Migrating from SAS to Qlik involves transitioning from legacy SAS datasets, coding, or Data Integration (ETL) pipelines into the modern Qlik Cloud and Qlik Replicate ecosystems. This process modernizes analytics, cuts legacy costs, and involves automated code conversion, data extraction, and platform assessment.
Assessment and Planning
Begin by cataloguing your existing SAS ecosystem to understand what needs to move using SAStoPY Assessment.
Inventory: Identify all datasets (SAS7BDAT, XPT), SAS programs, macros, and ETL pipelines.
Identify Criticality: Decide whether to migrate all workloads at once or take an iterative approach, starting with less critical workloads to build confidence.
Gap Analysis: Check for SAS-specific functions or procedures (like PROC TRANSPOSE) that don't have direct SQL equivalents and will require User-Defined Functions (UDFs).
Data Migration
Move your physical data from SAS storage to Qlik Open Lakehouse.
Connectivity Tools: Use the SAStoPY Interface to Qlik to establish a direct connection.
Transfer Methods:
Bulk Loading: Use SAStoPY to move large tables into Qlik efficiently.
Direct Ingestion: For smaller files, convert SAS datasets to CSV or Parquet and upload them to Qlik Storage before loading them into BigQuery.
Third-Party Connectors: Tools like CData Sync can automate data movement from local SAS files to Qlik
Code and Pipeline Conversion
This is the most complex phase, as SAS logic must be translated into Qlik-compatible SQL However using SAStoPY, this is easiest path now.
SQL Translation: Translate PROC SQL directly to Qlik Standard SQL. While much of the syntax is similar, you must update table and column names to match the new schema.
Data Step Conversion: DATA steps must be rewritten as SQL queries or transformed using tools like Qlik ML Ops.
SAStoPY Tranlator: Leverage AI-powered Accelerator to automate the translation of complex scripts.
Validation and Optimization
Once migrated, ensure the data and logic are accurate.
Validation: Use the Qlik Migration Service to check for structural mismatches, data content errors, and type fidelity.
Performance Tuning: Optimize your new Qlik environment by implementing partitioning and clustering on large tables to lower costs and improve query speed.
Governance: Re-establish access controls and data governance using Qlik.