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In this article we’re going to check what is an Azure function and how we can employ it to create a basic extract, transform and load (ETL) pipeline with minimal code. Extract, transform and Load Before we begin, let’s shed some light on what an ETL pipeline essentially is. ELT stands for extract, load and transform.
These teams are as follows: Advanced analytics team (data lake and data mesh) – Dataengineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.
Real-time Data Ingestion and Processing Data lakes can handle real-time data streams, making them ideal for use cases that require immediate data ingestion and processing.
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