This is a sample case study that may be used on the Google Data Engineer Certification exam. It describes a fictitious business and solution concept to provide additional context to exam questions.
Flowlogistic Case Study
Flowlogistic is a leading logistics and supply chain provider. They help businesses
throughout the world manage their resources and transport them to their final
destination. The company has grown rapidly, expanding their offerings to include
rail, truck, aircraft, and oceanic shipping.
The company started as a regional trucking company, and then expanded into other
logistics markets. Because they have not updated their infrastructure, managing and
tracking orders and shipments has become a bottleneck. To improve operations,
Flowlogistic developed proprietary technology for tracking shipments in real time
at the parcel level. However, they are unable to deploy it because their technology
stack, based on Apache Kafka, cannot support the processing volume. In addition,
Flowlogistic wants to further analyze their orders and shipments to determine how
best to deploy their resources.
Flowlogistic wants to implement two concepts using the cloud:
- Use their proprietary technology in a real-time inventory-tracking system that
indicates the location of their loads.
- Perform analytics on all their orders and shipment logs, which contain both
structured and unstructured data, to determine how best to deploy resources, which
customers to target, and which markets to expand into. They also want to use
predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
- 8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
- 3 physical servers
- Cassandra - metadata, tracking messages
- 10 Kafka servers - tracking message aggregation and batch insert
- Application servers - customer front end, middleware for order/customs
- 60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
- Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
- Network-attached storage (NAS) - image storage, logs, backups
- 10 Apache Hadoop / Spark Servers
- Core Data Lake
- Data analysis workloads
- 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts, security scanners, billing software
- Build a reliable and reproducible environment with scaled parity of production.
- Aggregate data in a centralized Data Lake for analysis.
- Use historical data to perform predictive analytics on future shipments.
- Accurately track every shipment worldwide using proprietary technology.
- Improve business agility and speed of innovation through rapid provisioning of
- Analyze and optimize architecture for performance in the cloud.
- Migrate fully to the cloud if all other requirements are met.
- Handle both streaming and batch data.
- Migrate existing Hadoop workloads.
- Ensure architecture is scalable and elastic to meet the changing demands of the
- Use managed services whenever possible.
- Encrypt data in flight and at rest.
- Connect a VPN between the production data center and cloud environment.
We have grown so quickly that our inability to upgrade our infrastructure is really
hampering further growth and efficiency. We are efficient at moving shipments
around the world, but we are inefficient at moving data around. We need to organize
our information so we can more easily understand where our customers are and what
they are shipping.
IT has never been a priority for us, so as our data has grown, we have not invested
enough in our technology. I have a good staff to manage IT, but they are so busy
managing our infrastructure that I cannot get them to do the things that really
matter, such as organizing our data, building the analytics, and figuring out how
to implement the CFO’s tracking technology.
Part of our competitive advantage is that we penalize ourselves for late shipments
and deliveries. Knowing where our shipments are at all times has a direct
correlation to our bottom line and profitability. Additionally, I don’t want to
commit capital to building out a server environment.
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