Understanding the difference between data and table
In the realm of data management, a fundamental principle revolves around the clear distinction between raw data and the higher-level abstraction known as tables. The actual underlying data, residing within files, constitutes the raw information in its most unprocessed form. These data files may be stored in various formats, such as CSV, Parquet, or JSON, and contain the granular details, records, or values that form the basis of analysis and processing.
On the other hand, tables serve as a sophisticated layer of abstraction, providing a structured and organized representation of the underlying data. Unlike the data files themselves, tables introduce the concept of metadata — a set of information about the structure and properties of the data rather than the data itself. This metadata encompasses crucial details such as column names, data types, constraints, and other attributes that define the schema of the data. The image below illustrates the distinction between data files and tables, and how they related to each other.
Table abstractions streamline data management by encapsulating metadata, providing a logical structure for efficient querying, analysis, and manipulation. Separating raw data from metadata empowers users to interact at a higher level, enhancing organization and comprehension of complex datasets. A key challenge addressed is the O(n) complexity of looking up directories for each partition in raw file formats. Table formats, like Iceberg, mitigate this by incorporating metadata, reducing complexity and improving efficiency.
Open table formats, exemplified by Iceberg, extend table capabilities with features such as schema evolution and transactional support. They contribute to standardization, fostering seamless and scalable data management across processing frameworks.
The table format’s abstraction facilitates easier analytics, freeing users from intricate file details. Users can query data directly, trusting the table format to manage complexities, simplifying interactions, and enhancing the overall user experience in data analysis.