ETL Overview
Within an enterprise there are various different applications and data sources which have to be integrated together to enable Data Warehouse to provide strategic information to support decision-making. On-line transaction processing (OLTP) and data warehouses cannot coexist efficiently in the same database environment since the OLTP databases maintain current data in great detail whereas data warehouses deal with lightly aggregated and historical data. Extraction, Transformation, and Loading (ETL) processes are responsible for data integration from heterogeneous sources into multidimensional schemata which are optimized for data access that comes natural to human analyst. In an ETL process, first, the data are extracted from
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2. Incremental extraction: In this type of extraction only the changes made to the source systems will be extracted with respect to the previous extraction. Change data capture (CDC) is mechanism that uses incremental extraction.
There are two physical methods of extraction: Online extraction and Offline extraction. Online extraction process of ETL connects to source system to extract the source tables or store them in a preconfigured format in intermediary systems e.g., log tables. In Offline extraction the data extracted is staged outside the source systems.
Transformation
The transform stage applies a series of rules or filters to the extracted data from to derive the data for loading into the end target. An important function of transformation is the cleaning of data, which process aims to pass only "proper" data to the target. one or more of the following transformation types:
1. Selecting only certain columns to load.
2. Translating coded values and encoding free-form values.
4. Deriving a new calculated value.
5. Sorting.
6. Joining data from multiple sources and duplicating the data.
7. Aggregation and disaggregation.
9.Turning multiple columns into multiple rows or vice versa.
10. Splitting a column into multiple columns.
12. Lookup and validate the relevant data from tables or referential files for slowly
imaging application. This is because the crucial data required for the classification phase are derived at this stage. Feature extraction is the process of estimating
d) The information is acquired on the data bus and send it to TDO. (Alghafli, Jones, and Martin. 2012).
the following is true about the process of read data, as described in the chapter?
This step includes the task of describing a subset of data for each type of user based on his/her privileges and requirements. This is done to ensure that no additional information is given to the user. Views provide a level of abstraction to the database.
Data in computerized form is discoverable, even if the paper “hard copies” of the information have been produced. The producing party can be required to design a computer program to extract the data from its computerized business records.
Extraction: This is the process of extracting any evidence that is found relevant to the situation at hand from the working copy media and subsequently saved to another form of media as well as printed
24) Before it can be loaded into the data warehouse, operational data must be extracted and
The data is then sent back through the system to the original user. The information that is on the data coming back could have came from a wide array of sources such as books, financial markets, embedded chips or even made up by someone trying to fool the user. The History? The Internet is first
The databases are required to be accessed very properly; the broken or fragmented data needs to be recovered. For querying and reporting purposes the data should be easily accessible
A data warehouse is a large databased organized for reporting. It preserves history, integrates data from multiple sources, and is typically not updated in real time. The key components of data warehousing is the ability to access data of the operational systems, data staging area, data presentation area, and data access tools (HIMSS, 2009). The goal of the data warehouse platform is to improve the decision-making for clinical, financial, and operational purposes.
Transaction processing systems (TPS) provide data collection, storage, processing and outputting functionalities for the core operations of a business. These functions are necessary for operational managers. In that way the data generated by the TPS answers general business questions and to track the flow of transactions throughout the business. TPS can keep track of such systems as payroll, inventory, sales, shipping and other vital business systems.
Purpose: It includes converting existing data for use in a new system. Verification of the old data becomes imperative to the useful computer system. Data input and data verification could be done in this phase. By
As the reader has got now all the information available about theory and methodology, it’s time to move on to the concrete part. Indeed, next header explains the extraction of data.
INPUT: It gather data 's from the environment to the system and is process to output.
ii) Preparation of the text : This step involves cleaning of the extracted data before analyzing it. Here non-textual and irrelevant content for the analysis are identified and discarded