Our Approach
Proven, In Depth Technical Expertise
We have architected data lakes and databases for major enterprises and for startups facing the world's toughest requirements. We know the relevant products and technologies, whether new or established, in depth.
Engineering Approach, Practical Experience
We'll start from your business interests. We will quantify your requirements as needed, then use our fact & measurement based methods to develop your data strategy, architecture & solutions.
We Deliver Business-Focused Results
Our customers get real, practical solutions to the toughest data management problems, employing our engineering skill to deliver what matters most to their business interests.
Analytic Data Management Services
Data Vision and Strategy
From your interests and goals, and those of your stakeholders, we'll help you create a vision and a strategy for your analytic data ecosystem.
Data Platform Architecture
Whether you need a data lake, a data warehouse, a NoSQL database, or a data streaming pipeline we will architect it to fit your environment and strategy. If you have requirements defined, we'll work to them; if not, we'll help you create or quantify them.
Data Platform Assessment/Selection
Wondering if your data platform is up to you next wave of demands? Whether that new workload or data source should go on Hadoop or the data warehouse? On premises or in the cloud? In which cloud? We'll assess, measure if needed, and recommend based on on our unmatched expertise with scalability, performance and data availability. You will learn whether changes are needed and, if so, what to do.
The Role of Cloud
How should cloud fit into your analytic ecosystem? And, how best to manage your data in each cloud you use? We’ll use our engineering approach to help you answer these questions and many more. If you are putting substantial amounts of analytic data in the cloud you need a data management architecture that deals effectively with your use cases and that provides data security, metadata management, performance, scalability and effectively managed cost. You don’t automatically save money when you put a data warehouse workload in the cloud. We’ll analyze your options and create a solution that achieves your goals with respect to performance, cost, security and other factors.
Solution Design
Once the platform for that challenging database or workload has been selected, you still need a design. We will identify the critical engineering issues such areas as scale, performance and data availability -- then work out the database design soluition. Whether you are using open source or commercial data managers, whether SQL or NoSQL, whether cloud or on prem, whether Hadoop or commercial platforms -- we have the expertise to create a design that will work. We'll collaborate with your existing team or work independently, as you prefer. We have helped many leading companies avoid the pitfalls of big data, fast data and complex data projects.
Review and Advice
Need a design review? A pre-production review? A this-isn't performing the way we expected review? Or something higher level, such as review of your roadmap?
Our review of key data architectural and data engineering issues will be penetrating but constructive. We'll identify key challenges and pitfalls and help your team navigate to success.
Or, if you just want some advice on a key question, we'll provide a phone consult or a brief onsite consult according to your needs.
WinterCorp Team
Richard Winter, CEO and Principal Architect
Data management industry expert Richard Winter has unmatched data platform/solution knowhow and experience. He has defined data requirements, developed data strategies and architectures, selected data platforms and engineered data
solutions for leading enterprises.
Rick Burns, Senior Data Management Technologist
Competitive engineering specialist Rick Burns has in depth knowledge of data platform scalability, performance and availability. He has developed and audited custom benchmarks, advised leading enterprises on data platform selection and analyzed data platforms.
Norbert Kremer, Data Scientist and Google Cloud Expert
Hands-on expert, Norbert Kremer, applies expertise in scalable database and cloud platforms to create big data and data warehouse solutions in healthcare, life sciences, financial, retail and digital media. Dr. Kremer's skill and implementation experience have resulted in major successes for clients in these and other fields.
Tennyson Yesupatham, Data Engineering Leader
Engineering leader and hands on expert, Tennyson Yesupatham has designed and implemented data solutions on Linux, Hadoop and cloud platforms. Mr. Yesupatham has in depth experience with data warehouse and data lake architectures. He leads a team of WinterCorp data engineers, capable of implementing a wide range of data solutions.
WinterCorp Data Engineers and Other Technical Specialists
Our team includes an array of technical specialists in data management covering design and solution engineering for all major data platforms: SQL, NoSQL, Hadoop and Cloud. For our projects, we put together teams of experienced professionals appropriate to the technical challenge and environment, operating under the direction of one of our leadership team.
Development, Reuse and Deployment of Analytics
Teradata AppCenter
Though we now know that data science, machine learning and other advanced analytics can reveal remarkable insights in an enterprise. But many executives find that there is a long road from insight to impact. Teradata AppCenter is a new product that seeks to make it easier for companies to develop analytics, actually deploy them in the business and reuse them across the organization. This report describes the problem; the solution that Teradata has brought to market; and, some of the ways it has been used.
Don't miss this report, with its insights about what it really takes to see payoff from analytics in your business.
Big Data - The Real Cost
The Total Cost of Data
In self-funded research project in 2013, WInterCorp developed a cost framework for the analytic use of data, incorporating all the major components of cost, including data preparation, application development, development of queries and analytics, administration of data and systems and systems cost. This report describes the framework and applies it to two example platforms: the relational data warehouse and the Hadoop ecosystem, as they existed in 2013. While some aspects of the examples would be different today, the framework is as valid today as it was then, and it is available to use in the form of a published spreadsheet.
Don't miss this report, with its insights about what it really costs to create and use an analytic data repository. The bottom line: system cost is a much smaller part of the overall picture than you might imagine. Productvity and agility are far more important than any other aspect of cost.