Recommendations Engine

Recommendations Engine

Besides providing the latest news and information, this platform may also be used in a myriad of other online businesses such as e-commerce and recommending products, services, and content that cater to its readers. To carry out in-depth analysis of system logs, activities, and customer behaviours through its websites, and to better cater to consumers and market demands, they decided to utilize big data technology via VAYUZ with the servers powered by the AWS®.

This solution allowed the company to take advantage of a faster, decentralized architecture that not only improved system stability, but also increased time to market response demands and the company’s overall competitiveness.


Build a more stable data warehouse. Enhance stability of current data warehouse to ensure efficiency in carrying out real-time analysis and recommendations for customers based on the system logs from websites, including shopping.

Eliminate performance bottlenecks. Address performance bottlenecks during Apache.

Hadoop* software data importation and SPSS*.

Modelling to effectively carry out more in-depth, cross-industry, and cross-device customer behaviour analysis.


  • Deploy big data analytics solution on this architecture. Build a massive parallel processing/computing (MPP) big data analytics framework using AWS servers to resolve performance bottlenecks caused by huge data volume from the traditional data warehouse, as well as boost system stability and I/O performance.
  • Selling more products according to the search of a user.
  • More recommended products to increase purchase amount.
  • Showing more discounts and offers according to the search of a user.