Ian Klosowicz
The best free places to practice SQL are BigQuery's public sandbox, Mode Analytics, SQLiteOnline.com, and Kaggle notebooks. All of them let you write real queries against real data with no credit card and no local install required.
I learned SQL without paying for a single tool. The free options are good enough to get you interview-ready, and in some cases they're the exact environments you'll use at work.
There are two kinds of free SQL tools: ones that give you a sandbox with pre-loaded data, and ones that let you bring your own. Both are useful. The sandbox tools remove friction for getting started. The bring-your-own tools build the muscle of working with real, unfamiliar data.
Here's what's worth your time:
Each has a different use case. None costs money.
BigQuery's free sandbox is the best tool on this list if you want to practice in the environment that most data teams actually use.
The sandbox gives you 1 TB of query processing per month at no cost. You don't need a credit card to activate it -- just a Google account. It runs real SQL (BigQuery's dialect is close to standard SQL with a few quirks), and it has dozens of public datasets built in: NYC taxi trips, Wikipedia pageviews, GitHub activity, U.S. census data, StackOverflow posts.
The StackOverflow dataset alone is worth learning BigQuery for. You can query 50 million posts, filter by tags, join questions to answers, and use window functions to rank users by reputation change over time. That's a genuine analytical task, not a homework problem.
The one limitation: BigQuery uses its own SQL dialect. DATE_DIFF, ARRAY_AGG, and a few other functions work differently than in PostgreSQL or MySQL. The core syntax is identical. The edge cases vary. If your target company uses BigQuery specifically, start here. If you're not sure what they use, BigQuery is still a fine choice because the fundamentals transfer.
To get started: go to console.cloud.google.com, create a project (free), switch to sandbox mode, and open the BigQuery editor. The public datasets appear in the left panel under "bigquery-public-data."
Mode is the lowest-friction option on this list. You open a browser, sign up for a free account, and you're writing SQL against pre-loaded tables inside 5 minutes.
The public warehouse has datasets covering e-commerce orders, marketing campaigns, user events, and web analytics -- the kinds of tables you'd actually query at a data analyst job. The editor is clean, results appear inline, and you can save queries to come back to later.
Mode uses a PostgreSQL-compatible dialect. That's a good choice for practice because PostgreSQL is the most commonly taught SQL flavor and the one closest to what you'll see in entry-level analyst interviews.
The downside is the datasets are fixed. You can't bring in your own data on the free plan. But if you just want a place to write JOINs, window functions, and aggregations without any setup, Mode is the fastest way in.
These are paste-and-go tools. You write a CREATE TABLE statement, insert some rows, and run queries against it -- all in the browser, no account required.
SQLiteOnline.com runs SQLite. DB Fiddle (dbfiddle.uk) lets you pick the SQL flavor: PostgreSQL, MySQL, SQLite, SQL Server, and others.
These are most useful for 3 specific situations:
They're not great for sustained practice because you have to build the schema yourself and the datasets are tiny. Use them as a scratchpad, not a primary training environment.
Kaggle is primarily known for machine learning competitions, but it's one of the best free SQL practice environments for one specific reason: hundreds of real-world datasets are attached directly to the notebook environment, and you can query them with SQL without downloading anything.
The workflow: create a Kaggle account, open a new notebook, attach a dataset, and use the Pandas read_sql function or the built-in SQLite interface to query it. The datasets range from Spotify track metadata to FIFA player ratings to Airbnb listings to public health data.
The main friction point is that Kaggle notebooks are Python-first. If you're not comfortable with Python yet, you'll spend time fighting the environment instead of practicing SQL. In that case, start with Mode or BigQuery instead.
If you're comfortable with Python or planning to learn both, Kaggle is worth it. The combination of real datasets and an integrated environment builds the exact workflow you'll use as a working analyst.
LeetCode has a dedicated SQL section with structured problems ranging from beginner to hard. You get a schema, a problem description, and an editor. Write a query, submit it, and it either passes or it doesn't.
It's useful for 1 thing: building speed and pattern recognition on JOIN, GROUP BY, and window function problems under simulated interview pressure. The top SQL problems on LeetCode (the 50-question study plan) cover most of what entry-level analyst interviews actually test.
The limitation is that LeetCode SQL has nothing to do with real analytical work. The problems are isolated puzzles, not business questions. You won't build any intuition for data exploration, query optimization, or working with messy data. Use it to sharpen interview technique, not as a primary learning environment.
If you're actively interviewing, 30 minutes a day on LeetCode SQL for 2 to 3 weeks before interviews is a good use of time. Outside of that window, spend your practice hours on real data.
Practice is most useful when it is aimed at the SQL an entry-level role actually tests rather than random puzzles.
If you want to practice on your own data -- a CSV you downloaded, a spreadsheet you've been maintaining, anything -- the fastest way is DuckDB or SQLite. Both are free, both install in under 5 minutes, and both run SQL directly against CSV files without any import step.
DuckDB is the better choice for most analysts right now. It reads Parquet and CSV natively, handles larger files without slowing down, and its SQL dialect is close to standard. To query a CSV file: install DuckDB, open the CLI, and run SELECT * FROM 'yourfile.csv' LIMIT 10. That's it.
SQLite is older and more widely documented. If you get stuck, more tutorials exist for SQLite than DuckDB. Either works for analyst practice.
I use SQL every day at work in Snowflake and Coalesce. The dialect is different from SQLite, but the thinking is the same. If you learn to write clean CTEs and think about data shapes in DuckDB, moving to Snowflake or BigQuery takes a day, not a week.
Pick based on your current situation:
Don't stack all of them at once. Pick 1, spend 2 weeks getting reps in, and move on if it's not working. Switching environments every 3 days is how you spend all your time on setup instead of SQL.
The structure that works best is a daily task list with specific query prompts -- something that tells you what to write each day so you're not starting from a blank page. That's what Analyst Hive is built around: a day-by-day program that covers SQL, Excel, and Power BI in the context of building portfolio projects and getting hired. Check it out at analysthive.io.
Picking a platform is the easy part; the harder part is structuring your practice so SQL actually sticks instead of evaporating a week later.
Is there a completely free SQL practice site with no sign-up required?
Yes. SQLiteOnline.com and DB Fiddle both run SQL in the browser with no account. They're limited to small schemas you build yourself, which makes them better for syntax testing than sustained practice. For real datasets with no signup, BigQuery requires a Google account but no payment method in sandbox mode.
What's the best free SQL environment for beginners?
Mode Analytics. It has a clean editor, PostgreSQL-compatible SQL, and pre-loaded datasets covering realistic business scenarios. You're writing queries against real tables in under 5 minutes with no local setup. Most beginners get further faster there than anywhere else because the friction is the lowest.
Can I practice SQL without installing anything?
Yes. BigQuery, Mode, Kaggle, LeetCode, SQLiteOnline, and DB Fiddle all run entirely in the browser. The only reason to install something locally is if you want to query your own files or need offline access. DuckDB and SQLite are the easiest local installs if you get there.
Is LeetCode SQL worth it for data analyst interviews?
It's worth doing the top 50 SQL problems in the 2 to 3 weeks before an interview. It builds pattern recognition for the kinds of JOIN and window function questions that show up in technical screens. It doesn't teach you anything about real analytical work, so don't use it as your only practice source outside of interview prep mode.
Does it matter which SQL dialect I practice in?
Not much at the beginner level. The core syntax -- SELECT, FROM, WHERE, GROUP BY, JOIN, window functions, CTEs -- is consistent across PostgreSQL, MySQL, BigQuery, and Snowflake. Dialect differences show up in date functions, string functions, and a few edge cases. Once you know SQL well in one dialect, the others take a day or two to adjust to.
How much does BigQuery actually cost for SQL practice?
Nothing for typical practice volume. The sandbox gives you 1 TB of query processing per month for free, and most practice queries process a few megabytes at most. You'd have to run thousands of full-table scans on massive public datasets to get close to the limit. For learning purposes, treat it as free.
If you want a structured way to build your SQL skills alongside your resume, LinkedIn, and portfolio, Analyst Hive covers all of it in a day-by-day program. No guessing what to do next. Learn more at skool.com/analysthive/about.
