[UPDATED CONTENT 2019 Exam]Storage SolutionsOLAP vs OLTP databasesConsistency concepts.Transactional consistency for various data storage solutions.Cloud StorageGsutil command line interface.DatastoreDatastore indexing - what is it, how to update, upload.BigQueryUpdate of BigQuery practicals including authorised views in the new BQ UI.Concepts of temporary tables.Types of schemas BQ accepts.BigTableBigTable fit for purpose of time-series data.Cbt command line interface for BigTable.BigTable consistency concepts and highly available configuration.DataflowDeploying dataflow jobs and what’s running in the background.Dataflow job monitoring through console -> Cloud Dataflow Monitoring Interface and also gcloud dataflow commands.Updating a dataflow streaming job on the fly.Logging of Cloud Dataflow jobs.Cloud Dataflow Practical - Running job locally and using Dataflow ServiceHadoop & DataprocApache Spark jobsStackdriverExport logs to BigQuery for further analysis, why and how.Machine Learning Solutions - New SectionIntroduction of new GCP ML products and open source products such as Cloud Machine Learning Engine, BigQuery ML, Kubeflow & Spark MLCloud AutoML -> AutoML Vision, AutoML Vision EdgeDialogflow - GCP’s Chatbot builderConcept of edge computing and distributed computingGoogle cloud’s TPU (Tensor Processing Unit)Common t
What you'll learn
Understand OLAP vs OLTP databases
Familiarize with Cloud Storage and its command line interface
Practice updating BigQuery schemas and using temporary tables
Monitor Dataflow jobs and deploy them effectively
Apply machine learning solutions using Google Cloud products
Course objectives
Equip students with practical skills in data engineering using Google Cloud
Provide insight into machine learning tools and their applications