Conclusion

Picture this: we’re in the final round of an SQL case study interview.

The interviewer asks us to pull metrics on customer engagement, flag high-risk orders, group recent purchases into weekly buckets, and pivot product categories for internal dashboards. That sounds like a lot to solve in one go, but thanks to what we’ve covered in this course, we know how to tackle each part using proven SQL patterns. We’ve learned to think in patterns, not just queries.

In this final lesson, our goal is to wrap everything up. We’ll reflect on what we’ve learned, how each pattern fits into real-world scenarios, and how we can keep improving our problem-solving with SQL.

Recap of what we’ve built together

Throughout this catalog, we’ve gone far beyond SQL syntax. We built a way of thinking—a problem-solving toolkit for the kinds of data challenges developers and analysts face daily.

Let’s recap the journey by revisiting the five core pattern categories we explored:

Aggregation patterns

These helped us answer questions like:

• How many items were sold?

• What are the top-selling products by category?

• What’s the total revenue by month?

We mastered patterns like Tally Count, Group Buckets, and Rolling Totals. These are fundamental when summarizing or analyzing trends across groups or over time.

Filtering patterns

Here, we focused on cleaning, narrowing down, and preparing data for analysis:

• Patch gaps helped us deal with missing or incomplete records.

• Existence check lets us verify related data’s presence (or absence).

• Deduplication ensured we retained only the most relevant row(s).

• Pattern search enabled smart text-based filtering.

These patterns are often the difference between correct results and misleading insights.

Comparison patterns

This set tackled the challenge of comparing datasets or finding differences:

• Set compare helped us find mismatches or overlaps.

• Join variants expanded our toolbox with anti-joins, semi-joins, and self-joins.

• Nested query lets us go deeper with layered logic.

We learned to ask: “Compared to what?” and write queries that reflect that thinking.

Sequencing and hierarchical patterns

These patterns brought order and structure to the table:

• Rank and row gave us ways to sort and assign positions.

• Lead-lag navigation lets us compare rows across a sequence.

• Recursive tree tackled parent-child relationships.

• Gaps and islands revealed missing records and continuous streaks.

These were especially powerful in time-series and reporting workflows.

Transformation patterns

This category focused on reshaping data:

• Pivot flip helped us convert rows into columns (or vice versa) to get the right layout for analysis or visualization.

While transformations may seem cosmetic, they often unlock the ability to analyze and share insights more effectively.

Full pattern example in action

Let’s walk through a realistic case that uses multiple patterns. Imagine we want to find all customers at risk of churning but haven’t placed an order in the last 30 days.

We can use:

• Existence check to see if a customer has a recent order.

• Filtering logic to isolate churn risk flags.