Transform the data by applying business rules, cleansing, and validating the data. Connectors: Flat files, XML, Oracle, IBM Db2, SQL Server, Teradata, Sybase, Vertica, Netezza, Greenplum, IBM Websphere MQ, ODBC, JDBC, Hadoop Distributed File System (HDFS), Hive/HCatalog, JSON, Mainframe (IBM z/OS),, SAP/R3 Transform To obtain business value from all this data means the ETL tool you choose should have the ability to extract data from many different sources. Each system will typically store data in different mutually incompatible formats. Modern organizations have data stored in many disparate systems such as: customer relationship management (CRM), sales, accounting, and stock tracking to name just a few. Most organizations will have data coming in from more than one source, meaning it will be necessary to automate the task of collecting that data and formatting it correctly for the data warehouse. This first phase refers to the task of pulling in data from a variety of sources. ExtractĮxtract data from one or more source systems containing customer, financial, or product data. Alternatively, purpose built ETL software may offer a graphical user interface to build and run ETL processes, which typically reduces development costs and improves maintainability. Precisely offers ETL solutions to help break down data silos Learn moreĮTL processes can be built by manually writing custom scripts or code, with the trade-off that as the complexity of the ETL operations increase, scripts become harder to maintain and update.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |