Architecture of ETL
ETL stands for Extract, Transform & Load. In today’s Data Warehousing the world. This term should extend Extract, Watch, Profile, Analyze, Cleanse, and Transform & Load. ETL with the necessary focus on data quality & metadata. The ETL process became a popular concept in the 1970s
The main goal of Extracting is to off-load the data from the source. The systems as fast as possible and as less compress for these source systems. Its development team and its end-users as possible. This implies that the type of source system and it is characteristics – OLTP system. Legacy data, many instances, old Data Warehouse, archives, fixed & variable external data, spreadsheets. it is also most applicable extraction method
Transform & Loading
The Transform & Loading the data integrated data to the presentation area. Which can access via front-end tools by the end-user community? The emphasis should be on using the offered functionality by the chosen ETL-tool. It is not enough to use an ETL-tool. Many use various backdoors .which do not maximize the usage of the tool. In a medium to the large scale data warehouse environment must standardize. It is possible instead customization. This will reduce the throughput time of the different source-to-target development activities. Which form the bulk of the traditional ETL effort system. A side-effect in a later stage is called the ETL-scripts. It is based on this standardization and pre-defined metadata.
The Monitoring of the data enables a verification of the data. This moved throughout the entire ETL process
The Data Profiling used to generate statistics about the sources. And as such, the goal here is to ‘understand’ the sources. It will use analytical techniques to discover the true content. The validating data patterns & formats identifying and validating redundant data across data source. That the correct tooling forward to automate this process. The huge amounts and variety of data.
Data Analysis will analyze the results of the profiled data. The communication medium between the source and the data warehouse issues.
The Cleansing section, the errors found can fix based on a pre-defined set of metadata rules. Here a distinction needs to make between completely or partly rejecting the record
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