ELT

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[/vc_column][/vc_row][vc_row][vc_column][vc_empty_space][vc_custom_heading text=”What it is and Why it matters” font_container=”tag:h2|text_align:center”][vc_empty_space][vc_column_text]ETL is a type of data integration that refers to the three steps (extract, transform, load) used to blend data from multiple sources. It’s often used to build a data warehouse. During this process, data is taken (extracted) from a source system, converted (transformed) into a format that can be analyzed, and stored (loaded) into a data warehouse or other system. Extract, load, transform (ELT) is an alternate but related approach designed to push processing down to the database for improved performance.[/vc_column_text][vc_empty_space][vc_custom_heading text=”Why ETL Is Important” font_container=”tag:h4|text_align:center”][vc_empty_space][vc_column_text]Businesses have relied on the ETL process for many years to get a consolidated view of the data that drives better business decisions. Today, this method of integrating data from multiple systems and sources is still a core component of an organization’s data integration toolbox.[/vc_column_text][vc_empty_space][vc_row_inner][vc_column_inner width=”1/2″][vc_single_image image=”1533″ img_size=”large”][/vc_column_inner][vc_column_inner width=”1/2″][vc_column_text]

  • When used with an enterprise data warehouse (data at rest), ETL provides deep historical context for the business.
  • By providing a consolidated view, ETL makes it easier for business users to analyze and report on data relevant to their initiatives.
  • ETL can improve data professionals’ productivity because it codifies and reuses processes that move data without requiring technical skills to write code or scripts.
  • ETL has evolved over time to support emerging integration requirements for things like streaming data.
  • Organizations need both ETL and ELT to bring data together, maintain accuracy and provide the auditing typically required for data warehousing, reporting and analytics.

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