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Data Profiling: Definition, Tasks, Use Cases, and Challenges

data profiling data analysis data management data quality data integration

This page covers the definition of data profiling, classification of data profiling tasks, use cases, and challenges associated with data profiling.

Data Profiling Definition

Data profiling is the process of examining a large database and collecting informative summaries, often in the form of a smaller, more manageable database.

Data Profiling

Data Profiling Tasks

Data profiling involves several key tasks:

  • Data Examination and Statistics Collection: Examining data within an existing data source and gathering statistics and information about it.
  • Data Transformation: Converting large datasets into smaller, more informative subsets.
  • Metadata Collection: Collecting metadata to support effective data management.
  • Column Information Generation: Providing detailed information about columns and column sets.

Figure 1 (not included here but assumed to exist) depicts these various data profiling tasks.

Data Profiling Use Cases

Data profiling is valuable in various scenarios:

  • Query Optimization: Counting and generating histograms to improve query performance.
  • Data Cleansing: Removing duplicate patterns and addressing any data violations to improve data quality.
  • Data Integration: Identifying and managing cross-database inclusion dependencies to facilitate seamless integration.
  • Scientific Data Management: Handling new and complex scientific datasets effectively.
  • Data Analytics and Data Mining: Preparing data for effective data analysis and mining.

Data Profiling Challenges

Data profiling presents several challenges:

  • Computational Complexity: Managing the complexity arising from the number of rows (sorting, hashing), columns, and combinations.
  • Large Space Requirements: Dealing with the significant storage space needed for profiling operations.
  • Handling New Data Types and Models: Adapting to data types beyond strings and numbers, and data models beyond relational ones.
  • Evolving Requirements: Meeting new requirements such as user-oriented profiling and streaming data analysis.