Flowlogistic Case Study Company Overview 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. Company Background The company started as a regional trucking company, and then expanded into other logistics market. 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. Solution Concept 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 markets to expand info. 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:
• Databases 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, Business Requirements
• Build a reliable and reproducible environment with scaled panty 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 new resources
• Analyze and optimize architecture for performance in the cloud
• Migrate fully to the cloud if all other requirements are met Technical Requirements
• Handle both streaming and batch data
• Migrate existing Hadoop workloads
• Ensure architecture is scalable and elastic to meet the changing demands of the company.
• Use managed services whenever possible
• Encrypt data flight and at rest
• Connect a VPN between the production data center and cloud environment SEO Statement 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. CTO Statement 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. CFO Statement Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out 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. Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?
Select one answer.
Marked answer: A
Community Discussion
Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.
Chose A1
A is right answer
1
Given the requirements for ingesting data from global sources, processing and querying in real-time, and storing the data reliably for the real-time inventory tracking system, the most suitable combination of Google Cloud Platform (GCP) products is: A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage Explanation: Cloud Pub/Sub: It is a messaging service that allows you to asynchronously send and receive messages between independent applications. Cloud Dataflow: It can handle both streaming and batch data, making it suitable for real-time processing of data from various sources. Cloud Storage: Cloud Storage can be used to store the processed and analyzed data reliably. It provides scalable, durable, and globally accessible object storage, making it suitable for storing large volumes of data.
Chose A2
A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage Here's why this combination is suitable: Cloud Pub/Sub: It is used for ingesting real-time data from various global sources. It's a messaging service that can handle large volumes of data and is highly scalable. Cloud Dataflow: It's a stream and batch data processing service that allows you to process and analyze the data in real-time. It can take data from Pub/Sub and perform transformations or aggregations as needed. Cloud Storage: It provides reliable storage for the data. You can store the processed data in Cloud Storage for further analysis, and it is a scalable and durable storage solution. Option B is not ideal because Local SSDs are not a suitable storage option for persisting data that needs to be reliably stored. Option C includes Cloud SQL, which is not typically used for ingesting and processing real-time data. Option D includes Cloud Load Balancing, which is not relevant to the use case of ingesting and processing data for the inventory tracking system.
Keyboard:←→previous / next·A–Echoose·Entercheck, then next
