A leading telecom client engaged ProCogia to perform an end-to-end review of their data processing solutions that were running on-premise. ProCogia advised the client more compute power was required and led the implementation of EMR spot instances.
Results included high scaling and high availability with more efficient processing while a reduction in infrastructure and maintenance costs.
A leading telecom client’s data processing jobs were running on an on-premise cluster and required compute power that stretched the systems and caused jobs to fail. The challenge was to run those jobs as efficiently as possible without adding extra nodes (hardware) or maintenance overheads.
Collaborated with stakeholders to understand the current processing jobs & predict future demand based on team fluctuations
Implemented AWS EMR, a cluster platform running Spark and Hadoop which enabled teams to analyze large amounts of data with auto-scaling capabilities
Configured the auto-scaling feature of EMR to run the client’s Spark jobs freeing up client’s FTEs. This saved resources by terminating the EMR clusters as soon as the job completed.
Additional cost-savings were realized through utilizing EMR spot instances instead of the on-demand reserved or dedicated instances.
ProCogia provided the client an environment that had the features they required to process data with high scalability, high availability, and optimized performance.
The leading telecom client is now able to process a high throughput of jobs in less time while saving on maintenance and hardware costs.
We provide data consultancy to organizations to optimize your investment in people, processes, and technology. This is typically through data strategy engagements, roadmaps, transformations, and independent technology advice.
Cloud Data Engineering
We partner with all major cloud providers, allowing us to adopt a data-agnostic approach focused on delivering tailored game-changing solutions.
Data Operations (DataOps)
We build robust, scalable, and enterprising data environments that enhance the collaboration between Data Science teams.