Mastech InfoTrellis works closely with enterprise clients in understanding their critical business needs and current IT environment to determine the best way to leverage the Cloud. A Cloud Advisory team analyses how particular business applications are currently hosted in an on-premise or current Cloud infrastructure to build a roadmap for the Cloud migration journey. Independently, a Cloud Advisory team can help clients build out a DevOps and CloudOps support framework for agile, Cloud-native application development. These Advisors help assess and determine architecture and framework for the adoption of public, private, or hybrid Cloud across functional IaaS and PaaS needs, taking into account the appropriate target Cloud platforms from AWS, IBM, Azure, Google, or Oracle.
Mastech InfoTrellis Cloud Advisory Services are aligned with other key services in Data Management, Data Engineering, Data Science, and Customer Experience to provide the Cloud Advisory Services below:
These move to Cloud services help assess the current Cloud strategy of an enterprise and enable Cloud integration and networking, including DevOps and CloudOps support. The service focuses on data migration, application fit, and security posture. A clear Cloud adoption roadmap, timeline, and scope to migrate applications to Cloud are established for the client.
IBM Cloud Pak for Data
Mastech InfoTrellis is a Global Elite Partner of IBM and the right partner for enterprises in implementing IBM Cloud Pak for Data. An assessment takes care of Cloud readiness, business value, architecture, and GDPR/CCPA readiness, if necessary. The assessment services cover Cloud readiness assessment, benefits, value identification, IT gaps analysis, and a definite roadmap for IBM Cloud Pak for Data adoption.
Red Hat Open Shift (RHOS) Adoption
Mastech Infotrellis has deep expertise in RHOS, and this Service covers candidate application assessment along with the assessment for containers applied to Cloud-native application development, or application modernization. The Service develops the enterprise RHOS architecture and an adoption roadmap that includes the implementation of RHOS base Container infrastructure for clients to get started on this journey.
DevOps and SRE
As the pace of business increases, the need to become more agile and the move to Cloud accelerates. The adoption and use of sound dev/ops practices become critical, including developer self-service, deployment automation, and continuous delivery. Mastech Infotrellis’ highly skilled DevOps team offers clients global shared services on demand that can help shorten product development cycles, improve coding reliability, improve a client’s go-to-market pace, and stay ahead of the competition. Whether it is leveraging containers, Kubernetes, and microservices, or simply wanting to reduce project costs, Mastech Infotrellis can help clients run faster and improve operational efficiency.
As with any DevOps practice, the ability to quickly design, deploy and manage large, near-real-time data sets is key in a Data Driven Enterprise. By applying agile data engineering and data governance tools and practices to the enterprise ecosystem. The time to decision is reduced and the decision quality is improved MIT deploys DataOps practices and “best fit” tools and technologies, including the application of advanced techniques like statistical process control (SPC) on data quality pipelines to deliver the desired outcomes of the client, in a manner that is most efficient and cost-effective.
Customer Experience on the Cloud
Most Customer Experience (CX) solution deployments are hybrid Cloud deployments with the CX applications composed of one or more SaaS and on-premise enterprise applications. These applications are integrated with data sources and enterprise applications that are on-premise or on public/private clouds, with data pipelines bridging these boundaries. These solutions are crafted carefully by Mastech InfoTrellis so as not to compromise performance or introduce avoidable latencies, increasing solution efficiency, while minimizing cost.