Retail Focused Data Science, Data Engineering, and Data Architecture

Algonomy delivers its algorithmic solutions based on real-time data collected about your customers, products, and inventory. Our multi-cloud approach, comprising both private and public cloud infrastructure, scales and manages 5 petabytes of data–processing 10s of billions of real-time discrete data events across 1000 plus data sources–and has the additional capability to compute billions of algorithmic decisions daily. Our cloud-computing model is distributed across 20 geographically diverse data centers and is equipped with the latest in high performance and accelerated computing–highly scalable and resilient object stores for big data storage, super fast NVme connected SSD storage–for high throughput lowest latency workloads. Additionally, it is bridged and connected by the most advanced networking backbone to deliver complex business outcomes in milliseconds across the globe.

The Algonomy infrastructure is highly secure and fully redundant. It offers the most intelligent geo-location-based traffic management available, ensuring the low latency and fast response times required by business enquiries. We continually test response times across cities to verify response rate and availability. Data center locations are chosen not just for their geographic diversity but also for their network infrastructure, data security, and the data sovereignty directives under which they operate. Our smart failover technology provides geo-proximity based traffic switch over. This highly redundant and distributed architecture safeguards against downtime and data loss. Our solution is hot patchable and, depending on user demand, can scale vertically and horizontally to completely eliminate downtime.

Our Lakehouse implementation harnesses the potential of Data Warehouse and Data Lake for quick and easy retrieval and scalability. The process is robust and seamless – the most recent and frequently used data is kept in the data warehouse and high-volume data in the data lake, with an automatic switch between the two as per data requirements. The in-house implementation is cost effective and easier to implement.