Gone are the days of battling “big data”. We welcome it with open arms. The cloud allows us to store near-infinite amounts of data like text, video and scanned documents. Newcomp’s data engineering team builds the fabric to manage every type and size of modern data in an elastic, affordable way.
Build ingestion mechanisms to stream or copy data from any data source in real-time or on a schedule.
Orchestrate data movement (ELT/ETL) by coordinating data dependencies, relationships and transformations for delivery into data storage.
Organize raw data into meaningful zones for retention and governance before it is transformed for business analytics.
Optimize data for fast analytical workloads and data science by using dynamic computing and storage design.
Design rules to ensure quality and security standards while cataloguing data to make it easy to find, trust and govern.
Access data where it resides through a virtual layer in real-time, without replicating or moving the data.
We unite and align data from enterprise applications, external parties, flat files and legacy systems to discover never-before-seen relationships, patterns and insights.
Eliminate time wasted arguing over timing, source and versions of data used for decision-making by building a transparent hub for information.
Upgrades, migrations, performance risks – reporting and analysis directly on source data is full of risk, so we bring that data into an engine built for performance and stability.
Modern data needs a strong architecture to support massive amounts of data and computing power. Newcomp architects know how to start small, and build for the future – from basic data acquisition to the most advanced neural networks.
Data lives everywhere now, and our engineers are trained to capture it. They build pipelines to ingest third party, web, unstructured and machine data, then store, refine, organize and deliver it to consumers.
These teams feel the pain of limited insight or access to data. As our champions, they define the target, and help us validate results along the way.
These groups have many names – Centres of Excellence, data provisioning, data warehousing, etc. Our goal is not to work around these existing pipelines, but to help modernize and remove bottlenecks that are preventing data from flowing smoothly.
Deploy machine learning, AI and statistical models and applications
Develop data environments for reporting and analysis
Design & develop models, templates & processes for financial & operational planning
Design and build elastic infrastructure to accelerate analytical applications