7 Advanced SQL Tricks You Don't Know (but you should!)

Advanced SQL to Boost your daily workflows

SQL is easy to learn — but hard to master.

Most data analysts and scientists can SELECT, JOIN, and GROUP BY in their sleep. But there’s a whole world of advanced SQL tricks that can supercharge your queries, speed up analysis, and make you look like a wizard on your team.

Here are 7 powerful SQL features and techniques that even experienced analysts sometimes overlook.

1. LAG() and LEAD() – Look Behind or Ahead Without Self-Joins

These window functions let you access values from previous or next rows — without complicated joins.

SELECT 
  user_id,
  event_date,
  LAG(event_date) OVER (PARTITION BY user_id ORDER BY event_date) AS previous_event
FROM events;

📈 Use Case: Time between user actions, churn detection, or purchase patterns.

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2. FILTER() Clause – Cleaner Aggregations With Conditions

Ever use CASE WHEN just to conditionally count or sum things? FILTER is cleaner.

SELECT
  COUNT(*) AS total_orders,
  COUNT(*) FILTER (WHERE status = 'cancelled') AS cancelled_orders
FROM orders;

🧼 Why it’s great: More readable, especially when you have many conditional aggregations.

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3. WITH (CTEs) – Temporary Tables for Sanity and Reuse

Common Table Expressions (CTEs) make your queries modular, readable, and easier to debug.

WITH high_value_customers AS (
  SELECT customer_id, SUM(amount) AS total_spent
  FROM transactions
  GROUP BY customer_id
  HAVING SUM(amount) > 1000
)
SELECT * FROM high_value_customers;

🧠 Think of it like defining a temp table inside your query — great for complex pipelines.

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4. DISTINCT ON – Fastest Way to Get the "First Row Per Group" (PostgreSQL)

Want the first purchase per user, or most recent login? Instead of a subquery with ROW_NUMBER(), try:

SELECT DISTINCT ON (user_id) user_id, event_date
FROM events
ORDER BY user_id, event_date DESC;

⚡ Caveat: Works in PostgreSQL but not standard SQL. Blazing fast for certain tasks.

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5. UNNEST() – Explode Arrays into Rows

Some databases store arrays or lists in a single field. UNNEST() helps break those out.

SELECT user_id, UNNEST(tags) AS tag
FROM articles;

🔎 Use Case: Expand JSON or array columns to analyze individual elements.

Works in: PostgreSQL, BigQuery, Snowflake, and others.

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6. GENERATE_SERIES() – Create Calendar Tables or Simulated Data

Need a series of dates? Don’t reach for Excel.

SELECT 
  date::date 
FROM generate_series('2023-01-01', '2023-12-31', '1 day') AS date;

📅 Use Case: Join to this to fill in gaps (like missing sales dates).

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7. Materialized Views — Speed Up Repeated Queries

Instead of rerunning an expensive aggregation every time, you can cache it with a materialized view.

CREATE MATERIALIZED VIEW monthly_sales AS
SELECT 
  date_trunc('month', order_date) AS month,
  SUM(amount) AS total_sales
FROM orders
GROUP BY 1;

💡 Pro Tip: Remember to REFRESH MATERIALIZED VIEW when needed.

Final Thoughts

If you’ve been living in basic SQL, these techniques will unlock a whole new level of efficiency, power, and creativity in your queries.

Master these, and you’ll:

• Write more elegant code

• Impress your peers

• Reduce query time on massive datasets

Want more?

💥 Follow me for practical SQL tips, Python tricks, and real-world data workflows.

Have a go-to advanced SQL tip I didn’t mention? Drop it in the comments — let’s build a collection of underrated techniques together.

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