Data Engineering Solution

September 15, 2022


The Customer is a Leading Australian based Financial Services group.


As a part of the project, Mane Consulting’s analytics team was to design and implement data management and analytics platform to let the Customer collect the data from multiple sources and get insights into customer behaviour. The Customer wanted the platform to analyse historical data and enable forecasting. Access rights were another issue to solve, as the Customer planned to provide their tenants with the access to the tenant-related analytics.

Mane Solution

The data analytics platform was gathering raw data from 10+ sources. To collect this data and move it into Apache Kafka, Mane Consulting’s big data team suggested the MQTT protocol.

The team also suggested using Amazon Spot Instances to reduce the costs of AWS computing resources. To ensure the analytical system’s scalability, they used AWS Application Load Balancers.

Apache Kafka acted as a data streaming platform. There, the raw data was organized for further offload into the landing zone that was running on Amazon Simple Storage Service. For data storage and warehousing, Amazon Redshift was chosen.

To enable regular and ad-hoc reporting, Mane Consulting developed ROLAP cubes with 30+ dimensions and 10+ facts. For instance, the analytical system measured advertising impressions and click-throughs of a particular user.


With Mane Consulting’s big data services, the Customer was able to:

•          Measure the engagement and identify the preferences of a particular user.

•          Spot trends in the users’ behaviour.

•          Make predictions about how users would behave.

•          Benefit from insightful data analytics (for example, daily earnings, number of new users, customer service data and more).

The use of Amazon Spot Instances allowed the Customer to reduce the costs of AWS computing resources by 80%.