Views and materialized views in Snowflake - Mini Project: Build & Apply

Creating Views and Materialized Views in Snowflake

📖 Scenario: You work as a data engineer for a retail company. Your team wants to create reusable queries to analyze sales data efficiently. You will create a simple view and a materialized view in Snowflake to help the analysts get quick insights without rewriting queries every time.

🎯 Goal: Build a standard view and a materialized view in Snowflake using sales data. The view will show total sales per product category. The materialized view will pre-aggregate total sales per region for faster queries.

📋 What You'll Learn

Create a table called sales with columns product_id, category, region, and amount.

Create a view called category_sales_view that sums amount grouped by category.

Create a materialized view called region_sales_mv that sums amount grouped by region.

💡 Why This Matters

🌍 Real World

Views and materialized views help data teams provide reusable, efficient queries for business analysts and applications without duplicating data.

💼 Career

Data engineers and analysts often create views and materialized views to optimize query performance and simplify data access in cloud data warehouses like Snowflake.

1

Create the sales table with sample data

Write a SQL statement to create a table called sales with columns product_id (integer), category (string), region (string), and amount (number). Insert these exact rows: (1, 'Electronics', 'North', 100), (2, 'Clothing', 'South', 50), (3, 'Electronics', 'East', 75), (4, 'Clothing', 'North', 60).

Snowflake

-- Create the sales table and insert sample data
-- Your code here

Hint

Use CREATE OR REPLACE TABLE to define the table and INSERT INTO to add rows.

2

Create the category_sales_view view

Write a SQL statement to create or replace a view called category_sales_view. This view should select category and the sum of amount as total_sales from the sales table, grouped by category.

Snowflake

CREATE OR REPLACE TABLE sales (
    product_id INT,
    category STRING,
    region STRING,
    amount NUMBER
);

INSERT INTO sales (product_id, category, region, amount) VALUES
(1, 'Electronics', 'North', 100),
(2, 'Clothing', 'South', 50),
(3, 'Electronics', 'East', 75),
(4, 'Clothing', 'North', 60);

-- Create the category_sales_view view
-- Your code here

Hint

Use CREATE OR REPLACE VIEW and a GROUP BY query.

3

Create the region_sales_mv materialized view

Write a SQL statement to create or replace a materialized view called region_sales_mv. This materialized view should select region and the sum of amount as total_sales from the sales table, grouped by region.

Snowflake

CREATE OR REPLACE TABLE sales (
    product_id INT,
    category STRING,
    region STRING,
    amount NUMBER
);

INSERT INTO sales (product_id, category, region, amount) VALUES
(1, 'Electronics', 'North', 100),
(2, 'Clothing', 'South', 50),
(3, 'Electronics', 'East', 75),
(4, 'Clothing', 'North', 60);

CREATE OR REPLACE VIEW category_sales_view AS
SELECT category, SUM(amount) AS total_sales
FROM sales
GROUP BY category;

-- Create the region_sales_mv materialized view
-- Your code here

Hint

Use CREATE OR REPLACE MATERIALIZED VIEW with a grouped query.

4

Verify the views and materialized view exist

Write SQL statements to select all columns from category_sales_view and from region_sales_mv to verify they return the aggregated sales data.

Snowflake

CREATE OR REPLACE TABLE sales (
    product_id INT,
    category STRING,
    region STRING,
    amount NUMBER
);

INSERT INTO sales (product_id, category, region, amount) VALUES
(1, 'Electronics', 'North', 100),
(2, 'Clothing', 'South', 50),
(3, 'Electronics', 'East', 75),
(4, 'Clothing', 'North', 60);

CREATE OR REPLACE VIEW category_sales_view AS
SELECT category, SUM(amount) AS total_sales
FROM sales
GROUP BY category;

CREATE OR REPLACE MATERIALIZED VIEW region_sales_mv AS
SELECT region, SUM(amount) AS total_sales
FROM sales
GROUP BY region;

-- Select from category_sales_view and region_sales_mv
-- Your code here

Hint

Use simple SELECT * FROM queries to check the views.

Practice

1. What is the main difference between a view and a materialized view in Snowflake?

easy

A. A view does not store data, while a materialized view stores query results.

B. A view stores data permanently, but a materialized view does not.

C. A materialized view updates automatically with every query, but a view does not.

D. A view can only be used once, while a materialized view can be reused multiple times.

Solution

1. Step 1: Understand what a view does

   A view saves a query for reuse but does not store the actual data; it runs the query each time.

2. Step 2: Understand what a materialized view does

   A materialized view stores the results of the query physically, so it can return data faster without rerunning the query.

3. Final Answer:

   A view does not store data, while a materialized view stores query results. -> Option A

4. Quick Check:

   View = no stored data, Materialized view = stored data [OK]

Hint: Remember: views save queries, materialized views save data [OK]

Common Mistakes:

• Thinking views store data permanently
• Believing materialized views update instantly with every query
• Confusing reuse capability between views and materialized views

2. Which of the following is the correct syntax to create a materialized view in Snowflake?

easy

A. CREATE VIEW my_view MATERIALIZED AS SELECT * FROM my_table;

B. CREATE MATERIALIZED VIEW my_view AS SELECT * FROM my_table;

C. CREATE MATERIALIZED my_view VIEW AS SELECT * FROM my_table;

D. CREATE VIEW MATERIALIZED my_view AS SELECT * FROM my_table;

Solution

1. Step 1: Recall Snowflake syntax for materialized views

   The correct syntax starts with CREATE MATERIALIZED VIEW followed by the view name and query.

2. Step 2: Check each option's order and keywords

   Only CREATE MATERIALIZED VIEW my_view AS SELECT * FROM my_table; uses the correct order and keywords: CREATE MATERIALIZED VIEW my_view AS SELECT * FROM my_table;

3. Final Answer:

   CREATE MATERIALIZED VIEW my_view AS SELECT * FROM my_table; -> Option B

4. Quick Check:

   Correct syntax = CREATE MATERIALIZED VIEW [OK]

Hint: Materialized view syntax starts with CREATE MATERIALIZED VIEW [OK]

Common Mistakes:

• Mixing order of keywords
• Placing MATERIALIZED after VIEW
• Using incorrect keyword sequences

3. Given the following Snowflake SQL code:

CREATE TABLE sales (id INT, amount FLOAT);
INSERT INTO sales VALUES (1, 100.0), (2, 200.0);
CREATE MATERIALIZED VIEW sales_mv AS SELECT SUM(amount) AS total FROM sales;
INSERT INTO sales VALUES (3, 300.0);
SELECT * FROM sales_mv;

What will the SELECT query return?

medium

A. total = 600.0

B. total = 300.0 (only new rows)

C. total = 300.0 plus previous sums

D. total = 300.0

Solution

1. Step 1: Understand materialized view refresh behavior

   Materialized views in Snowflake do not automatically update after data changes; they show data as of last refresh.

2. Step 2: Analyze the timing of inserts and query

   The materialized view was created after inserting two rows (100.0 + 200.0 = 300.0). The third row (300.0) was inserted after the view creation but before the select.

3. Final Answer:

   total = 300.0 -> Option D

4. Quick Check:

   Materialized view shows data at last refresh, not latest inserts [OK]

Hint: Materialized views show data as of last refresh, not latest inserts [OK]

Common Mistakes:

• Assuming materialized views auto-update instantly
• Adding new rows to materialized view results without refresh
• Confusing materialized views with normal views

4. You created a materialized view in Snowflake but it returns outdated data after table updates. What is the best way to fix this?

medium

A. Use a normal view instead of a materialized view to get fresh data.

B. Drop and recreate the materialized view every time data changes.

C. Manually refresh the materialized view using ALTER MATERIALIZED VIEW ... REFRESH.

D. Restart the Snowflake warehouse to update the materialized view.

Solution

1. Step 1: Identify how to update materialized views

   Materialized views do not update automatically; they require manual refresh to show latest data.

2. Step 2: Choose the correct refresh method

   Snowflake supports manual refresh with ALTER MATERIALIZED VIEW ... REFRESH to update the stored data.

3. Final Answer:

   Manually refresh the materialized view using ALTER MATERIALIZED VIEW ... REFRESH. -> Option C

4. Quick Check:

   Manual refresh updates materialized view data [OK]

Hint: Use ALTER MATERIALIZED VIEW ... REFRESH to update data [OK]

Common Mistakes:

• Dropping and recreating instead of refreshing
• Restarting warehouse has no effect on view data
• Confusing materialized views with normal views for freshness

5. You want to speed up queries that aggregate large sales data but also need the most current totals. Which approach best balances speed and freshness using Snowflake views?

hard

A. Use a materialized view and schedule frequent refreshes to keep data updated.

B. Use a normal view only, since it always shows fresh data but may be slower.

C. Create a materialized view and never refresh it to save compute costs.

D. Use both a normal view and a materialized view simultaneously in the same query.

Solution

1. Step 1: Understand trade-offs between views and materialized views

   Normal views provide fresh data but can be slow on large data; materialized views are fast but need refresh to update.

2. Step 2: Find a balanced solution

   Scheduling frequent refreshes of materialized views keeps data reasonably fresh while improving query speed.

3. Final Answer:

   Use a materialized view and schedule frequent refreshes to keep data updated. -> Option A

4. Quick Check:

   Materialized view + refresh = speed + freshness balance [OK]

Hint: Schedule refreshes on materialized views for fresh and fast data [OK]

Common Mistakes:

• Not refreshing materialized views and expecting fresh data
• Using only normal views and ignoring performance
• Trying to combine views in one query without refresh strategy