IBM ETL Services

The ETL process, which stands for extract, transform, and load, is a data integration methodology that amalgamates data from various sources into a unified and consistent data repository. This repository is subsequently loaded into a data warehouse or another designated target system. In the 1970s, with the burgeoning popularity of databases, ETL emerged as a pivotal process for integrating and loading data, facilitating computation and analysis. Over time, it evolved into the predominant method for handling data in projects related to data warehousing. ETL serves as the cornerstone for data analytics and machine learning workflows. By applying a set of business rules, ETL systematically cleanses and organizes data to meet specific business intelligence requirements, such as monthly reporting. Moreover, it is adept at addressing more sophisticated analytics that can enhance backend processes or user experiences. Organizations often employ ETL to: Extract data from legacy systems Enhance data quality and enforce consistency through data cleansing Load data into a designated target database

Extract: During the extraction phase, raw data is duplicated or exported from its source locations to a designated staging area. Data management teams have the flexibility to extract data from various sources, whether structured or unstructured. These sources encompass, but are not confined to: SQL or NoSQL servers CRM and ERP systems Flat files Email Web pages Transform: Within the staging area, the raw data undergoes comprehensive processing. This involves the transformation and consolidation of data tailored for its intended analytical use. The transformation phase encompasses various tasks, such as: Filtering, cleansing, de-duplicating, validating, and authenticating the data. Performing calculations, translations, or summarizations based on the raw data. This includes tasks such as standardizing row and column headers, converting currencies or other units of measurement, editing text strings, and more. Conducting audits to ensure data quality and compliance. Removing, encrypting, or safeguarding data as dictated by industry or governmental regulations. Formatting the data into tables or joined tables to align with the schema of the target data warehouse. Load: In the final step, the transformed data is transferred from the staging area to the designated data warehouse. Typically, this involves an initial loading of all data, followed by periodic loading of incremental data changes and, less frequently, full refreshes to replace existing data in the warehouse. For most organizations employing ETL, the process is automated, well-defined, continuous, and executed in batches. ETL activities are typically scheduled during off-hours when the traffic on both the source systems and the data warehouse is minimal.