Extract, load, transform (ELT) is used nowadays as an alternative to extract, transform, load (ETL). In contrast to ETL, in ELT models the data is not transformed at entry to the destination (data warehouse, database, cloud, data lake), but stored in its original format.
ELT leverages the processing power of your database to enable faster loading/processing. Data cleansing, enrichment, and transformation occur after loading the data into the destination. Some of the features are:
Saves processing time - Data can be loaded in the raw format even before you decide to process and manipulate it. In addition, however, only that part of the data can be transformed that is needed, rather than having to process all the data into a new format before it can be analysed or used. For example, one can apply transformations to a subset of the data only.
Cloud-based data warehouses offer near-endless storage capabilities and scalable processing power. Platforms like Amazon Redshift and Google BigQuery make ELT pipelines possible because of their incredible processing capabilities.
Flexibility - With traditional ETL, data is stored in an OLAP warehouse in a well thought and decided structure. The existing structure/format of data may require modifications to add a new type of analysis or workload. ELT, in the same scenario, provides the flexibility to transform data on the fly to produce different types of metrics, forecasts and reports without re-organising or re-structuring anything.
Biggest Advantages of ELT
Thus, concluding, the advantages of ELT are -
- Its flexibility and ease of storing new, unstructured data.
- Saves time by quickly processing and saving any type of information.
- Provides quick access to trusted datasets.