![]() ![]() For others, the motive may be the desire to consolidate databases after a merger or acquisition. For some, the need will arise when it’s time to upgrade legacy databases or transition to cloud-native storage. Benefits of Using an Extraction ToolĬompanies and organizations in virtually every industry and sector will need to extract data at some point. In other words, the code itself may have to be rebuilt from scratch each time an extraction takes place. Hand-coding can be a painstaking process that is prone to errors and difficult to replicate across multiple extractions. If you’re planning to move data from a legacy databases into a newer or cloud-native system, you’ll be better off extracting your data with a complete data integration tool.Īnother consequence of extracting data as a stand alone process will be sacrificing efficiency, especially if you’re planning to execute the extraction manually. As a result, the data may be useful for archival purposes, but little else. Raw data which is extracted but not transformed or loaded properly will likely be difficult to organize or analyze, and may be incompatible with newer programs and applications. However, it’s important to keep in mind the limitations of data extraction outside of a more complete data integration process. Data Extraction without ETLĬan data extraction take place outside of ETL? The short answer is yes. But without a way to migrate and merge all of that data, it’s potential may be limited. Similarly, retailers such as Office Depot may able to collect customer information through mobile apps, websites, and in-store transactions. Data extraction was made it possible to consolidate and integrate data related to patient care, healthcare providers, and insurance claims. For example, GE Healthcare needed to pull many types of data from a range of local and cloud-native sources in order to streamline processes and support compliance efforts. The ETL process is used by companies and organizations in virtually every industry for many purposes. Loading: The transformed, high quality data is then delivered to a single, unified target location for storage and analysis. ![]() For example, duplicate entries will be deleted, missing values removed or enriched, and audits will be performed to produce data that is reliable, consistent, and usable. During the transformation phase, data is sorted, organized, and cleansed.
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