Below are the standard procedure for data extraction. This phase of ETL is all about pulling data from sources such as databases, applications, web pages, and files. DATA EXTRACTION STEPSĭata extraction is the most initial and essential phase of the ETL process. Also, data engineers must be careful regarding response time and performance critical metrics of the implemented ETL process. ![]() Hence the data engineer must be conscientious while designing an ETL process. The main objective of the ETL process is to extract all the required data from the source seamlessly. Data auditing includes data profiling and assessing poor-quality data and its impact on organizational performance. AUDIT OF DATA SOURCEĪn audit of data sources includes assessing the information quality and usefulness of the available data for fulfilling the business requirement. Usage and Latency – Analyzing how the data will be loaded at the target database and how the target users will consume it. The organizations analyze the available data using business analytics tools, which help to extract a broad range of data sources and types.ĭata Source and target analysis – Analysis of how the data gets produced and in what format the data needs to be stored. ![]() One of the most essential parts of the ETL best practices is a clear understanding of business requirements. Top 10 ETL Best Practices UNDERSTAND THE PROJECT REQUIREMENTS.
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