Evaluating ETL and ELT approaches for data integration
ETL and ELT are two main approaches to integrating, loading, and preparing data in BigQuery. When building a data analytics practice, to provide ongoing data value, you will want to decide on one of these approaches.
In ETL, data is extracted from a data source, transformed, and then loaded into a data warehouse or other target system. The transformation step is often complex and time-consuming, as it involves cleaning, validating, and standardizing the data. There are SaaS tools that automate and manage ETL pipelines, and there are many options today to create your own ETL pipelines by joining multiple services before the data arrives in BigQuery.
The other primary data integration approach is ELT. ELT is when data is extracted from a data source and loaded directly into a target system. Any transformation steps are then performed in the target system. This approach is often faster than ETL as the transformations can be...
