Engineer Data in Google Cloud
Advanced 7 Steps 8 hours 51 Credits
This advanced-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From BigQuery, to Dataprep, to Cloud Composer & Tensorflow, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Complete this quest, including the the challenge lab at the end, to receive an exclusive Google Cloud digital badge. The challenge lab does not provide prescriptive steps, but require solutions to be built with minimal guidance and will put your Google Cloud technology skills to the test!
Prerequisites:This Quest requires proficiency with Google Cloud Services, particularly those relating to working with large datasets. It is recommended that the student have at least earned a Badge by completing the hands-on labs in the Baseline: Data, ML, and AI and/or the Google Cloud Essentials Quests before beginning. Additional lab experience with the Scientific Data Processing and the Machine Learning APIs Quests will be useful.
Cloud Dataprep by Trifacta is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis. In this lab you will explore the Cloud Dataprep UI to build a data transformation pipeline.
This lab shows you how to connect and manage devices using Cloud IoT Core; ingest the stream of information using Cloud Pub/Sub; process the IoT data using Cloud Dataflow; use BigQuery to analyze the IoT data. Watch this short video, Easily Build an IoT Analytics Pipeline.
In this lab you will build several Data Pipelines that will ingest data from a publicly available dataset into BigQuery.
In this lab you will use a newly available ecommerce dataset to run some typical queries that businesses would want to know about their customers’ purchasing habits.
In this lab you will build an end to end machine learning solution using Tensorflow + AI Platform and leverage the cloud for distributed training and online prediction.
In this advanced lab you will create and run an Apache Airflow workflow in Cloud Composer that exports tables from a BigQuery dataset located in Cloud Storage bucktes in the US to buckets in Europe, then import th0se tables to a BigQuery dataset in Europe.
This challenge lab tests your skills and knowledge from the labs in the Engineer Data in Google Cloud quest. You should be familiar with the content of labs before attempting this lab.