Good mooorrrnnning fellow data cruncher!

The Query here 👋

Here’s what we have for you today:

• A special new SQL tip format 🎥

• A concept every data analyst should know 👨‍💻

• The latest open data jobs 💼

• Data humor (the best kind) 🤣

Looking to become a Data Analyst? START HERE.

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I started The Query with one mission: to share the data skills I wish I'd learned earlier.

While you've been loving the SQL tips in our newsletter, they're great for quick insights but not for deep learning.

That's why we're trying something new - a video on our YouTube channel!

This video dives into how to extract specific pieces of text from larger strings using SQL, something I've been wanting to explain in detail.

After watching, share your thoughts and any topics you'd like covered next in the comments.

Check it out and enjoy the deeper dive into SQL!

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remote, data jobs

Open to exploring new job opportunities?

We cultivate the best data analyst jobs from around the internet to make your search easier.

Check out this week’s featured jobs here.

1. Marketing Analytics @ Strategy Collective — $65-85k per year

2. Manager of Business Intelligence @ UTR Sports — $100-135k per year

3. Senior BI Manager @ Acentra Health — $120-125k per year

freelance gigs

Need work experience? Get real experience with real projects.

1. Tableau data analyst — $10-30 per hour (apply here)

2. Looker help — $intermediate (apply here)

3. Excel Data analyst — $intermediate (apply here)

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Column vs. Row Store Databases

Kyle here 👋 — A foundational data concept all data analysts should know is the difference between column and row store databases.

Here's a breakdown that will help you shine in your discussions.

Row Store Databases (RDBMS): Think of a row store database like a traditional spreadsheet where data is stored in rows. This is the most common type of database you might have already used. It's excellent for operations involving the entire row of data, like entering a new record or retrieving all details of a specific entry. It works wonders when you need to process transactions, which is why it's a go-to for online retail, banking systems, and other transaction-oriented applications.

Column Store Databases: Now, imagine instead of storing data by rows, you store it by columns. This is what column store databases do. Each data column is stored separately, making it faster to retrieve all the information in a single column across multiple rows. It's a star performer when dealing with analytics and data warehousing, as these operations often require heavy reading and aggregation of specific attributes (like summing up all sales in a month).

Why Should You Care?

• Performance: In row stores, if you need to access data from a few columns, you end up scanning entire rows which can be slow. Column stores, on the other hand, can quickly fetch only the required columns, speeding up query times.

• Storage Efficiency: Column stores can compress data more effectively because the data in a column is typically more uniform than data in a row.

• Real-world Applications: Knowing the difference can help you suggest the right database for the job. For instance, if your company deals with heavy report generation, a columnar database might be recommended.

Interview Tip: When asked about databases in an interview, show that you understand not only the technical differences but also how these affect business decisions and performance. Mentioning scenarios like data warehousing for column stores and transaction processing for row stores can demonstrate practical knowledge.

Remember, it's not about memorizing definitions; it's about understanding concepts and their impact on the real world of data.

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content & resources 🤓 

1. Become a Data Analyst Guide: Our full guide on what it takes to land a job as a data analyst.

2. Open Data Analyst Jobs: Find your next data job here!

3. Download our SQL Cheatsheet as a PDF and desktop wallpaper here. 

4. LinkedIn: We create content on LinkedIn daily. You can follow Cody here and Kyle here.

5. YouTube: We’re starting to create more content on YouTube. Watch our latest data videos here.

That’s it for today.

Stay crunchin’ folks and see you next week!

— Kyle & Cody

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