Google Cloud

Notes for google cloud data engineer certification.

The following list is based on the sample case study for the GCDE certification exam: https://cloud.google.com/certification/guides/data-engineer/casestudy-flowlogistic

The case study focuses on a logistics company tracking orders and shipments via rail, truck, aircraft, and ships.

Also see the tutorials/guides/resources/use cases here: https://cloud.google.com/solutions/

Goals and Motivation

Goals:

• Implement real-time inventory tracking system that tracks locations
• Perform data analytics on order and shipment logs (structured/unstructured data) to make decisions about deploying resources, targeting customers, and expanding into markets
• Predict delays in shipments

Requirements:

• Reliable, reproducible environment that scales
• Aggregated data in centralized data lake
• Historical data used to perform predictive analytics on future shipments
• Accurate tracking of worldwide shipments (proprietary technology)
• Improvement of business agility and speed of innovation via rapid provisioning of new resources
• Analysis and optimization for performance in the cloud
• Migration to cloud, if all other requirements met

Deeper reasoning:

• Inability to upgrade infrastructure hampering growth and efficiency
• Ineffective at moving data around
• Need to better understand where/who customers are, what they are shipping
• IT is too busy managing infrastructure to organize data/build analytics/implement tracking technology
• Penalties for late shipments and deliveries translates into direct correlation between profitability and bottom line

Technology Stack

Databases:

• SQL DB storing user data, static data
  • Google Cloud/MySQL
• Cassandra DB storing metadata, tracking messages
• Kafka servers tracking message aggregation and batch insert

Applications:

• Customer frontend, middleware for orders and customs
• Tomcat for Java services
• Nginx for static content
• Batch servers (?)

Storage:

• iSCSI (internet small-computer-system interface) to manage VM hosts
• Fiber channel network for SQL server storage
• NAS (network attached storage) for image storage, logs, and backups

Analytics:

• Hadoop/Spark servers
• Core data lake
• Data analysis workloads

Miscellaneous servers:

• Jenkins
• Monitoring of servers
• Bastion hosts
• Security scanners
• Billing software

Note: there is a huge list of all possible Google Cloud products to help figure out what products are used for what technologies [1]