There are various reasons to implement ETL pipelines in Data Science. Data from multiple systems (CRMs, social media platforms, Web reporting, etc.) are migrated, aggregated, and modified to meet the parameters of the destination database to deliver significant insights. ETL pipelines help data scientists to prepare data for analytics and business intelligence. Companies that use batch processing can now switch to continuous processing without interrupting their current operations. The transition to cloud-based software services and enhanced ETL pipelines can ease data processing for businesses. ETL is a must-have for data-driven businesses. Anyone who works with data, whether a programmer, a business analyst, or a database developer, creates ETL pipelines, either directly or indirectly. This data warehouse is accessible to data analysts and scientists and helps them perform data science tasks like data visualization, statistical analysis, machine learning model creation, etc. It entails gathering data from numerous sources, converting it, and then storing it in a new single data warehouse.
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