https://www.techtarget.com/searchdatamanagement/feature/Choosing-a-modern-data-warehouse-to-fit-your-data-needs
Data warehouses are special-purpose platforms designed to ingest, store and facilitate the processing of large amounts of information. When I began designing data warehouses 30 years ago, the initial driver was to improve operational system performance by offloading reporting workloads to a separate platform.
As the reporting systems matured, users realized that they were able to use the new environments to transform raw data into actionable insights that business personnel could use to make better decisions. From front-line units to the executive team, the opportunities for all levels of business personnel to use information to improve organizational efficiency were -- and are now -- endless.
Now, big data platform usage ranges from shop floor robots that access modern data warehouses to improve their robotic actions to business personnel using increasingly intelligent analysis tools during their daily activities.
Modern data warehouses improve business intelligence by enhancing data quality and consistency, allowing users to better understand the meaning of the data, promoting a data-driven culture and facilitating historical intelligence and forecasting capabilities.
Before you begin evaluating the different platforms, it's important to learn about the different types of big data implementations. A modern data warehouse is only one of many platform options that may fit your organization's needs.
Vendors of all sizes are capitalizing on the interest in business intelligence by offering a wealth of big data products to the IT community. A highly competitive market arena compels all modern data warehouse platform vendors to accelerate the release of new products and enhancements to existing offerings.
As a result, competitors frequently release features that expand their products' administration, data integration, metadata management, analytics and information governance capabilities. The latest trend is AI and machine learning augmented tools that assist human workers to collect, prepare and analyze big data and share business insights.
To correctly select and implement the most appropriate big data platform for their organizations, IT shops must create and execute a well-thought-out, detailed analysis of competing offerings. Because of the wide variety of cloud and on-premises architecture and data infrastructure combinations available, evaluation teams need to expand the scope of their analysis to include the modern data warehouse ecosystem.
Not only does the evaluation team need to understand the modern data warehouse product, team members must also learn the intricacies of the offering's underlying architecture. Choosing the correct large data store ecosystem -- which includes the type, platform, server, storage architecture, on-site or cloud infrastructure, data store and ancillary tool sets -- is critical to the success of any application that stores and processes large data volumes.
10 Jun 2020