In computing terms the ETL pipeline stands for extract, transform and load which is a process in database usage in order to prepare the data for analysis specifically in data warehousing. The ETL pipeline was a popular concept during the 70s. In an open source data pipeline, it is quite common to execute the three phases simultaneously because extraction of the data requires a lot of time. As the data is being extracted another transformation process executes the same when processing the data which has already been received and preparing it to be loaded when the process for data loading begins without wasting time by waiting for the completion of the earlier three phases.
The systems of ETL regularly combine data from an open source data pipeline which is typically developed and supported by various vendors or hosted on computer hardware which is separate. The separate systems which contain the original data are regularly managed and operated by different individuals managing different tasks at the same time. It is quite common to notice it cost accounting system combining data from payroll, purchasing or sales in an ETL pipeline.
The ETL process is a combination of three stages which are explained below for your understanding.
The first stage of the ETL process involves the extraction of data from the systems where it is stored. This is an important aspect of the process because extracting the data appropriately sets the stage to successfully complete the subsequent stages. Data warehousing projects of all types combine data from various sources systems where the separate systems may be using different formats for organizing the data. The common data source formats may include but not just limited to XML, relational databases, flat files and even database structures like information management systems or other structures of data. The streaming of the extracted data and loading on the move to the destination database is an alternate method of performing ETL when the need for no intermediate data storage is needed. In general terms, the extraction stage aims to convert the extracted data into a single format which is suitable for transformation processing.
The second stage of the ETL process is the transformation stage whereby a set of rules and functions are applied to the data extracted for preparing it to be loaded to the end target. It is quite common to notice that some data does not require any transformation and such data is identified as “direct move” or pass through data. Cleaning of the data is also an important function of the transformation when only the proper data to the target is passed through. Challenges may be encountered when different systems interact with the interfacing and communicating. Sets of characters which may be available in one system may not be available in the other. In some cases, one or more transformation types may be needed in order to meet the technical and business needs of the server or the data warehouse.
The final stage of the ETL process is the load phase which could be a simple flat file which is delimited from a data warehouse. This process can vary widely depending on the needs of the organization. Some data warehouses may overwrite information by updating the extracted data frequently on a weekly or a monthly basis while other data warehouses may include fresh data at regular intervals in a historical form which could even be hourly. In order to understand this, it would be advisable to consider a data warehouse which is needed to maintain sales records for the previous year. Data warehouses of this type overwrite any data which is older than a year with fresh data. However, the entry of the data for any one year period is done in a historical manner. The scope and timing for replacing or appending the data are strategic design choices which are dependent on the needs of the business and the time available. Systems that are more complex can maintain a history along with an audit trail of any changes loaded within the data warehouse. As the load phase interacts with the database the restrictions defined in the database schema along with the triggers activated during the data load will apply for uniqueness, mandatory fields, and referential integrity. These can also contribute to the overall quality performance of the data during the ETL process.
This, in short, is the ETL process in a nutshell in computing terms.