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Panoply is an automated data warehouse that allows you to load unlimited volumes of data and easily perform ad hoc transformations and rollbacks, without a full ETL setup and without the need for ETL testing. Any manipulation beyond copying is a transformation. The entire ETL process is built up with data transformations. Reusing the predefined transformations during the ETL process development will speed up the work. ETL stands for Extract-Transform-Load and it is a process of how data is loaded from the source system to the data warehouse. The requirement is that an ETL process should take the corporate customers only and populate the data in a target table. An example of an automated data management system that supports ELT, doing away with the complexity of the ETL process, is Panoply. ETL process involves the following tasks: 1. Transformation: The process of manipulating data. Data is extracted from an OLTP database, transformed to match the data warehouse schema and loaded into the data warehouse database. An ETL with the correct logging process is important to keep the entire ETL operation in a state of constant improvement, helping the team manage bugs and problems with data sources, data formats, transformations, destinations, etc. A source table has an individual and corporate customer. ETL Process: ETL processes have been the way to move and prepare data for data analysis. Examples include cleansing, aggregating, and … account: This is the user friendly name for the view/client, which will allow users to easily select which view/client they wish to report against). Few transformations in ETL can be predefined and used across the DW system. ETL is a process in Data Warehousing and it stands for Extract, Transform and Load.It is a process in which an ETL tool extracts the data from various data source systems, transforms it in the staging area and then finally, loads it into the Data Warehouse system. We will use a simple example below to explain the ETL testing mechanism. ETL stands for Extract, Transform and Load, which is a process used to collect data from various sources, transform the data depending on business rules/needs and load the data into a destination database. For example, while data is being extracted, a transformation process could be working on data already received and prepare it for loading, and a loading process can begin working on the prepared data, rather than waiting for the entire extraction process to complete. ETL developers spend their time in building (or) re-processing all the data transformations. The need to use ETL arises from the fact that in modern computing business data resides in multiple locations and in many incompatible formats. Often, the three ETL phases are run in parallel to save time. Extracting the data from different sources – the data sources can be files (like CSV, JSON, XML) or RDBMS etc. The process of resolving inconsistencies and fixing the anomalies in source data, typically as part of the ETL process. Logging ETL processes is the key guarantee that you have maintainable and easy-to-fix systems. This is the first step in ETL process. 5) Scheming test examples and test situations from every obtainable contribution 6) If all test examples are set, pre-action test and data training are done 7) Finally, implementation is completed till outlet condition is fulfilled 8) Once the total ETL process is completed, a report of it is done and then finishing is obtained. 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