Scientific Data Processing: SQL and Earthquakes

GDG Cloud Twin Cities
Wed, Mar 15, 6:00 PM (CDT)

32 RSVP'ed

About this event

Scientific data can be complex and overwhelming, but with the right tools and skills, you can turn it into valuable insights. Join us for a hands-on event featuring two labs that will help you unlock the full potential of your scientific data.  Don't have scientific data?  That's okay too, learn SQL and to use virtual machines.

This week's hands-on labs:

Introduction to SQL for BigQuery and Cloud SQL

In this lab you will learn fundamental SQL clauses and will get hands on practice running structured queries on BigQuery and Cloud SQL.

Rent-a-VM to Process Earthquake Data

In this lab you spin up a virtual machine, configure its security, access it remotely, and then carry out the steps of an ingest-transform-and-publish data pipeline manually. This lab is part of a series of labs on processing scientific data.

With our "Introduction to SQL for BigQuery and Cloud SQL" lab, you will learn fundamental SQL clauses and get hands-on practice running structured queries to extract relevant scientific data. And with our "Rent-a-VM to Process Earthquake Data" lab, you will gain valuable experience in setting up and configuring a data pipeline to process scientific data. Don't let your scientific data go to waste - join us and learn how to make it work for you!

Across the labs we'll:

Learn SQL with a great introduction lab,

set up a VM, and

process earthquake data in the VM, and then share it. 

Whether you are new to SQL or experienced with data processing, this event will provide you with practical skills that you can apply immediately to your data analysis projects. Join us and take your data analysis skills to the next level!

We'll work through the labs to get hands-on experience and when you complete each multi-lab quest, you can earn a badge for your LinkedIn!

Learn Data, ML, AI, or Cloud skills with us one week at a time.

All upcoming events:

#googlecloudplatform #gcp #event #data #dataanalysis


  • Jeffrey Williams

    Jeffrey Williams

    UC Berkeley

    Tech Lead

    See Bio