Fast Full-Text Search: VectorDB + Apache Iceberg

Customizing the Script Table and Output Paths: Adjust the table_path and output_path variables to point to your actual data lake paths. Transformations: Customize the transform_data() function to apply specific transformations relevant to your dataset. Data Format: Change the data format in load_data() and save_data() functions if you are working with different formats. This modular approach allows you to extend and adapt the script for more complex data transformations and processing tasks, making it a powerful tool for unfreezing and analyzing enterprise data locked in lakehouses. Would you also like the code in Mathematica?

Explanation Initialize Spark Session: initialize_spark(): Initializes a Spark session with a specified application name. Load Data from Iceberg Tables: load_data(): Loads data from the specified Iceberg table path using PySpark. Transform Data: transform_data(): Applies data transformations, such as filling null values, adding new columns, and computing new values based on existing columns. Save Transformed Data: save_data(): Saves the transformed data to the specified output path in Parquet format. Main Function: main(): Orchestrates the process by calling the initialization, data loading, transformation, and saving functions sequentially.

Part 2 # Save Transformed Data def save_data(df, output_path): ??df.write.format("parquet").mode("overwrite").save(output_path) # Main Function def main(table_path, output_path): ??# Initialize Spark ??spark = initialize_spark() ??? ??# Load Data ??df = load_data(spark, table_path) ??? ??# Transform Data ??df_transformed = transform_data(df) ??? ??# Save Transformed Data ??save_data(df_transformed, output_path) ??? ??# Stop Spark Session ??spark.stop() # Define Paths table_path = "s3://your-datalake-path/iceberg-table" output_path = "s3://your-datalake-path/transformed-data" # Execute Main Function if __name__ == "__main__": ??main(table_path, output_path)

Part 1 from pyspark.sql import SparkSession from pyspark.sql.functions import col, when, lit from pyspark.sql.types import * # Initialize Spark Session def initialize_spark(app_name="UnfreezeDataLake"): ??spark = SparkSession.builder \ ????.appName(app_name) \ ????.getOrCreate() ??return spark # Load Data from Iceberg Tables def load_data(spark, table_path): ??df = spark.read.format("iceberg").load(table_path) ??return df # Transform Data def transform_data(df): ??# Example transformation: Fill null values, add computed columns, etc. ??df_transformed = df.fillna({'column1': 0, 'column2': 'unknown'}) \ ????.withColumn('new_column', col('column1') * 2) \ ????.withColumn('status', when(col('column1') > 0, lit('active')).otherwise(lit('inactive'))) ??return df_transformed

To address the problem of frozen data in data lakes and iceberg tables, we can develop a Python script using modular formulas and efficient data processing techniques. Here’s a Python code example using the PySpark library, which is commonly used for processing large datasets in data lakes. We will employ modular functions to structure the code for readability and maintainability.