ELT is the future of dataĮLT simplifies data integration, results in lower failure rates, allows for flexible scaling and moves the transformation process to the warehouse, where you can apply such skills as SQL to achieve data transformation. And it allows the entire process – from extraction to load to transformation – to be done by a data analyst rather than requiring an engineer. This not only eliminates data engineering time in building custom pipelines but also in maintaining them. Moreover, with the ELT process, data pipelines can be automated. This saves you re-engineering time and cost and gives you more flexibility to query the raw data as many times as you want. With ELT, because the raw data is already loaded into the destination system, a data analyst can create the queries in real-time without engineering resources. This can be costly, time-consuming and will require data engineering expertise. However, in ETL, when your query needs change, you will need to rebuild your ETL pipelines. One aspect of data analytics is that there is often a need to mine the same data source for different purposes. In ELT, the raw data is stored in the destination system, so it does not need to be reloaded and can be transformed again immediately, saving significant time. In ETL the raw data source must first be re-loaded (assuming it’s still available) to the secondary system and then re-transformed.
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