Advanced 5 Steps 4h 51m 37 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 Big Query, to Dataproc, to Tensorflow, this quest is composed of specific labs that will put your GCP 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.
PrerequisitesThis Quest requires proficiency with GCP 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 GCP 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.
In this lab you will use Google Cloud Dataflow to create a Maven project with the Cloud Dataflow SDK, and run a distributed word count pipeline using the Google Cloud Platform Console.
This lab shows you how to connect and manage devices using Cloud IoT Core; ingest the steam 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 create an instance of Confluent Kafka to communicate with Google Cloud Pub/Sub using source and sink mechanisms.
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 explore millions of New York City yellow taxi cab trips available in a BigQuery Public Dataset, create a ML model inside of BigQuery to predict the fare, and evaluate the performance of your model to make predictions.
In this lab you will build an end to end machine learning solution using Tensorflow + Cloud ML Engine and leverage the cloud for distributed training and online prediction.