Data extraction is the first step of nearly every data-driven process, from business analytics to cybersecurity. Whether retrieving data from a database source or capturing key data in a forensic investigation, data extraction plays a crucial role in locating, processing, and storing relevant information within data-driven applications.
In this simple guide, we’ll explain data extraction processes, show how to perform them, and recommend the best tools for different organizations and applications.
What Is Data Extraction?
As its name describes, data extraction is the process of extracting data from a data source, such as a cloud server or a piece of physical hardware. Though the exact processes used often vary between different applications and tools, they all share the same basic concepts and data extraction techniques.
Data extraction transfers raw data from a source to a targeted destination, such as a data warehouse, for processing and transformation. This allows enterprises to use the data in applications, reporting, and various forms of analysis. Data extraction also serves as the first step in the “extract, transform, and load” (ETL) process for data ingestion.
Luckily, you don’t need a computer science degree to perform data extraction since many new software tools have made the process straightforward and able to perform rapidly.
How to Perform Data Extraction
No matter the data source, the general process for extracting data usually comes down to the following three steps—most just prep work!
- Verify existing data structures by checking for structural changes to your data, such as new tables or columns added to a database.
- Identify target data by selecting the parts of the data, such as specific fields or tables, that you want to extract.
- Extract the data.
Though that last “extract the data” step may sound vague, it’s only because it’s typically the most straightforward part of the extraction process. In fact, it’s really no different than simply “selecting” data according to your chosen targets. The data migration processes responsible for transferring and loading the selected information comprise the “T” and “L” steps of “ETL” and fall outside of the actual data extraction process.
Data Extraction Techniques
When it comes to extracting data from a source, you have several data extraction techniques to choose from. These techniques are typically grouped into one of two major categories.
Logical Data Extraction Techniques
Logical data extraction involves extracting data through a software. In other words, logical extraction doesn’t necessarily require a physical connection between devices and instead operates entirely through software.
If you’re performing data extraction through a database and/or software, you’re probably utilizing some form of logical data extraction. There are three types of logical data extraction, depending on how you plan to extract source data:
- Update Notification Extraction: A source system issues an update notification whenever changes occur, providing a simple way to implement data extraction. Most databases and software-as-a-service (SaaS) applications include this functionality, often using webhooks.
- Incremental Extraction: If a source system can’t produce update notifications or similar, incremental extraction regularly provides an alternative by regularly checking for updates and extracting any new data. However, since this technique doesn’t produce “live” update notifications, it risks missing out on intermittent changes, such as deleted files, between checks.
- Full Extraction: If a source system can’t produce update notifications or check for changes, full extraction—extracting all the data from a source system every time—may be necessary. However, this technique should be avoided since it can place a massive load on the network.
Physical Data Extraction Techniques
Physical data extraction involves making bit-by-bit copies of a hard drive or some other data storage device. There are two types of physical data extraction:
- Online Extraction: Data is extracted directly from the source.
- Offline Extraction: Data is extracted indirectly from the source through an external medium. Here, the external medium usually saves a copy of the source and produces the copied data in the form of Flat (generic format) files or database-specific dump files.
Data Extraction Tools
Traditionally, data extraction—and much of the ETL process—was done through custom-coded scripts and programs. Though custom coding can deliver great results, increased complexity and rising demand for ease of access have given way to simpler solutions.
Thankfully, simplicity hasn’t come at the cost of robustness; if anything, modern data extraction tools are even more robust than traditional methods, with many being capable of automatically connecting to a wide range of data sources and APIs. Compare this capability to having to custom-code a connection to each and every source—the benefit is clear.
In summary, ETL and data extraction tools do most of the work for you without having to maintain code and keep track of updates. Though many data sources and management systems include these tools, companies with specific data needs often prefer custom data software solutions.
The Ciphertex® Advantage
Securely handling data requires secure solutions. From secure data storage to extraction and transportation, the Ciphertex Data Security® team has met the data security and storage needs of numerous organizations across a wide range of industries. To learn more about how we can help you develop better data solutions, call our sales team at 818-773-8989.