📱Data Analysis
📱SQL Practice: Basic Queries

📖 Master the Art of Data Storytelling

Data visualization isn’t just about making charts—it’s about telling a story that drives decisions. Here are 15 essential tips to create impactful, clear, and engaging visualizations that your audience will actually understand and remember:

✅ Ask the right questions to uncover meaningful insights
✅ Choose the right chart to match your story
✅ Keep it simple—remove distracting fonts and elements
✅ Use consistent colors and make labels clear and visible
✅ Design for comprehension, not confusion

🔅 Distributed Databases with Apache Ignite

📝 Deep dive into learning about and creating distributed databases with Apache Ignite.

🌐 Author: Janani Ravi
🔰 Level: Intermediate
⏰ Duration: 1h 55m

📋 Topics: Apache Ignite, Distributed Databases

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📱Data Analysis
📱Distributed Databases with Apache Ignite

📦 Exercise Files

📖 SQL execution order

A SQL query executes its statements in the following order:

1) FROM / JOIN
2) WHERE
3) GROUP BY
4) HAVING
5) SELECT
6) DISTINCT
7) ORDER BY
8) LIMIT / OFFSET

The techniques you implement at each step help speed up the following steps. This is why it’s important to know their execution order. To maximize efficiency, focus on optimizing the steps earlier in the query.

With that in mind, let’s take a look at some optimization tips:

1) Maximize the WHERE clause

This clause is executed early, so it’s a good opportunity to reduce the size of your data set before the rest of the query is processed.

2) Filter your rows before a JOIN

Although the FROM/JOIN occurs first, you can still limit the rows. To limit the number of rows you are joining, use a subquery in the FROM statement instead of a table.

3) Use WHERE over HAVING

The HAVING clause is executed after WHERE & GROUP BY. This means you’re better off moving any appropriate conditions to the WHERE clause when you can.

4) Don’t confuse LIMIT, OFFSET, and DISTINCT for optimization techniques

It’s easy to assume that these would boost performance by minimizing the data set, but this isn’t the case. Because they occur at the end of the query, they make little to no impact on its performance.