Organizations today are faced with an increasing variety of data – structured, unstructured, streaming, social, time-series, machine-generated, etc. Specialized data stores have emerged that match the characteristics of the underlying data (graph, document, key-value, wide-column), yet this presents new challenges: more data silos, specialized IT skills and infrastructure complexity. A leading systems integrator (SI) needed to deploy cloud-based infrastructure that supported graph and document databases, scalable compute and storage, and was compatible with R.
Analytics Engines deployed XDP, its enterprise data backbone that provides data integration, infrastructure automation, and analytics workflow execution. A bespoke analytics architecture—including Neo4j, Hadoop, MongoDB, Spark, and R-Studio—was configured using software defined infrastructure, and deployed to the AWS cloud in less than 1 week.
XDP enabled the SI to rapidly deploy a new scalable analytics infrastructure that matched data assets to data stores through a repeatable, reproducible and auditable process. XDP’s data virtualization layer provided a unified view across the data silos and natively integrated with the SI’s existing analytics tools. By eliminating the complexity of configuring and administrating infrastructure, the SI is able to focus more time on delivering higher-value solutions to their clients.