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Author: Admin | 2025-04-28
Attributes into the data warehouse.Cleaning – filling up the NULL values with some default values, mapping U.S.A, United States, and America into USA, etc.Joining – joining multiple attributes into one.Splitting – splitting a single attribute into multiple attributes.Sorting – sorting tuples on the basis of some attribute (generally key-attribute).Loading: The third and final step of the ETL process is loading. In this step, the transformed data is finally loaded into the data warehouse. Sometimes the data is updated by loading into the data warehouse very frequently and sometimes it is done after longer but regular intervals. The rate and period of loading solely depends on the requirements and varies from system to system.ETL process can also use the pipelining concept i.e. as soon as some data is extracted, it can transformed and during that period some new data can be extracted. And while the transformed data is being loaded into the data warehouse, the already extracted data can be transformed. The block diagram of the pipelining of ETL process is shown below: ETL Tools: Most commonly used ETL tools are Hevo, Sybase, Oracle Warehouse builder, CloverETL, and MarkLogic.Data Warehouses: Most commonly used Data Warehouses are Snowflake, Redshift, BigQuery, and Firebolt.ADVANTAGES OR DISADVANTAGES:Advantages of ETL process in data warehousing:Improved data quality: ETL process ensures that the data in the data warehouse is accurate, complete, and up-to-date.Better data integration: ETL process helps to integrate data from multiple sources and systems, making it more accessible and usable.Increased data security: ETL process can help to improve data security by controlling access to the data warehouse and ensuring that only authorized users can access the data.Improved scalability: ETL process can help to improve scalability by providing a way to manage and analyze large amounts of data.Increased automation: ETL tools and technologies can automate and simplify the ETL process, reducing the time and effort required to load and update data in the warehouse.Disadvantages of ETL process in data warehousing:High cost: ETL process can be expensive to implement and maintain, especially for organizations with limited resources.Complexity: ETL process can be complex and difficult to implement, especially for organizations that lack the necessary expertise or resources.Limited flexibility: ETL process can be limited in terms of flexibility, as it may not be able to handle unstructured data or real-time data streams.Limited scalability: ETL process can be limited in terms of scalability, as it may not be able to handle very large amounts of data.Data privacy concerns: ETL process can raise concerns about data privacy, as large amounts of data are collected, stored, and analyzed.Overall, ETL process is an essential process in data warehousing that helps to ensure that the data in the data warehouse is accurate, complete, and up-to-date.
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