Sep 08, 2023
No image
Completed
VUniverse: body leasing for an innovative streaming service
$25,000+
7-12 months
United States, New York
1
Service categories
Service Lines
Cloud Consulting
Software Development
IT Services
Domain focus
Media & Entertainment
Other
Programming language
Ruby
Frameworks
Ruby on Rails
Subcategories
Cloud Consulting
SaaS
Challenge
The client came to us when most of the product was already developed.
Our task was to write part of the code for the back-end, which was responsible for the key functionality of the service - a system of recommendations for similar titles.
We used the Neo4j Graph Platform for this purpose, which allows for creating queries in natural language and is much faster than SQL.
VUniverse aggregates content from available streaming services (e.g. Netflix or HBO) so the main challenge was humongous amounts of data to be processed.
Each of the services contains thousands of titles, and each of them contains dozens of parameters: from the genre, through the cast, to the cover page and user rating.
The client came to us when most of the product was already developed.
Our task was to write part of the code for the back-end, which was responsible for the key functionality of the service - a system of recommendations for similar titles.
We used the Neo4j Graph Platform for this purpose, which allows for creating queries in natural language and is much faster than SQL.
VUniverse aggregates content from available streaming services (e.g. Netflix or HBO) so the main challenge was humongous amounts of data to be processed.
Each of the services contains thousands of titles, and each of them contains dozens of parameters: from the genre, through the cast, to the cover page and user rating.
Solution
A dedicated team was assigned to the project, which first of all took care finding the right technology for the task - easy creation of "connections" between films and efficient search.
The Neo4j Graph Platform, which, unlike SQL, doesn't arrange data in rows, but has a flexible structure defined by stored relationships between data records, was ideal for this.
With Neo4j, each data record, or node, stores direct pointers to all the nodes it's connected to. This allows for deeper and faster queries.
To better visualize how much data the graphs had to handle: there are 500,000 films in the service database. Each of them has a title, genre, cast, user rating... efficient technology was crucial here.
The system itself uses AWS because it scales resource consumption easily.
A dedicated team was assigned to the project, which first of all took care finding the right technology for the task - easy creation of "connections" between films and efficient search.
The Neo4j Graph Platform, which, unlike SQL, doesn't arrange data in rows, but has a flexible structure defined by stored relationships between data records, was ideal for this.
With Neo4j, each data record, or node, stores direct pointers to all the nodes it's connected to. This allows for deeper and faster queries.
To better visualize how much data the graphs had to handle: there are 500,000 films in the service database. Each of them has a title, genre, cast, user rating... efficient technology was crucial here.
The system itself uses AWS because it scales resource consumption easily.
Results
Thanks to the talent of our team, it was possible to complete the product and get a nomination in SXSW Innovation Awards.
With the right technology, we achieved significant customer benefits: better responsiveness thanks to data imports being reduced by half.
We have corrected countless bugs. Customer experience increased significantly (thanks to our recommendation system). We have also developed a test architecture to make it easier to maintain the project.
Thanks to our work, the client reached MVP very quickly and can look for investors.
The service and application are available for users in the beta phase.
Thanks to the talent of our team, it was possible to complete the product and get a nomination in SXSW Innovation Awards.
With the right technology, we achieved significant customer benefits: better responsiveness thanks to data imports being reduced by half.
We have corrected countless bugs. Customer experience increased significantly (thanks to our recommendation system). We have also developed a test architecture to make it easier to maintain the project.
Thanks to our work, the client reached MVP very quickly and can look for investors.
The service and application are available for users in the beta phase.