ELT (Extract, Load, Transform) is primarily used in cloud environments where data is loaded into a data warehouse or data lake before being transformed.
ELT leverages the processing power of modern data warehouses and data lakes, making it particularly suitable for:
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Cloud Data Warehouses: Services like Amazon Redshift, Google BigQuery, and Snowflake are prime examples. ELT allows you to quickly load raw data into these platforms and then use their built-in SQL engines to perform transformations.
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Cloud Data Lakes: ELT is used to populate data lakes such as Amazon S3, Azure Data Lake Storage, and Google Cloud Storage. The raw data is loaded into the data lake, and then transformation jobs are executed using services like Spark or serverless functions.
Why ELT is preferred in these environments:
- Scalability: Cloud environments offer virtually unlimited scalability, which is crucial for handling large datasets. ELT leverages this scalability to perform transformations quickly and efficiently.
- Cost-effectiveness: Cloud-based data warehouses and data lakes offer pay-as-you-go pricing models. ELT allows you to only pay for the compute resources used during the transformation process.
- Flexibility: ELT enables you to transform data in place, without having to move it to a separate transformation server. This reduces data latency and simplifies the data pipeline.
- Modern Applications: ELT is well-suited for data used within cloud environments, often including applications accessed on-demand continuously.
In summary, ELT is the dominant paradigm for data transformation in cloud-based data warehousing and data lake environments because it leverages the scalability, cost-effectiveness, and flexibility of these platforms.
