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What difference between data warehouse and traditional database used OLTP?
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∙ 14y ago
{| |+ Differences |- | Data warehouse database OLTP database Designed for analysis of business measures by categories and attributes Designed for real time business operations. Optimized for bulk loads and large, complex, unpredictable queries that access many rows per table. Optimized for a common set of transactions, usually adding or retrieving a single row at a time per table. Loaded with consistent, valid data; requires no real time validation Optimized for validation of incoming data during transactions; uses validation data tables. Supports few concurrent users relative to OLTP Supports thousands of concurrent users. |}
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Difference between data warehouse and datamart?
A data warehouse has multiple functional areas whereby a centralized organizational unit is responsible for implementing it. On the contrary, data marts focus on particular functional areas hence are simple forms of a data warehouse.
What is the difference between data warehouse testing and conventional testing?
ETL stands for Extract-Transform-Load and it is a process of how data is loaded from the source system to the data warehouse. Data is extracted from an OLTP database, transformed to match the data warehouse schema and loaded into the data warehouse database.Many data warehouses also incorporate data from non-OLTP systems such as text files, legacy systems and spreadsheets. Conventional testing is a sort of testing in which the test engineers will check the developed application or its related parts are working accordingly to the expectations of customer or not, from coding phase of SDLC to the end
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Database file is one that is stored on a server and it is in the format that the database needs to read it. It is also stored in the database catalog. A flat file is one that is dumped from a database to import or export into another database or program. A flat file is fields of data separated by delimiters, which keep the fields separated for export/import.
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Difference between data warehouse and OLTP?
A data warehouse differs from OLTP in that the former handles many large and complex queries regarding various rows of a table while the latter retrieves data from single rows. At the same time, a data warehouse is not real time and supports few users at a time compared to OLTP that can support many concurrent users.
What is the difference between database and data warehouse?
A database is a structured collection of data that is organized in a way that facilitates efficient storage, retrieval, and updating of data. A data warehouse, on the other hand, is a specialized type of database that is specifically designed for data analysis and reporting. Data warehouses typically store large volumes of historical data from various sources to support business intelligence and decision-making processes.
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Hierarchical databases organize data in a tree-like structure with parent-child relationships and a strict one-to-many hierarchy, while network databases use a more flexible model with many-to-many relationships through pointers or links between records. Hierarchical databases are faster for accessing data along predefined paths, while network databases allow for more complex data relationships but can be more complex to manage.
What are the key relationships between data warehouse and data mining?
Data warehouse is the database on which we apply data mining.
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the difference is about the transactions. the normal database is not included deadline, but the real time database is.
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A database is a structured collection of data organized for efficient retrieval and updating. A datastore is a repository for storing and managing data, which can include databases as well as other types of data storage systems such as key-value stores or document stores. Essentially, a database is a type of datastore, but not all datastores are databases.
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A record is a single row of data within a database that contains information about a specific entity. A database is a collection of records organized in a structured format that allows for storage, retrieval, and manipulation of data. In other words, a database is like a container that holds records.
