Migrating from SAS to Cloudera Data Platform (CDP) requires transitioning proprietary SAS syntax into distributed, open-source big data frameworks. This involves rewriting legacy PROC SQL into Apache Hive or Impala SQL, and converting DATA Steps and Macros into Apache Spark (using PySpark or Spark SQL) for massively parallel processing
Datasets to Parquet/ORC: Move legacy SAS .sas7bdat files into Apache Parquet or ORC formats via Apache Sqoop or modern CDP Data Ingestion tools. These formats yield better compression and faster query performance.
Data Steps to PySpark: Translate row-by-row SAS logical operations and merges into resilient distributed datasets (RDDs) and DataFrames in Spark.
Macros to Orchestrated Pipelines: Convert dynamic parameter handling into Apache Airflow or Oozie workflows executing templated Spark/SQL jobs, replacing proprietary macro loops.
PROC SQL to Impala/Hive: Map SAS SQL syntax (e.g., standard joins, aggregations, and extracts) into Hive/Impala queries. Use CDP Workload Manager to analyze existing workload execution paths and optimize query performance before executing.
Target Cloudera Engines
Apache Hive & Impala: Best for standard data warehousing, BI reporting, and ad-hoc analytics replacing interactive PROC SQL queries.
Apache Spark: Handles complex data transformations, machine learning, and heavy ETL formerly done by intensive SAS DATA Steps.
Apache Iceberg: Recommended for table formats, allowing you to perform row-level updates and time-travel queries within CDP.
Governance and Security
Instead of managing metadata in isolated SAS environments, you should use Apache Atlas to track data lineage and Apache Ranger to establish fine-grained, column-level security policies and role-based access controls across your migrated datasets.