The cost-time-value equation for ETL is defined by three characteristics: When ETL is used properly, it will permit you to get more out of your data.ĮTL is an important step in the data integration process The ETL value equationĪ complete end-to-end ETL process may take a few seconds or many hours to complete depending on the amount of data and the capabilities of the hardware and software. Once the load phase is finished, the data will then reside in a location that is known to everyone who has permission to access that data.Ĭonnectors: Flat files, XML, Oracle, IBM Db2, SQL Server, Teradata, Sybase, Vertica, Netezza, Greenplum, ODBC, JDBC, Hadoop Distributed File System (HDFS), Hive/HCatalog, Mainframe (I BM z/OS),, Tableau, QlikView A quality data warehouse will provide those within the organization easy access to the information they need, when they need it. With all of the data successfully collected and transformed as necessary, the last phase is to load that data to the warehouse for storage and access. Load the results into one or more target systems such as a data warehouse, datamart, or business intelligence reporting system. Date: DateAdd, DateDiff, DateLastDay, DatePart, IsValidDate.Text: Concatenate, CharacterLengthOf, LengthOf, Pad, Replace, ToLower, ToText, ToUpper, Translate, Trim, Hash.Logical: And, Or, Not, IfThenElse, RegEx, Variables.Mathematical: +, -, x, /, Abs, IsValidNumber, Mod, Pow, Rand, Round, Sqrt, ToNumber, Truncate, Average, Min, Max.Transforms: Aggregation, Copy, Join, Sort, Merge, Partition, Filter, Reformat, Lookup.To meet the current and future requirements of the business, your ETL tool should be able to perform all of the following types of operations: To combine and report on the data extracted in Stage 1, for example comparing orders from the order entry system with stock levels in the warehouse management system, may require multiple steps and many different operations. This is the phase where generic data is turned into something that can be a valuable asset for the business. In other words, the raw data flowing into the organization may not be suitable for any kind of use, so it needs to be cleaned, filtered, etc. Largely, transformation in this context has to do with manipulating the data in a manner which serves the needs of the business. 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.
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