LitGrades VS SQL query for CSV

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SQL query for CSV helps people analyze large sets of csv records in a short time using declarative SQL syntax.

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Familiarity
SQL syntax is widely used and familiar to many developers, making it easier for them to query CSV data without learning a new query language.
Expressiveness
SQL is a powerful language with a rich set of features, allowing complex queries, joins, and aggregations on CSV data.
Ad-hoc Analysis
Users can perform quick and efficient ad-hoc data analysis directly on CSV files without needing to import the data into a database.
Accessibility
Online tools like try.csv-query.ca make it easy to query CSV files directly from the browser, increasing accessibility for users.

Performance Limitations
Handling large CSV files in memory might lead to performance issues, as CSV files are not optimized for querying compared to databases.
Limited Data Integrity
CSV files lack data types and constraints, which can lead to errors or unexpected results when querying data, compared to structured databases.
Lack of Security
CSV files can be easily modified or corrupted, and querying them directly may expose sensitive data if not properly managed.
Resource Intensive
Running complex queries on large CSV files can be resource-intensive and may not be suitable for environments with limited computational resources.