active data warehouse

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Published By: StreamSets     Published Date: Sep 24, 2018
Treat data movement as a continuous, ever-changing operation and actively manage its performance. Before big data and fast data, the challenge of data movement was simple: move fields from fairly static databases to an appropriate home in a data warehouse, or move data between databases and apps in a standardized fashion. The process resembled a factory assembly line.
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practices, modern, data, performance
    
StreamSets
Published By: AWS     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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AWS
Published By: AWS     Published Date: Jun 20, 2018
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated q
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AWS
Published By: IBM     Published Date: Oct 01, 2013
In-depth Comparison of IBM Smart Analytics System 7700,Teradata Active Enterprise Data Warehouse and Oracle Exadata Database Machine.
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ibm, ibm smart analytics system, ibm smart analytics system 7700, it solutions, data warehouse, database, data management system, data mangementent, enterprise warehouse solutions, warehouse solutions, data management, business technology
    
IBM
Published By: Aventail     Published Date: Oct 26, 2007
The goal of pervasive BI applications is to take the data that produced the back office ROI of more than 400% and deliver it to front-line employees in a form appropriate to their job functions – with similar results. There are thousands of business process steps in a typical enterprise where pervasive BI insights can be added. What are leading companies doing with pervasive BI?
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business intelligence, database, enterprise data warehouse, data mart, data warehouse, data mining, active data warehousing, active data warehouse, informatica
    
Aventail
Published By: Informatica     Published Date: Oct 26, 2007
The goal of pervasive BI applications is to take the data that produced the back office ROI of more than 400% and deliver it to front-line employees in a form appropriate to their job functions – with similar results. There are thousands of business process steps in a typical enterprise where pervasive BI insights can be added. What are leading companies doing with pervasive BI?
Tags : 
business intelligence, database, enterprise data warehouse, data mart, data warehouse, data mining, active data warehousing, active data warehouse, informatica, data management
    
Informatica
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