Examples

Examples#

Basic Example#

from wxdi.data_product_recommender.platforms import SnowflakeQueryParser
from wxdi.data_product_recommender.recommender import DataProductRecommender

# Initialize with Snowflake parser
parser = SnowflakeQueryParser()
recommender = DataProductRecommender(parser)

# Load and analyze query logs
recommender.load_query_logs_from_csv_file('query_logs.csv')
recommender.calculate_metrics()

# Get top 20 recommendations
recommendations = recommender.recommend_data_products(num_recommendations=20)

# Export to Markdown
recommender.export_recommendations_markdown(recommendations, 'output/recommendations.md')

# Export to JSON
recommender.export_recommendations_json(recommendations, 'output/recommendations.json')

Multi-Platform Example#

from wxdi.data_product_recommender.platforms import (
    SnowflakeQueryParser,
    DatabricksQueryParser,
    BigQueryQueryParser
)
from wxdi.data_product_recommender.recommender import DataProductRecommender

# Snowflake
snowflake_parser = SnowflakeQueryParser()
snowflake_recommender = DataProductRecommender(snowflake_parser)
snowflake_recommender.load_query_logs_from_csv_file('snowflake_logs.csv')

# Databricks
databricks_parser = DatabricksQueryParser()
databricks_recommender = DataProductRecommender(databricks_parser)
databricks_recommender.load_query_logs_from_csv_file('databricks_logs.csv')

# BigQuery
bigquery_parser = BigQueryQueryParser()
bigquery_recommender = DataProductRecommender(bigquery_parser)
bigquery_recommender.load_query_logs_from_csv_file('bigquery_logs.csv')

Custom Scoring Weights#

from wxdi.data_product_recommender.platforms import SnowflakeQueryParser
from wxdi.data_product_recommender.recommender import DataProductRecommender

parser = SnowflakeQueryParser()
recommender = DataProductRecommender(parser)

# Customize scoring weights
recommender.weights = {
    'query_count': 0.5,      # Emphasize query volume
    'user_diversity': 0.3,   # Moderate user diversity
    'recency': 0.1,          # Less emphasis on recency
    'consistency': 0.1       # Less emphasis on consistency
}

recommender.load_query_logs_from_csv_file('query_logs.csv')
recommender.calculate_metrics()
recommendations = recommender.recommend_data_products(num_recommendations=20)

See Also#