The Customer is one of the largest Internet of Vehicles companies in the EU that delivers fleet and asset management solutions. The Customer collects, stores and processes IoT data from 600,000 vehicles connected to their systems to enable the users of their solutions to make informed decisions.
Having a robust IoT data collection and storage solution, the Customer was looking to enhance their big data analytics capabilities. Their existing solution is based on Apache Cassandra. Being a key-value storage, Apache Cassandra doesn’t support efficient big data analytics well enough. For example, currently, to retrieve data for 70,000 cars, the Customer needs to create 70,000 individual requests that will return 70,000 individual reports, which requires extra efforts to get a complete picture.
Our big data consultants had a three-day onsite visit to the Customer, where they carefully examined the existing solution: its architecture, relevant documentation, available data sources, as well as currently applied data management practices.
As a next step, we held a workshop dedicated to the solution-to-be, where we discussed the expected deadline for the solution launch, the existing and preferred technologies and available licenses. During the workshop, we also laid down architecturally significant requirements concerning the solution’s availability, performance, security, and scalability. Then, we prioritized each requirement describing their impact on the Customer’s business as critical, high, medium or low.
Based on the workshop’s findings, our consultants designed the architectural concept of the solution-to-be. We defined high-level architecture components, for example, integration services and a data lake, and described their functions.
After presenting the solution concept, our consultants organized a Q&A session where we provided comprehensive answers on any aspect that the Customer was interested in. For instance, we explained the advantages and disadvantages of a data lake compared to a data hub. We also compared on-premises implementation against cloud options, including different cloud scenarios – with Amazon Web Services and Microsoft Azure at the core.
Besides, our Apache Cassandra consultants provided the Customer with the recommendations on how to improve Apache Cassandra’s performance, which embraced such aspects as the structure of tables, partition keys, as well as the format of data to be stored.
The Customer received a visit report that contained the following:
- Architecturally significant requirements for the solution-to-be and their impact on the business.
- High-level design of key architecture components with their functions described.
The Customer received comprehensive answers to their questions concerning the future solution’s implementation, the pros and cons of different technologies and architecture components, the nuances of data storage and integration and other important issues.
Business requirements analysis, functional decomposition, workshop on architecturally significant requirements, Q&A session, comparative analysis of technologies.