Data Engineering
- Home
- Services
- Data Engineering
Data engineering teams enable data-driven decision making and machine learning capabilities. They build systems that help you collect, transform, and publish data.
Data Engineering
Better Data Engineering principles, better outcomes.Failure to recognize the advances of data management will leave you with legacy tools, high costs, poorly developed staff, and ultimately slower business growth (or worse, a declining market position). How you build your data systems matters. Strong data engineering principles enables you to reliably author, deploy, and support your mission-critical data products.
Companies of all sizes have huge amounts of disparate data to comb through to answer critical business questions. Data engineering is designed to support the process, making it possible for consumers of data, such as analysts, data scientists and executives, to reliably, quickly and securely inspect all of the data available.
- Constant feedback loops during development ensure constant progress and alignment with your business needs.
- Data problems can be solved using people or automation. Automation wins through repeatability and preventing inevitable human error.
- Monitoring, logging, operational visibility is critical to business confidence as you get proactive with problem-solving.
- Data technologies are both broad and deep: theory doesn’t translate to the real world, expertise and experience do.
Ingest data from internal and external sources, in real-time or batch. Catalog and secure data so that it’s governed and compliant.Monitor and measure data quality and performance.
Cleanse your data and increase its value and usability. Enrich datasets through joining, mapping, and feature creation.Model for various data consumers.Manage workflows for scheduling, audit, performance, and data quality.
Analyze via tooling for analytics, machine learning, and AI.Serve data to applications, processes, or people.Govern the catalog of data, dictionaries, and mappings