Your First 30 Days with Snowflake

This guide gives you a structured path from complete beginner to confident Snowflake user in just one month. No fluff, no marketing jargon, just the practical stuff you need to know. If you want to understand the technical details behind what makes Snowflake special, start with my deep dive on Snowflake architecture.

Week 1: Getting Started (Days 1-7)

Day 1-2: Account Setup and First Login

Sign up for a free Snowflake trial at signup.snowflake.com. You’ll get $400 in free credits and 30 days to explore. Choose your cloud provider (AWS, Azure, or GCP). It doesn’t matter much for learning, but pick the one your company already uses if you’re planning a production deployment.

After you confirm your email, you’ll land in the Snowsight interface. This is Snowflake’s modern web UI, and it’s where you’ll spend most of your time. Take 15 minutes to click around. Notice the main sections: Worksheets (where you write queries), Data (to browse databases and tables), and Monitoring (to track costs and performance).

Your trial account comes with sample data already loaded. Look for the SNOWFLAKE_SAMPLE_DATA database in the left sidebar. It has datasets like TPCH (transaction processing benchmark) and TPCDS (decision support benchmark). These are perfect for learning.

Day 3-4: First Queries

Open a new worksheet and run your first query:

SELECT * FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER LIMIT 10;

Notice how fast that was? Now try something more interesting:

SELECT 
    c_mktsegment,
    COUNT(*) as customer_count,
    AVG(c_acctbal) as avg_balance
FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER
GROUP BY c_mktsegment
ORDER BY customer_count DESC;

Snowflake uses standard ANSI SQL, so if you know SQL from other databases, you’re already 80% there. The main differences are in how you create and manage objects, which we’ll get to.

Practice writing different types of queries: simple SELECTs, JOINs, aggregations, window functions. The sample data has everything you need. Run at least 20 different queries to get comfortable with the interface.

Day 5-6: Understanding Warehouses

Here’s where Snowflake gets interesting. Click on “Admin” then “Warehouses” in the left sidebar. You’ll see COMPUTE_WH, which is your default virtual warehouse. This is where your queries actually run.

Try creating a second warehouse:

CREATE WAREHOUSE MY_TEST_WH WITH 
    WAREHOUSE_SIZE = 'SMALL'
    AUTO_SUSPEND = 300
    AUTO_RESUME = TRUE;

Now run the same query on both warehouses and compare the performance. Use the query history tab to see timing and credit consumption. This is your first lesson in cost management; bigger warehouses cost more but run faster.

Play with auto-suspend settings. Set one warehouse to suspend after 5 minutes, another after 10 minutes. Watch how they automatically shut down when idle and resume when you run a query. This is how Snowflake can save you money, since you only pay when compute is actually running.

Day 7: Cost Awareness

Go to Account → Usage to see your credit consumption. You’ll notice storage is cheap (pennies per GB per month) but compute can add up. A Medium warehouse costs 4 credits per hour. At $2-3 per credit depending on your contract, that’s $8-12 per hour.

Learn to check query costs before running big jobs. Use the QUERY_HISTORY view:

SELECT 
    query_text,
    warehouse_name,
    total_elapsed_time/1000 as seconds,
    credits_used_cloud_services
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE start_time >= DATEADD(day, -1, CURRENT_TIMESTAMP())
ORDER BY total_elapsed_time DESC
LIMIT 20;

This shows your most expensive queries from the last day. Get in the habit of checking this regularly.

Week 2: Core Concepts (Days 8-14)

Day 8-9: Databases and Schemas

Create your first database and schema:

CREATE DATABASE MY_LEARNING_DB;
CREATE SCHEMA MY_LEARNING_DB.STAGING;
CREATE SCHEMA MY_LEARNING_DB.PRODUCTION;

Snowflake uses a three-level hierarchy: Database → Schema → Table. Think of databases as major projects, schemas as environments or functional areas within those projects, and tables as your actual data.

Practice creating different types of tables:

-- Permanent table (default, persists until dropped)
CREATE TABLE MY_LEARNING_DB.STAGING.CUSTOMERS (
    customer_id INT,
    name VARCHAR(100),
    email VARCHAR(100),
    created_at TIMESTAMP_NTZ
);

-- Temporary table (exists only in session)
CREATE TEMPORARY TABLE temp_calculations AS
SELECT customer_id, COUNT(*) as order_count
FROM MY_LEARNING_DB.STAGING.ORDERS
GROUP BY customer_id;

-- Transient table (no fail-safe, cheaper storage)
CREATE TRANSIENT TABLE MY_LEARNING_DB.STAGING.LOGS (
    log_id INT,
    message VARCHAR(500),
    log_time TIMESTAMP_NTZ
);

Day 10-11: Loading Data

Time to get some real data in there. Start with a simple CSV file. Create a small test file locally with this content:

customer_id,name,email
1,John Smith,john@example.com
2,Jane Doe,jane@example.com
3,Bob Wilson,bob@example.com

Create a stage (a temporary location for files):

CREATE STAGE MY_LEARNING_DB.STAGING.my_stage;

Upload the file through the UI: go to Data → Databases → MY_LEARNING_DB → STAGING → Stages → my_stage, then click “Upload Files”.

Now load it into your table:

COPY INTO MY_LEARNING_DB.STAGING.CUSTOMERS
FROM @MY_LEARNING_DB.STAGING.my_stage/customers.csv
FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER = 1);

Verify it loaded:

SELECT * FROM MY_LEARNING_DB.STAGING.CUSTOMERS;

This is the foundation of all data loading in Snowflake. Everything else builds on the COPY command.

Day 12-13: Query Optimization Basics

Run a query and click on the query ID in the history. This opens the query profile, which is a visual representation of how Snowflake executed your query. Learn to read this. Look for expensive operations (red boxes) and partition pruning stats.

Try queries with and without WHERE clauses on the same data:

-- Without filter (scans everything)
SELECT COUNT(*) FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM;

-- With filter (uses partition pruning)
SELECT COUNT(*) 
FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.LINEITEM
WHERE l_shipdate >= '1998-01-01';

Check the query profiles for both. The second one should scan fewer micro-partitions because Snowflake can eliminate partitions that don’t contain data from 1998 or later.

For more advanced optimization techniques, check out this post on Snowflake query optimization.

Day 14: Time Travel

Snowflake keeps up to 90 days of data history (depending on your edition). Try this:

-- Update some data
UPDATE MY_LEARNING_DB.STAGING.CUSTOMERS 
SET email = 'newemail@example.com' 
WHERE customer_id = 1;

-- Check the change
SELECT * FROM MY_LEARNING_DB.STAGING.CUSTOMERS WHERE customer_id = 1;

-- Go back in time 5 minutes
SELECT * FROM MY_LEARNING_DB.STAGING.CUSTOMERS 
AT(OFFSET => -60*5) 
WHERE customer_id = 1;

You just queried data as it existed 5 minutes ago. This is incredibly powerful for recovering from accidental deletes or updates, or for comparing data across time.

Week 3: Intermediate Features (Days 15-21)

Day 15-16: Views and Materialized Views

Create a regular view:

CREATE VIEW MY_LEARNING_DB.PRODUCTION.customer_summary AS
SELECT 
    c.customer_id,
    c.name,
    COUNT(o.order_id) as order_count,
    SUM(o.order_amount) as total_spent
FROM MY_LEARNING_DB.STAGING.CUSTOMERS c
LEFT JOIN MY_LEARNING_DB.STAGING.ORDERS o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.name;

Views in Snowflake are just stored queries; they don’t store data themselves. Every time you query a view, Snowflake runs the underlying SELECT statement.

Now create a materialized view (requires Enterprise Edition or higher):

CREATE MATERIALIZED VIEW MY_LEARNING_DB.PRODUCTION.customer_summary_mv AS
SELECT 
    c.customer_id,
    c.name,
    COUNT(o.order_id) as order_count,
    SUM(o.order_amount) as total_spent
FROM MY_LEARNING_DB.STAGING.CUSTOMERS c
LEFT JOIN MY_LEARNING_DB.STAGING.ORDERS o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.name;

Materialized views actually store the results and automatically update when the underlying data changes. They cost storage but make queries faster.

Day 17-18: Cloning

One of Snowflake’s best features is zero-copy cloning:

CREATE DATABASE MY_LEARNING_DB_CLONE CLONE MY_LEARNING_DB;

This creates a complete copy of your database in seconds, regardless of size. But it doesn’t actually copy any data, it just creates pointers to the existing micro-partitions. You only start paying for storage when you modify the cloned data.

Use clones for testing, development environments, or giving teams safe sandboxes to experiment in:

-- Create a dev environment
CREATE DATABASE DEV_DB CLONE PROD_DB;

-- Let developers break things without affecting production
GRANT ALL ON DATABASE DEV_DB TO ROLE developer_role;

Day 19-20: User and Role Management

Learn Snowflake’s role-based access control (RBAC). Create some test users and roles:

-- Create roles
CREATE ROLE analyst_role;
CREATE ROLE developer_role;

-- Grant privileges
GRANT USAGE ON DATABASE MY_LEARNING_DB TO ROLE analyst_role;
GRANT USAGE ON SCHEMA MY_LEARNING_DB.PRODUCTION TO ROLE analyst_role;
GRANT SELECT ON ALL TABLES IN SCHEMA MY_LEARNING_DB.PRODUCTION TO ROLE analyst_role;

-- Create user and assign role
CREATE USER test_analyst PASSWORD='SecurePassword123!' DEFAULT_ROLE=analyst_role;
GRANT ROLE analyst_role TO USER test_analyst;

Snowflake’s security model is powerful but can get complex. The key concept: users don’t have direct privileges on objects. Instead, you grant privileges to roles, then assign roles to users.

Day 21: Snowpipe Basics

Snowpipe enables continuous data loading. Set up a simple example:

CREATE PIPE MY_LEARNING_DB.STAGING.customer_pipe AS
COPY INTO MY_LEARNING_DB.STAGING.CUSTOMERS
FROM @MY_LEARNING_DB.STAGING.my_stage
FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER = 1);

Now any time a new file lands in that stage, Snowpipe automatically loads it within minutes. This is how you build near real-time data pipelines without complex scheduling.

Week 4: Advanced Topics (Days 22-30)

Day 22-24: Streams and Tasks

Streams track changes to tables, perfect for CDC (change data capture):

CREATE STREAM customer_stream ON TABLE MY_LEARNING_DB.STAGING.CUSTOMERS;

Now any INSERT, UPDATE, or DELETE on that table gets recorded in the stream. You can query the stream to see what changed:

SELECT * FROM customer_stream;

Tasks let you schedule SQL statements to run automatically:

CREATE TASK process_customer_changes
    WAREHOUSE = COMPUTE_WH
    SCHEDULE = '5 MINUTE'
AS
INSERT INTO MY_LEARNING_DB.PRODUCTION.CUSTOMERS
SELECT customer_id, name, email, created_at
FROM customer_stream
WHERE METADATA$ACTION = 'INSERT';

Combine streams and tasks to build automated ETL pipelines that process changes as they happen.

Day 25-26: Performance Tuning

Learn to read query profiles like a pro. Look for these patterns:

High partition scanning? Add a clustering key to your large tables. Join performance issues? Check if you’re joining on the right columns. Spilling to disk? Increase warehouse size. Result cache misses? Adjust how often you run queries.

Try adding a clustering key to a table:

ALTER TABLE MY_LEARNING_DB.STAGING.ORDERS 
CLUSTER BY (order_date);

This tells Snowflake to organize micro-partitions by order_date, making range queries on that column much faster.

Day 27-28: Cost Optimization Strategies

Review everything you’ve learned about costs. Set up resource monitors to cap spending:

CREATE RESOURCE MONITOR monthly_budget WITH 
    CREDIT_QUOTA = 100
    TRIGGERS
        ON 75 PERCENT DO NOTIFY
        ON 100 PERCENT DO SUSPEND
        ON 110 PERCENT DO SUSPEND_IMMEDIATE;

This sends alerts at 75% usage, suspends warehouses at 100%, and immediately kills all queries at 110%. Adjust the percentages based on your comfort level.

Review your warehouse configurations. Are auto-suspend timeouts appropriate? Are warehouse sizes right for the workload? Are you using separate warehouses for different teams?

Day 29-30: Real Project Planning

Now that you understand the basics, design a real implementation. Consider:

How many warehouses do you need? One for ETL, one for reporting, one for ad-hoc analysis is a good start. What’s your data loading strategy? Batch with COPY or streaming with Snowpipe? How will you organize databases and schemas? By project, by department, by environment?

Document your security model. Which roles need access to what? How will you manage user provisioning? What’s your cost budget and how will you track it?

When planning your Snowflake implementation, consider whether building custom solutions or using Snowflake’s native features makes more sense. This build vs. buy framework can help guide these decisions.

Advanced Capabilities Worth Exploring

Once you’ve mastered the basics, there are several advanced features worth diving into:

If you’re interested in data science and machine learning, explore Snowflake Snowpark which lets you write Python and Scala code that runs directly in Snowflake. For ML workflows specifically, check out our guide on building and serving ML models in Snowflake.

Snowflake also offers built-in AI capabilities through Snowflake Cortex AI, which brings machine learning and generative AI directly into your data warehouse without needing external tools.

The Bottom Line

Thirty days from now, you’ll be comfortable writing queries, loading data, managing warehouses, and optimizing costs. You won’t be an expert – that takes months of production experience – but you’ll be dangerous enough to build real things.

The key is hands-on practice. Don’t just read this guide – actually do the exercises. Create the tables, load the data, run the queries, break things and fix them. Snowflake’s free trial gives you everything you need to learn.

Focus on understanding the core concepts: storage-compute separation, warehouse sizing and management, Time Travel, cloning, and cost control. These fundamentals underpin everything else. Get these right and the advanced features become straightforward.

Most importantly, don’t be intimidated. Snowflake is actually easier to use than traditional databases once you understand the paradigm shift. No index tuning, no vacuum operations, no complicated partitioning schemes. It just works, and it scales effortlessly.