![]() ![]() Transformation: The data may be checked for duplications or discrepancies, and organized for further use.Loading and storage: Data is loaded via a data pipeline from the source into the target system (the data warehouse), where it awaits transformation.Extraction: New data is collected from different areas of the business, including company financial records, customer transactions, apps, and inventory.The overall data analytics process using ELT involves several stages: ![]() This lets business users transform raw data within a data warehouse at any time for any particular use case. More and more businesses are opting to skip preload transformations in favor of running transformations at query time - a process referred to as ELT (extract, load, transform). Today, however, cloud-based data warehouses from most providers - including Amazon Redshift from AWS, Microsoft Azure SQL Data Warehouse, Oracle, Google BigQuery, and Snowflake - offer flexible infrastructures with processing and storage capacity that can quickly scale based on an organization's data needs. Due to the limited capacity of these expensive systems, business users needed to perform as much prep work as possible before loading data into the management system. Historically, businesses used ETL (extract, transform, load) tools to aggregate data into expensive on-premises data warehouse systems. ![]()
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