The Query (aka Kyle and Cody) here 👋

Here’s what we have for you today:

• A database concept every analyst should know 🧠

• One of the best data projects yet 📊

• Contract to hire freelance jobs 🤝

• Remote full time jobs 💼

Looking to become a Data Analyst? START HERE.

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remote, data jobs

Because who likes writing SQL from a busy office?

1. Data Analyst, CRM @ US Foods — $69-109k per year (apply here)

2. Data Analyst @ Parade — $90-120k per year (apply here)

3. Data Analyst @ Rinsed — $120-135k per year (apply here)

freelance gigs

Need work experience? Get real experience with real projects.

1. Data Analyst to Join Our Team — $ entry level (apply here)

2. SQL Query Help — $$ intermediate level (apply here)

3. Marketing Data Analyst — $40-78 per hour (apply here)

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This week, we’re featuring a realistic DVD Rental Database to hone your SQL skills.

It’s rare to get access to realistic practice datasets. Usually they only contain one or two tables. But in the real world your data comes from many different tables, and you as the analyst have to figure out how everything ties together.

That’s why I like this dataset from postgresqltutorial so much. It has 15 tables that represent what a real DVD Rental company database might look like.

Try out the tutorial on their site and you’ll get some great experience with realistic data!

If you do an analysis of any of these make sure to share it on Linkedin and tag Cody and Kyle in your post!

class SQLMiniLesson:

Understanding Column vs. Row Store Databases

Kyle here 👋 — Are you gearing up for data analyst interviews? One key area you might encounter 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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With great power comes great responsibility.

content & resources 🤓 

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

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

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

4. Courses: Our course on showcasing your data portfolio is live!

That’s it for today.

Stay crunchin’ folks and see you next week!

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