Just enough Terraform for Data Analytics on GCP

1. About

This is a self paced hands-on lab that strives to demystify using Terraform to provision Data Analytics services on GCP. Not all Data Analytics services are covered. This lab does not feature any dazzling demos - the focus is purely environment provisioning-configuring automation.

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2. Target Audience

Terraform noobs

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3. Duration

~90 minutes and then some to destroy the environment

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4. Format

Self-paced, fully scripted, no research is required.

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5. Terraform Provider/Data Analytics Services Scope

The following is a subset of Google Data Analytics portfolio covered. Its not all-encompassing. Contributions are welcome.

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6. Prerequisites

Applicable for Google Customer Engineers, in Argolis-
Grant yourself Organization Policy Administrator role from Cloud Console, at an Organization scope
(If you have done created VPC native services in Argolis, you have likely already granted yourself this permission
Don't forget to set the context in the Cloud console back to the project you created.

7. Lab Goals

1. Understanding of core Terraform concepts
2. Knowledge of creating a set of Data Analytics services
3. Terraform scripts that can be repurposed for your trails, PoCs, projects and such

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8. Resources

Hashicorp docs: https://registry.terraform.io/providers/hashicorp/google/4.41.0
Production hardened Terraform Blueprints: https://github.com/GoogleCloudPlatform/cloud-foundation-fabric

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9. Google Cloud Collaborator Credits

#	Google Cloud Collaborators	Contribution
1.	Anagha Khanolkar	Vision, author
2.	Rick Chen	Terraform expertise, feedback
3.	Jay O' Leary	Testing and feedback

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10. Lab flow

Optional exercises:

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11. Get started with the lab

The lab modules listed are best run sequentially to avoid dependency issues.
Each module has an explanation of exactly what is covered, and covers one service at a time to make it bite-sized.

#	Module
01	Module-01: Setup Foundations
02	Module-02: Enable Google APIs and update Organization Policies
03	Module-03: Create user managed service account and grant IAM roles
04	Module-04: Create Network, Subnet, Firewall rule(s), Reserved IP addreess, VPC peering, Router and NAT
05	Module-05: Create Cloud Storage buckets
06	Module-06: Upload objects to Cloud Storage
07	Module-07: Create BigQuery objects
08	Module-08: Create Dataproc Persistent Spark History Server
09	Module-09: Create a Dataproc Metastore Service
10	Module-10: Create a Dataproc Cluster on GCE
11	Module-11: (Optional) Run a PySpark notebook on Dataproc Cluster on GCE on a BigLake table
12	Module-12: (Optional) Run a Serverless Spark batch job
13	Module-13: Run a shell script
14	Module-14: Customize a file with Terraform
15	Module-15: Provision Vertex AI Workbench Notebook instances
16	Module-16: (Optional) Run a Python Notebook on the User Managed Notebook instance on Vertex AI Workbench
17	Module-17: (Optional) Run a PySpark Notebook on the Managed Notebook instance on Vertex AI Workbench
18	Module-18: Provision Cloud Composer
19	Module-19: Other helpful Terraform commands
20	Module-20: Running this lab in one shot

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12. Dont forget to

Shut down the environment. Module 19 covers the same.

13. Contributions welcome

Community contribution to improve the lab is very much appreciated.

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14. Getting help

If you have any questions or if you found any problems with this repository, please report through GitHub issues.

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15. Want something added to this lab?

Log an entry in GitHub issues

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