A comprehensive guide to your modern data analytics solution.
Different users in your organization have different needs for data warehouses, whether they're part of an IT, data engineering, business analytics, or data science team.
A modern data architecture meets these diverse needs by providing a way to manage all data types, workloads, and analytics. It consists of architectural patterns that integrate the necessary components to work together according to industry best practices.
The modern data warehouse contains these components. It has the capacity to streamline data processes more efficiently than other warehouses, so that everyone from data scientists to IT teams can do their jobs faster and with more accuracy, and can focus on the innovative work that helps the organization grow without facing any roadblocks or complications.
Benefit from industry-standard patterns and components which are integrated to work together.
Simplifying the organization of any data types and offers various approaches for manipulating it.
More flexibility for easy utilization of data without having to move it.
Allows for fast and easy deployment, scaling, and management.
Including SQL, machine learning, graphs and charts, as well as spatial processing.
Steps you should take when designing your modern data warehouse.
Define your specific business requirements, anticipate end user's needs, agree on a scope, and come up with a conceptual design.
Create both a logical design (relationships) and a physical (storage and retrieval) design for your data warehouse.
Remember to allow room for expansion and evolution to meet the evolving needs of your end users.
Consider the advantages of cloud computing so you can focus on extracting value from your data.
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