Context

In a context of increased media competition, our client - the digital media department of a leading company in the Canadian media market - wanted to take advantage of their large amount of data to build a content recommendation engine for their users. To achieve this, a data architecture was needed but the amount of data needed for the project was so great that Big Data tools had to be implemented. As a complete beginner in Big Data and Cloud technologies, the client asked us to help them define the best Big Data architecture for this project. The complexity of the mandate laid in the multiplicity and diversity of data sources to be integrated for the tool’s development.

Our achievements

Our mission was to suggest a triple architecture - solutions, technical, and business architecture - from an agnostic review of the different tools available on the market. We then conducted a needs analysis with the teams involved in the project - business, IT, BI. Then, our Big Data experts conducted a review of all the major cloud tools (Amazon EMR, Microsoft Azure, Google Cloud Platform), the various Big Data solutions (Cloudera, Hortonworks, HDInsight) as well as Big Data technologies “on demand” (Spark, Impala, Kafka, Storm, NiFi). The goal behind this complete review was to put together several architectural recommendations for our client to make the final architecture choice. We also had to ensure that the technological architecture would fit properly into the structure of the business model, so that the information could be used by the right people.

Impact

This first mission was crucial to start this great Big Data project. It represents the foundations of the project. The implementation of the chosen technologies would start in a second time, and the tool’s development in a third step. In addition to implementing Cloud best practices in the company, this project has largely been used to develop the data-driven and Big Data culture in this large company. The digital media department's BI team becomes the ambassador for future Big Data projects in the company.

Key figures
  • 4 integration architectures suggested
  • 1 terabyte of data available
  • 15 data sources to integrate
  • 11 Cloud and Big Data tools evaluated
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