Data Education Center: What is File Subsetting?

File subsetting is a data management technique essential for efficiently handling large datasets by extracting only the relevant portions of data needed for specific tasks. This method is particularly useful in environments where data storage and processing resources are at a premium.

File subsetting involves selecting parts of a dataset that are directly relevant to a specific analysis or operation. This could mean extracting certain columns from a database or certain elements based on specific criteria.

The process is usually conducted in a client-server setting where the necessary data is extracted on the server side before being transmitted to the client, thereby saving bandwidth and reducing load times.

The main components of file subsetting include the criteria for selection (such as date ranges, specific variables, or key identifiers) and the tools or scripts used to extract these subsets. Tools for subsetting can vary from simple command-line utilities to more complex software solutions that integrate directly with database management systems.
 

What are the Benefits of File Subsetting?

Faster Performance

Imagine trying to analyze a multi-gigabyte file with millions of records. File subsetting allows you to create a smaller, more manageable version, leading to significant improvements in processing speed. Tasks like data cleaning, filtering, and analysis become much faster and more efficient, allowing you to extract valuable insights quicker.

Reduced Costs

Storing and managing massive datasets can incur significant costs for organizations. File subsetting helps alleviate this burden by creating smaller subsets that require less storage space. This can lead to cost savings on hardware resources and cloud storage fees.

Enhanced Data Security

During development and testing processes, it might be necessary to work with sensitive data. File subsetting allows you to create anonymized subsets that exclude sensitive information like customer names or social security numbers. This minimizes the risk of data breaches and ensures regulatory compliance.

Improved Data Quality

Subsetting can be a valuable tool in data quality initiatives. By focusing on specific subsets of data, you can identify inconsistencies and errors more efficiently. This allows for targeted data cleansing efforts, ultimately leading to more reliable and accurate data analysis.

Streamlined Collaboration

File subsetting fosters better collaboration within teams. Data analysts and developers can share specific subsets containing relevant data points, allowing everyone to work with a focused and manageable dataset. This improves communication and reduces the risk of confusion or errors.

File Subsetting vs. Database Subsetting: What's the Difference?

While file subsetting deals with extracting data from flat files, another technique known as database subsetting focuses on extracting data from relational databases. Both serve crucial purposes in data management, but they cater to different data storage formats. Here's a breakdown of the key differences:

• Data Source:

  • File subsetting works with various file formats like CSV, Excel spreadsheets, log files, and text files.

  • Database subsetting, on the other hand, is specifically designed for extracting data from structured databases like SQL Server, Oracle, or MySQL.

• Selection Criteria:

  • File subsetting typically relies on defining selection criteria based on specific values within columns of the file.

  • Database subsetting utilizes a powerful query language called SQL (Structured Query Language) to specify complex filtering conditions for data retrieval.

• Use Cases:

  • File subsetting is well-suited for tasks like data analysis, reporting, and test data management.

  • Database subsetting finds application in development, testing, data masking for security purposes, and generating specific reports from large datasets within the database.

• Advantages of Each Method:

  • File subsetting is simpler and less resource-intensive when dealing with large volumes of unstructured data. It is ideal for quick data extraction without the need for complex SQL queries.

  • Database subsetting is more suitable for complex queries that require maintaining data integrity across multiple tables and relationships. It supports more sophisticated data integrity rules and relational checks.

While both techniques offer distinct functionalities, they can also be used in conjunction. For instance, you might subset data from a flat file and then load it into a database for further analysis using database subsetting techniques.
 

The Advantages of File Subsetting

File subsetting offers a range of benefits that can significantly enhance data management processes within organizations. Here’s how adopting file subsetting can transform your data handling:

Efficiency in Data Management

By focusing only on relevant segments of data, organizations can reduce the amount of data they need to process and store. This not only speeds up data handling but also reduces infrastructure costs since less storage space is required.

Improved Performance

Developers and analysts can perform tests and analyses more quickly because they deal with smaller datasets that are easier to manage and process. This leads to faster development cycles and quicker deployment of products or updates.

Cost Reduction

With less data to store and manage, the cost of data storage can decrease significantly. Additionally, since subsetting can be automated, it also reduces the labor costs associated with data management.

Enhanced Data Security

Smaller data sets mean there’s less sensitive data at risk. Subsetting (and masking) the data can help in complying with data protection regulations by minimizing (and precluding) the exposure of sensitive information​​.

The Challenges of File Subsetting 

While file subsetting offers a multitude of benefits, it's crucial to be aware of potential challenges and implement best practices to ensure successful implementation. Here's a closer look at some key challenges and how to address them:

• Data Integrity: The subsetting process itself should not introduce errors or inconsistencies within the extracted data. It's essential to thoroughly test and validate the subsetting logic to ensure it accurately reflects the selection criteria and maintains the integrity of the extracted data points.
   

  • Mitigation: Utilize data validation tools integrated with your file subsetting solution. These tools can automatically identify potential errors or inconsistencies within the extracted data, ensuring its accuracy and reliability for further analysis.
     

• Complexity for Large Files: Defining selection criteria for very large files can become complex, especially when dealing with numerous data points and intricate filtering conditions. Managing complex logic and ensuring accurate results can be challenging.
   

  • Mitigation: Look for file subsetting solutions with intuitive user interfaces that allow for easy definition of selection criteria. Advanced features like visual query builders can simplify the process, especially when dealing with complex filtering conditions within large datasets.
     

• Version Control: Maintaining different subsets of the same file can be challenging, especially when working with multiple teams or for different purposes. Ensuring everyone has access to the correct and most recent version of the subset is crucial.
   

  • Mitigation: Utilize file subsetting solutions with integrated version control capabilities. These features allow you to track changes, revert to previous versions if needed, and ensure everyone within the organization

File Subsetting Solution

Thanks to the built-in vertical and horizontal selection filters in the IRI data manipulation and management program, SortCL, you can subset files according to quantitative or qualitative criteria in these SortCL-driven tools and for their use cases:

IRI CoSort - for high-volume sorting, data transformation, data cleansing and reporting

IRI NextForm - for data and database migration

IRI FieldShield - for masking sensitive data in RDB schema and flat files

IRI RowGen - for synthesizing realistic test data in structured (flat) and semi-structured files

IRI Voracity - for all of the above, plus ETL, pivoting, and more

The IRI Workbench job design IDE, a graphical user interface built on Eclipse, is the common front end for these tools, and as such addresses the aforementioned subsetting challenges; i.e.,

• Data Integrity - contains built-in data profiling modules and data validation functions

• Complexity for Large Files - front-ends conditions in graphical dialogs and color-coded, syntax aware SortCL code editor

• Version Control - integration with Git repositories allow you to manage and track changes to the SortCL programs and subsets as needed

If you are interested in producing subset data from files, email info@iri.com with your requirements. 

If you are interested in learning more about database subsetting, see this article, and for more information on data subsetting in general, see this article.

See Also:

• What is Database Subsetting?

• What is Subsetting?