What should they do?

Topic 2, 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 C user data, inventory, static data

3 physical servers

Cassandra C metadata, tracking messages

10 Kafka servers C tracking message aggregation and batch insert Application servers C customer front end, middleware for order/customs

60 virtual machines across 20 physical servers

Tomcat C Java services

Nginx C static content

Batch servers

Storage appliances

iSCSI for virtual machine (VM) hosts

Fibre Channel storage area network (FC SAN) C SQL server storage

Network-attached storage (NAS) image storage, logs, backups

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 wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads.

What should they do?
A . Store the common data in BigQuery as partitioned tables.
B . Store the common data in BigQuery and expose authorized views.
C . Store the common data encoded as Avro in Google Cloud Storage.
D . Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

Answer: B