💡 Memory Hook: Think of LATERAL as “Left Table Access Required Always.”

🧩 Difference Between LATERAL JOIN and CROSS JOIN

🧠 How to Remember for Interviews & Exams

Here’s a step-by-step memory map for Lateral Joins:

Practice Trick: Use this template in your head:

SELECT ... FROM t, LATERAL function(t.column) AS alias;

🧭 Why It’s Essential to Learn

1. Real-world JSON handling → APIs, IoT, logs
2. Simplifies ETL → Transform data without Python/Spark
3. Appears in SnowPro certification exams
4. Used by data engineers daily in ingestion pipelines
5. Improves SQL fluency in analytics workflows

🧩 Example 4 – Combining LATERAL with JSON and Aggregation

CREATE OR REPLACE TABLE sales (
  region STRING,
  data VARIANT
);

INSERT INTO sales VALUES
('North', PARSE_JSON('{"sales": [100, 200, 150]}')),
('South', PARSE_JSON('{"sales": [50, 75, 125]}'));

SELECT
  region,
  AVG(value::int) AS avg_sales
FROM sales,
LATERAL FLATTEN(input => data:sales)
GROUP BY region;

✅ Output:

✅ Use Case: Aggregate data within nested structures.

🧩 Example 5 – LATERAL with SPLIT_TO_TABLE() and Aggregation

CREATE OR REPLACE TABLE reviews (
  review_id INT,
  tags STRING
);

INSERT INTO reviews VALUES
(1, 'positive,verified,fast-delivery'),
(2, 'negative,late-shipment');

SELECT
  review_id,
  COUNT(tag.value) AS tag_count
FROM reviews,
LATERAL SPLIT_TO_TABLE(tags, ',') AS tag
GROUP BY review_id;

✅ Output:

🧩 Example 6 – Nested LATERAL JOINs

You can even use multiple LATERAL JOINs together!

CREATE OR REPLACE TABLE orders_multi (
  id INT,
  data VARIANT
);

INSERT INTO orders_multi VALUES
(1, PARSE_JSON('{"items":["Keyboard","Mouse"],"prices":[20,15]}'));

SELECT
  id,
  item.value::string AS product,
  price.value::int AS cost
FROM orders_multi,
LATERAL FLATTEN(input => data:items) AS item,
LATERAL FLATTEN(input => data:prices) AS price;

✅ Output:

🧠 ** – Nested LATERAL JOINs**

⚙️ Performance Considerations

🧠 Common Mistakes

🧩 Real-World Use Cases

🧠 Exam & Interview Preparation Strategy

1. Memorize syntax patterns

   FROM table, LATERAL FLATTEN(input => column)

2. Understand use cases

   • JSON flattening
   • String splitting
   • Dynamic expansion

3. Practice examples on Snowflake UI.

4. Remember difference between CROSS JOIN and LATERAL.

5. Draw diagrams to visualize the flow.

🎯 Summary

🧠 Conclusion

The Snowflake Lateral Join is a cornerstone feature for modern data engineers and analysts. It bridges the gap between structured and semi-structured data, allowing flexible, dynamic transformations without complicated code or external ETL tools.

By mastering LATERAL JOINs, you’ll confidently handle JSON, arrays, and table functions — making your SQL more powerful, reusable, and scalable.