Data mining is leading to a major change in the way organizations store, manage and analyze their data. This comprehensive guide explores the basic components of a modern data-driven architecture, explaining how it can support your business with workable applications and enable data-driven decision-making.

A data warehouse is a centralized repository that holds large amounts of structured, semi-structured, and unstructured data. This raw data is stored in its native format, regardless of data type or structure, and can be transferred from multiple sources, including applications, click data, social media and IoT devices. All data in all forms can be collected at one point.

Centralizing much of your data in one place, even if not all of your data, offers unique possibilities for data analytics, but creating a goal is only the first step in creating a truly modern data intelligence architecture. You have to make a number of critical steps along the way to make data management more cluttered than traditional systems and a more expensive data swamp. But when properly designed, a modern data-driven architecture eliminates these problems and continues It provides a cost-effective, high-performance, feature-rich data analytics system for the future.

What will you learn when you read this document?

• Can a data client help my organization break out of costly data bogs?
• What are the benefits of separating storage and information processing in data-driven architecture?
• How can a modern data tool speed up data analytics and reduce ETL requirements?
• What are the basic components of a high-performance, searchable query engine for data purposes?