NMX’s diverse team integrates analytics and data science with model development and operations experience to address both Big and complex data across long development and production lifecycles. Among our most valuable skills is dealing with “messy” data, which in our experience is the rule, as opposed to the exception. We also have experience with often-overlooked issues such as security, quality, and conformance to acceptable-use and regulatory policies. These topics, as well as Big Data model architecture, analytic pipelines, fault immunity strategies, consistency and performance all require careful consideration during the data assessment phase of development. Modern technologies, such as Hadoop, NoSQL, containerization and orchestration solve problems only if the underlying data and deployment models are properly designed.

NMX’s engineers understand the requirements of analytic processes and share their insights with the engineers building logical and physical data models. This type of specialized expertise is needed during the early phases of a project and again at various inflection points during development and long-term operations. The NMX approach of providing engineering resources on demand is ideal for projects involving large quantities of complex data. We mitigate risks created by large-scale data collection and help our clients to better understand the data they are collecting and what data they should be collecting.

Experience

NMX has years of experience collecting, cleaning, compiling, distilling and processing large data sets for the purpose of helping clients dramatically improve decision-making. It can be painstaking work, but we know how to do it. We’ve done it extensively in the semiconductor, embedded software, and electronics systems industries. Examples of clients in these sectors include Intel, AMD, Qualcomm and NXP. During the last several years we’ve applied our knowledge and experience to an expanding set of industries and sectors where we have implemented and deployed solutions spanning the Apache Big Data technology stack.