Data extraction, transformation, and loading
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The document discusses the key functions of ETL (extract, transform, load) processes which are important for reshaping relevant data from source systems into useful information stored in a data warehouse. It examines the challenges and techniques for data extraction and the wide range of transformation tasks. It also explains that ETL encompasses extracting data from source systems, transforming it into appropriate formats for the data warehouse, and loading it into the data warehouse repository.
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An Overview on Data Quality Issues at Data Staging ETL
An Overview on Data Quality Issues at Data Staging ETLidescitation A data warehouse (DW) is a collection of technologies
aimed at enabling the decision maker to make better and
faster decisions. Data warehouses differ from operational
databases in that they are subject oriented, integrated, time
variant, non volatile, summarized, larger, not normalized, and
perform OLAP. The generic data warehouse architecture
consists of three layers (data sources, DSA, and primary data
warehouse). During the ETL process, data is extracted from
an OLTP databases, transformed to match the data warehouse
schema, and loaded into the data warehouse database
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data warehousing need and characteristics. types of data w data warehouse arc...
data warehousing need and characteristics. types of data w data warehouse arc...aasifkuchey85 The document discusses data warehouses and database management systems (DBMS). It provides information on:
- The key difference between online analytical processing (OLAP) and online transaction processing (OLTP) databases and their purposes. OLAP databases contain historical data for analysis while OLTP databases contain current operational data.
- The top-down and bottom-up approaches for constructing a data warehouse, which involve extracting data from external sources, transforming and loading it, and then storing it in data marts or a centralized data warehouse.
- Some common components of a data warehouse architecture including the external sources, staging area, data warehouse, data marts, and data mining.
- Properties and features of a DB
Meta Data and it's Type
Meta Data and it's TypeFaisal Liaqat Metadata is data that provides information about other data. In a data warehouse, there are three types of metadata: operational metadata contains information about the structure and format of source data; extraction and transformation metadata documents how data is extracted from sources and transformed; and end-user metadata enables users to navigate and query the data warehouse using their own business terms. Metadata plays a crucial role in data warehouses by connecting all components and allowing users to access data in a meaningful way.
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Data extraction, transformation, and loading
- 1. *
- 2. * *Survey broadly all the various aspects of the data extraction ,transformation and loading(ETL)functions *Examine the data extraction function,its challenges,its tecniques,and learn how to evaluate and apply the tecniques *Discuss the wide range of tasks and types of the transformation function
- 3. *Understand the meaning of data integration and consolidation *Percieve the importance of data load function
- 4. *ETL functions reshape the relevant datd from the source systems into useful information to be stored in the data warehouse. *Without these function,there would be no strategic information in the data warehouse. *If the source data is not extracted correctly,cleansed,and integrated in the proper formats,query processing,the backbone of the data warehouse,could not happen.
- 5. * *The architecture divided into three functional areas such as data acquisition, data storage and information delivery. *ETL encompass the area of data acquisition and data storage. *These are back-end processes that cover the extraction of data from the source systems. *Next, source data into the exact formats and structures appropriate for data storage in data warehouse. *After data transformation of the data, moving data into the data warehouse repository.
- 7. * *Data extraction-why not extract all of operational data and dump into data warehouse? *Avoid creating