![]() ![]() This allows syncs to be rolled back and resumed as needed. Finally, a staging area is useful when there are issues with loading data into the centralized database.It avoids performing extractions and transformations simultaneously, which also overburdens data sources.The staging area allows for the possibility to bring data together at different times, a way not to overwhelm data sources. It's usually impossible to simultaneously extract all the data from all the source systems.The use of the staging area is the following: Data pipelines themselves are a subset of the data infrastructure, the layer supporting data orchestration, management, and consumption across an organization. ETL pipelines are data pipelines that have a very specific role: extract data from its source system/database, transform it, and load it in the data warehouse, which is a centralized database. The ETL process consists of pooling data from these disparate sources to build a unique source of truth: the data warehouse. Today, organizations collect data from multiple different business source systems: Cloud applications, CRM systems, files, etc. This article seeks to bring clarity on how this process is conducted, how ETL tools have evolved, and the best tools available for your organization today. ETL plays a central role in this quest: it is the process of turning raw, messy data into clean, fresh, and reliable data from which business insights can be derived. The story is still the same: businesses have a sea of data at disposition, and making sense of this data fuels business performance. ETL (Extract-Transform-Load) is the most widespread approach to data integration, the practice of consolidating data from disparate source systems with the aim of improving access to data. ![]()
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