The final step in the ETL process is to load the newly transformed data into a new destination. ETL tools automate the extraction process and create a more efficient and reliable workflow. Cloud, hybrid, and on-premises environmentsĪlthough it can be done manually, hand-coded data extraction can be time-intensive and prone to errors.Raw data can be extracted from a wide range of sources, including: In this first step of the ETL process, structured and unstructured data is imported and consolidated into a single repository. To make a complex data strategy like this work, the data must be able to travel freely between systems and apps.īefore data can be moved to a new destination, it must first be extracted from its source. Most manage data from a variety of sources and use a number of data analysis tools to produce business intelligence. Step 1: Extractionįew businesses rely on a single data type or system. These are data extraction, data transformation, and data loading. Three steps make up the ETL process and enable data to be integrated from source to destination.
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As a result, the ETL process plays a critical role in producing business intelligence and executing broader data management strategies. OverviewĪ typical ETL process collects and refines different types of data, then delivers the data to a data warehouse such as Redshift, Azure, or BigQuery.ĮTL also makes it possible to migrate data between a variety of sources, destinations, and analysis tools. ETL also makes it possible for different types of data to work together. ETL allows businesses to gather data from multiple sources and consolidate it into a single, centralized location. The process of ETL plays a key role in data integration strategies. ETL in the Cloud: What the Changes Mean for YouĮTL stands for "extract, transform, and load.".
![source unpack 2 missing interface source unpack 2 missing interface](https://pbs.twimg.com/media/E149YqsX0AEx1mq.jpg)