How to Setup a Data Warehouse for Analytics

#2: Data Pipeline Summer Challenge

Data warehouses are the source of truth of any modern data stack. It is where the data is stored and queried, making it one of the most important components.

Every tool in your data pipeline will touch your data warehouse.

This is why it’s so important to set it up correctly, considering how it interacts with each of your tools.

In today’s rendition of Data Pipeline Summer, we will first learn best practices for setting up your data warehouse for analytics. These include:

• creating databases for different environments

• creating users and roles with the correct access permissions

• understanding the differences between materialization types and when to use each

We will end with this week’s challenge- creating your Snowflake environment and configuring permissions for each of the tools we will use throughout the challenge.

If you missed last week’s email, be sure to give that a read before diving deeper into data warehouses.

Let’s get started!

One last thing- if you’re reading this within minutes of this hitting your inbox, I’m most likely about to go live with Maven Analytics (join here)! We will be discussing all things analytics engineering, so it’s a great chance to ask me all of your hard-hitting questions.

And, if you miss it, I’m hoping to send the session out in an upcoming newsletter.