Data extraction and transformation (cleaning, sanitising, and scrubbing) are two of the three phases of ETL, a three-step process in computing. Data may be gathered from a variety of sources and sent to a variety of destinations when it has been compiled. It is possible for system administrators to do ETL processing manually as well as using software programmes. Automating the whole process using ETL software is common, and it may be done manually, on a recurring schedule, or as a collection of individual tasks.
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Extraction of data from a source system is made easier with an ETL system that adheres to data type and validity criteria and checks that the data is structurally compatible with the output requirements. If you're looking for an ETL system that can produce data in a presentation-ready manner, there are plenty out there that can do so.
The ETL technique was popularised in the 1970s and is often used in data warehousing today. Typically, ETL systems combine data from many applications (systems) that are created and maintained by different suppliers or housed on different computer hardware. The original data is often contained in distinct systems that are controlled and administered by different parties. Data from several sources may be merged into a single cost accounting system.
Extract
extraction, transformation, and loading all describe the process of loading data into a target database, such as an operational database store or a data mart, for querying and analysis purposes.
Using ETL, data is extracted from a system and processed in another (s). This is often the most critical step in the ETL process, as it ensures the success of all the other steps that follow. It's not uncommon for data warehousing operations to include information from several sources. Data may be organised and/or formatted in a variety of ways by various systems. In addition to relational databases and other data structures like Information Management Systems (IMS) and Virtual Storage Access Methods (VSAM) and Indexed Sequential Access Methods (ISAM), common data-source formats include XML, JSON, and flat files, as well as those obtained from outside sources such as web spidering and screen scraping. When no intermediate data storage is necessary, another option to execute ETL is to stream the extracted data source and load it on-the-fly into the destination database. The data science course in India can be helpful to enhance your skills.
Transform
Data transformation is the process of applying a set of rules or functions to the extracted information before it is loaded into the final destination.
Data cleansing is an important part of transformation, which aims to deliver only the most accurate information to the target. When different systems come together, the difficulty lies in getting them to talk to one another. Depending on the system, some character sets may or may not be accessible.
To meet the needs of a server or a data warehouse, one or more of the following types of transformations may be necessary:
- Columns Only: (or selecting null columns not to load). Source data "attributes" include roll no., age, and income, although the option may simply include roll no. and salary. Null salary records may be omitted.
- Hex: (If source system codes male as "1" and female as "2", but warehouse codes male as "M" and female as "F")
- Encoding: (E.g., mapping "Male" to "M")
Load
During the loading process, data is loaded into the eventual destination, such as a flat file or data warehouse. This technique depends on corporate demands. Daily, weekly, or monthly data warehouse updates may erase earlier information. Other data warehouses (or portions of the same) may provide new historical data every hour. Imagine a data warehouse that stores last year's sales information to understand this. Any data older than a year in this warehouse is deleted and replaced. Data entry is done chronologically. Timing and scope are strategic design considerations based on the company's needs. Advanced systems may keep a history and audit record of all data warehouse changes. When the load phase interacts with the database, database structure and data load triggers (such as uniqueness, referential integrity, and essential fields) affect data quality.
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