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In contemporary data management, efficient search and retrieval of information from large datasets is crucial. PostgreSQL, while primarily a structured data management system, offers robust tools for handling unstructured or semi-structured data through its built-in full-text search (FTS) capabilities and extensions like pg_trgm and pg_bigm. Traditional SQL queries using LIKE and ILIKE operators are effective for precise textual matching but may struggle with extensive unstructured text. Migrating full-text search from SQL Server to Amazon Aurora PostgreSQL-Compatible Edition requires adjustments to queries and schema structures, as the implementations differ. The AWS Schema Conversion Tool does not automatically convert full-text search code. SQL Server's FTS is designed for searching specific words or phrases within unstructured text, allowing rapid searching, ranking, and indexing. To enable FTS in SQL Server, one must enable it for the database, create a full-text catalog, and define a full-text index on relevant text columns. Migration to PostgreSQL involves using AWS SCT and AWS DMS. PostgreSQL supports various text searching methods, including exact search, pattern matching, regular expressions, and FTS, utilizing tsvector and tsquery data types. PostgreSQL's FTS allows for advanced querying capabilities, including ranking search results based on relevance using the ts_rank function. The ts_headline function can summarize documents based on search queries. Performance can be improved in PostgreSQL through the use of GIN indexes and stored generated columns. The pg_trgm extension enhances text search by leveraging trigrams, allowing for efficient similarity operations on text columns.