Schema Design

Key Considerations:

• Field Standardization:Define a set of standard fields that every grant listing should have in the Elasticsearch index, such as title, description, eligibility_criteria, application_deadline, start_date, category, and provider.
• For dates and numerical fields, specify the format (e.g., ISO 8601 for dates) to ensure consistency.
• Data Types and Analysis:Choose appropriate data types for each field in Elasticsearch (e.g., text for full-text fields, keyword for exact matches, date for dates).
• Configure analysis settings for text fields to include standard analyzers, custom tokenizers, or filters to handle synonyms, stop words, and text normalization.
• Handling Optional and Variable Content:Use nested objects or arrays for fields that can have multiple values (e.g., multiple eligibility criteria or categories).
• Consider dynamic fields or a flexible "tags" field for capturing additional information that doesn't fit neatly into the standard schema.
• Search Optimization:Design the schema with search use cases in mind, considering which fields should be searchable and whether any fields should be prioritized in search relevance scoring.
• Implement multi-field definitions for important text fields to support both full-text search and keyword matching (e.g., using fields in Elasticsearch to index a text field as both text and keyword).

Implementation:

• Use Elasticsearch mappings to define the schema, specifying field names, types, and analysis settings.
• Test the schema with a subset of the data to ensure that it supports the desired search functionality and adjust as needed based on test results.

Documentation:

• Document the schema design, including the rationale for field selections and configurations, to guide data normalization and indexing efforts.