Integrated ETL

Extract, transform and load is the core process of data integration and is typically associated with data warehousing. ETL tools extract data from a chosen source, transform it into new formats according to business rules, and then load it into target data structure. Managing rules and processes for the increasing diversity of data sources and high volumes of data processed that ETL must accommodate, make management, performance and cost the primary and challenges for users. The traditional ETL approach requires users to map each physical data item with a unique metadata description; newer ETL tools allow the user to create an abstraction layer of common business definitions and map all similar data items to the same definition before applying target-specific business rules, isolating business rules from data and allowing easier ETL management.

Curveline ETL process feature:

⇒ Curveline ETL feature automates the process of translating and importing the data into the Curveline planning application
⇒ Integrates third party general ledger and other systems
⇒ Connectivity to JDBC/ODBC databases
⇒ Data import/export from/to excel, CSV and other text file formats
⇒ Validation and translation rules
⇒ Scalable
⇒ Recoverable
⇒ Auditable
⇒ Batch process scheduling

ETL is used to migrate data from one database to another, to form data marts and data warehouses and also to convert databases from one format or type to another. The transform stage applies a series of rules or functions to the extracted data from the source to derive the data for loading into the end target. Some data sources will require very little or even no manipulation of data. In other cases, one or more of the following transformation types may be required to meet the business and technical needs of the target database:


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