Problems We Faced

The problem we faced was related to the processing of large datasets, which is a critical component of the GoodData platform. Our client had a massive amount of data to process, including customer demographics, purchase history, website interactions, and other key metrics. However, the existing data processing infrastructure was not designed to handle such a large amount of data, resulting in significant delays in data processing and analysis.

The primary issue was related to the architecture of the data processing pipeline. The existing system used a monolithic architecture, where all the components of the pipeline were tightly integrated. This made it difficult to scale the system and handle large datasets efficiently. As a result, the data processing pipeline was taking an unacceptably long time to process the data, causing delays in the delivery of analytics insights to clients.

Another issue was the use of an inefficient data storage system, leading to delays in data retrieval and processing. GoodData was using a storage system that couldn't handle large volumes of data efficiently, the system was taking longer to retrieve data and process it, leading to significant delays in delivering insights to clients.

Moreover, a lack of proper monitoring and maintenance led to delays in data processing. If the system is not monitored regularly, issues were going unnoticed, leading to delays in processing time. Similarly, the system was not maintained regularly, becoming outdated and failing to handle the increasing volume of data.