SQL SERVER
HADOOP
NETEZZA
REDSHIFT
Build a flexible, scalable and reliable single source of truth in your organization that delivers standardized and intuitively modeled data for analysis purposes.
Leverage major cloud service providers like Amazon and Microsoft to provide flexibility in storing and processing large amounts of data.
Architecture to optimize data storage, access, organization, movement, quality, performance, and scalability
Data modeling to structure data for flexibility and fast access
Windows Azure Cloud platform utilizing Cloud Services, Virtual Machines, Virtual Networks, Caching,
Data integration to create consistent, relevant, and trustworthy data viewed across business units and subject areas through source-to-target mapping and ETL (extract, transform, load) development/management/replication management/tuning
Reporting, dashboards, and self-service to provide actionable information to support business objectives
Maintenance/support to enable and enhance data warehouse operations
Upgrades/migrations to advance or retool technical innovations while limiting operational risk
Analytics through statistical modeling, response forecasting, and other sophisticated techniques applied against structured and unstructured data
Coherent’s people are bright, engaged and ambitious… eager to solve problems. We knew we could rely on them to work through anything.
Expertise from the Coherent Data team
by Lenny Kotlyar
by Valery Shklenski
by Yury Zhukovsky
Unlock your data by making it accessible, available, and standardized for analysis
MS Data Solutions
We create scalable solutions that deliver accurate data that our customers need with an affordable price and a familiar tools set
Coherent Data Starter Kit
Proven, phased management and technical framework that helps companies implement Data systems with highly predictable outcomes
SSIS Data Integration Service
Solving complex data migration & consolidation challenges with the SQL integration platform
Data quality is KING in business intelligence
When talking about quality assurance in Data, creating test plans and cases comes immediately to mind – particularly for vulnerable bottlenecks like the ETL process.
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