Examples of using A data warehouse in English and their translations into Slovenian
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Programming
Design and implement a data warehouse.
Building a data warehouse for Laser* BI.
High performance and flexibility of a data warehouse.
Set up a data warehouse in our data centre or at your premises.
Gora participates in all stages of building a data warehouse:.
Designing and building a data warehouse is a challenging job.
Some differences between a database and a data warehouse:.
A data warehouse is used to store huge(HUGE) quantities of data. .
Select an appropriate hardware platform for a data warehouse.
Establishing a data warehouse and a business analytics system- SKRINJA 2.0.
High performance and flexibility of a data warehouse. More about solution….
In a data warehouse the data from transaction systems and other external sources is collected.
The first example relates to data that originates from a data warehouse.
To this end, we can develop a data warehouse based on your operational database.
How Business Intelligence solutions consume data in a data warehouse.
A data warehouse is a system that helps to analyze data, create reports and visualize them.
In this course, you will learn how to implement a data warehouse platform to support a BI solution.
A data warehouse is a system that stores data from a company's operational databases as well as external sources.
The solution is based on Oracle BI tools and is comprised of a data warehouse and business data warehouses. .
A data warehouse is a collection of corporate information and data derived from operational systems and external data sources.
In this course,students will learn how to implement a data warehouse platform to support a business intelligence(BI) solution.
A data warehouse will be set up for all programme actions(centralized and decentralized) in so far as different IT management tools will continue to be used.
Overview This 5-day course describes how to implement a data warehouse platform to support a business intelligence(BI) solution.
A data warehouse encompasses data from a longer time period and data aggregates that are not located in the transactional database, where business data is typically stored.
The main difference between data integration and ETL is that the data integration is the process of combining data in different sources to provide a unified view to the users while ETL is the process of extracting,transforming and loading data in a data warehouse environment.
It supports shared services with a data warehouse, file sharing and other tools, shortening the time and cutting the cost of finding new locations.
I should mention the creation of a data warehouse and standardised reporting to decision-making bodies, which allowed us to ensure a more up-to-date and centralised access to risk-related information.
Database professionals responsible for implementing a data warehouse, developing SSIS packages for data extraction, loading, transferring, transforming, and enforcing data integrity using MDS, and cleansing data using DQS.
Build a solid foundation with a data warehousing solution designed for real-time transactional and analytical processing environments.
At this stage of technological development,mankind has not come up with a better data warehouse than optical media.