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Migration guide

Qlik modernization overview

Prepare the code, target decisions, and validation criteria for Qlik modernization overview.

Start with a representative workload

Use this guide to prepare a Qlik modernization overview assessment. The first decision is whether the intended target preserves the behavior your business relies on, at an acceptable operating cost.

What to collect from Qlik

Choose the target deliberately

Select the execution engine, data layout, and scheduler around the workload. Use representative volumes and operating constraints to compare candidate targets before converting the wider estate.

Agree what a passing result means

  1. Fix the baseline. Use the same input snapshot and record source parameters and expected outputs.
  2. Compare the data. Check row counts, keys, duplicates, nulls, aggregates, and row-level values with agreed precision tolerances.
  3. Review exceptions. Keep a list of behavior that needs manual work, an owner, and a repeatable test.
  4. Check operations. Measure runtime and cost, rehearse retries and recovery, and confirm who owns the production job.

What to decide after the pilot

Review generated code, test results, unresolved exceptions, and measured delivery effort together. Expand only after the sample meets your acceptance criteria. Agree whether your team leads the next wave, needs engineering support, or wants managed delivery.

Review a sample from your estate

Request a free assessment to agree a representative sample and target. We will follow up with the code-transfer steps; this is not an instant upload.

Explore other modernizations

Targets: Snowflake Databricks Google Cloud Azure AWS PySpark Polars Iceberg DBT SQLMesh
Sources: SAS Alteryx Talend Qlik DataStage Informatica COBOL Oracle Teradata SSIS