What is Data Extraction?
Data extraction is the process of collecting or retrieving disparate types of data from a variety of sources, many of which may be poorly organized or completely unstructured.?Data extraction?makes it possible to consolidate,?process, and refine data so that it can be stored in a centralized location in order to be transformed. These locations may be on-site, cloud-based, or a hybrid of the two.
Data extraction is the first step in both?ETL (extract, transform, load)?and?ELT (extract, load, transform)?processes. ETL/ELT are themselves part of a complete?data integration?strategy.
Data Extraction and ETL
To put the importance of data extraction in context, it’s helpful to briefly consider the ETL process as a whole. In essence, ETL allows companies and organizations to 1) consolidate data from different sources into a centralized location and 2) assimilate different types of data into a common format. There are three steps in the ETL process:
The ETL process is used by companies and organizations in virtually every industry for many purposes. 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. Data extraction was made it possible to consolidate and integrate data related to patient care, healthcare providers, and insurance claims.
Similarly, retailers such as?Office Depot?may able to collect customer information through mobile apps, websites, and in-store transactions. But without a way to migrate and merge all of that data, it’s potential may be limited. Here again, data extraction is the key.
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Data Extraction without ETL
Can data extraction take place outside of ETL? The short answer is yes. However, it’s important to keep in mind the limitations of data extraction outside of a more complete data integration process. 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. As a result, the data may be useful for archival purposes, but little else. 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.
Another consequence of extracting data as a stand alone process will be sacrificing efficiency, especially if you’re planning to execute the extraction manually.?Hand-coding?can be a painstaking process that is prone to errors and difficult to replicate across multiple extractions. In other words, the code itself may have to be rebuilt from scratch each time an extraction takes place.
Benefits of Using an Extraction Tool
Companies and organizations in virtually every industry and sector will need to extract data at some point. For some, the need will arise when it’s time to upgrade legacy databases or transition to?cloud-native storage. For others, the motive may be the desire to consolidate databases after a merger or acquisition. It’s also common for companies to want to streamline internal processes by merging data sources from different divisions or departments.
If the prospect of extracting data sounds like a daunting task, it doesn’t have to be. In fact, most companies and organizations now take advantage of data extraction tools to manage the extraction process from end-to-end. Using an ETL tool automates and simplifies the extraction process so that resources can be deployed toward other priorities. The benefits of using a data extraction tool include: