Migrating to DBR 15.3+: Replacing Old PySpark Functions with New
Requirement
Introductions: Several PySpark functions have been updated, deprecated, or replaced with improved alternatives. These changes aim to enhance performance, optimize data processing, and align with the latest advancements Staying up-to-date with these function changes is essential to maintain efficient and scalable data pipelines. Legacy functions may no longer be supported, leading to compatibility issues, performance bottlenecks, or incorrect results. Dealing with medical sales data, HCP interactions, or Life science analytics, upgrading to the latest PySpark functions will improve reliability and efficiency.
Requirements: Upgrade the following PySpark functions from older DBR versions to DBR 15.3+, ensuring optimized execution in Databricks. Use the datasets 'purgo_playground.dq_datasets'.
|Functions|
|substr(col, start, length)|
|df.collect()|
|dropDuplicates()|
|df.registerTempTable("table")|
Replace substr() the column "customer_name" as "short_customer_name" with length (1, 5).
Replace collect() with showing of 10 records.
Replace dropDuplicates() for the column "hcp_id".
Replace registerTempTable() for the column “hcp_interactions”.
Final Output: Show the output in Pyspark code with above DBR 15.3+
Unity Catlog details: purgo_playground.dq_datasets