• Compute Engine

• App Engine

• Networking and Security

• GCP account online

Cloud IAM

• Provides administrators the ability to manage cloud resources centrally by controlling who can take what action on specific resources.

• Understand how IAM works and how rules apply esp. the hierarchy from Organization -> Folder -> Project -> Resources

• Understand IAM Best practices

• Make sure you know the Big Query Access roles

Storage Services

• Understand each storage service options and their use cases.

• Cloud Storage

• Cost-effective object storage for an unstructured data.

• Very important to know the different classes and their use cases esp. Regional and Multi-Regional (frequent access), near line (monthly access) and Cold line (yearly access)

• Understand Signed URL to give temporary access and the users do not need to be GCP users

• Understand permissions – IAM vs ACLs (fine grained control)

Relational Databases

• Know Cloud SQL and Cloud Spanner

• Cloud SQL

• Is a fully-managed service that provides MySQL and PostgreSQL only?

• Limited to 10TB and is a regional service.

Cloud Spanner

• Is a fully managed, mission-critical relational database service.

• Provides a scalable online transaction processing (OLTP) database with high availability and strong consistency at global scale.

• Globally distributed and can scale and handle more than 10TB.

• Not a direct replacement and would need migration

• There are no direct options for Microsoft SQL Server or Oracle yet.

NoSQL

• Know Cloud Datastore and Big Table

• Datastore

• Provides document database for web and mobile applications.

• Datastore is not for analytics

• Understand Datastore indexes and how to update indexes for Datastore

Bigtable

• Provides column database suitable for both low-latency single-point lookups and recalculated analytics

• Understand Bigtable is not for long term storage as it is quite expensive

• Know the differences with HBase

• Know how to measure performance and scale

Data Warehousing

• Big Query

• Provides scalable, fully managed enterprise data warehouse (EDW) with SQL and fast ad-hoc queries.

• Remember it is most suitable for historical analysis.

• Know how to access control tables, columns within tables and query results (hint – Authorized View)

• Be sure to cover the Best Practices including key strategy, cost optimization, partitioning and clustering

• Obviously there is lots of Data and Just Data

• Know the Big Data stack and understand which service fits the different layers of ingest, store, process, analytics, and use

Cloud Storage

• As the medium to store data as data lake

• Understand what class is the best suited and which one provides geo- redundancy.

Cloud Pub/Sub

• As the messaging service to capture real time data esp. IoT

• Is designed to provide reliable, many-to-many, synchronous messaging between applications esp. real time IoT data capture

• How it compares to Kafka

Cloud Dataflow

• To process, transform, transfer data and the key service to integrate store and analytics.

• Know how to improve a Dataflow performance

• Google expects you to know the Apache Beam features as well

• Understand PCollections, Transforms, ParDo and what they do

• Understand windowing and triggers

Cloud Big Query

• For storage and analytics. Remember Big Query provides the same cost- effective option for storage as Cloud Storage

• Understand how Big Query Streaming works

• Know Big Query limitations esp. with updates and inserts

Cloud Dataprep

• To clean and prepare data. It can be used anomaly detection.

• Does not need any programming language knowledge and can be done through graphical interface

• Be sure to know or try hands-on on a dataset

Cloud Dataproc

• To handle existing Hadoop/Spark jobs

• You need to know how to improve the performance of the Hadoop cluster as well :). Know how to configure the Hadoop cluster to use all the cores (hint- spark executor cores) and handle out of memory errors (hint – executor memory)

• How to install other components (hint – initialization actions)

Cloud Data lab

• Is an interactive tool for exploration, transformation, analysis and visualization of your data on Google Cloud Platform?

• Based on Jupiter

Cloud Composer

• Fully managed workflow orchestration service based on Apache Airflow

• Pipelines are configured as directed a cyclic graphs (DAGs)

• Workflow lives on-premises, in multiple clouds, or fully within GCP.

• Provides ability to author, schedule, and monitor your workflows in a unified manner