Become an
Analytics Engineer
These are the onboarding training that we require each of our own Analytics Engineers to complete to start ramping up on knowledge. We are sharing this info with everyone since the more people that know the modern data stack the better we all become!
We are providing the links below without warranty or support since the links will redirect you to websites that are not controlled or maintained by DataLakeHouse or AICG. We will do our best to maintain these links and add relevant content as it becomes available. If you have a suggestion for additional info to be added to this doc please let us know via our DLH Slack Channel.
We believe any hands-on labs MUST BE completed and not just read for quick understanding. It is best to do the training below within your first 30 days so there is continuity in the learning process.
We are a Linux (Ubuntu) and Mac team, so if there is the ability to NOT use Windows then we would prefer that for the engineer so we can have better synchronization between engineers/developers and usually less issues 🙂
LINKS
Review and bookmark the links found via the Common Links doc
Complete the Level Up: First Concepts self-paced which will introduce you to Snowflake via a video series
Complete the Data Warehousing Workshop hands-on course by Snowflake which will walk you through step by step on how to navigate and work within Snowflake
Complete all information in the links in this Snowflake University setup.
Review the Getting Started with dbt training section below
Data Vault introduction using Snowflake, hands-on training
How to talk about Data Vault’s to various audiences
dbt™ is a transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.
Getting started with dbt
Sign up for dbt Slack channel in order to be part of the developer community
Discourse is a place for community members to share their tactics when using dbt. Think articles like:
These articles normally involve code snippets, and might delve into the “why”, or discuss trade offs. Typically, these discussions don’t have a “one-size-fits-all” approach. dbt YouTube channel which has a lot of useful information. Please subscribe
