Big data engineering is about identifying and assembling the right big data technologies to design the perfect architecture – including open-source software – to achieve business objectives and generate concrete value from existing data assets while having the necessary governance to create a foundation that will scale and increase value over time and integrate with existing assets. The focus is on making different tools and assets work together so companies can evolve from initial success through to effective data management into analytic insights.The skilled and experienced architects and engineers of NodeLogix design and build big data solutions that produce faster time-to-value, with clear architectural blue prints for the long term. Our approach to big data engineering has been honed over scores of successful projects. NodeLogix also builds on a foundation of reuse for frameworks and components that enable companies to unlock the value of their data much sooner than other methods.
NodeLogix builds Big Data applications and solutions based on Hadoop, Spark, Kafka, NoSQL and other leading platforms. We can assist with:
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- IT and operational cost reductions
- Device behavioral data for improved customer service and proactive maintenance
- Detailed parametric and test data to improve manufacturing yield
- Improved insight into consumer behavioral data
- Adoption of analytics as a competitive weapon
In addition, our extensive industry expertise means we have specific use cases and assets for manufacturing, insurance, banking, payments, capital markets, healthcare, media and advertising, transportation, retail, telecommunications and more.
What’s different about how NodeLogix does Big Data engineering?
- Proven design patterns – based on years of experience in building large-scale systems, NodeLogix brings tradecraft of the best patterns and approaches to modeling data and integrating
- Objectivity and independence – we help you discover the best choices for every project based on deep real world experience in using technologies in production
- Comprehensive and inclusive –partiality towards integrating existing data sets and tools
- Tested and repeatable – we use pre-built components and quality templates for integration and data access
- Agile approach– we incrementally build architectures to achieve business goals quickly and expand from initial success to keep driving results