Start with a representative workload
Use this guide to prepare a Qlik to Databricks 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
- Include load scripts, variables, include files, and QVD dependencies.
- Sample resident loads, preceding loads, mapping tables, and join/concatenate behavior.
- Identify business logic embedded in chart expressions separately from the data-load pipeline.
Plan the Databricks implementation
Agree the split between Spark SQL and PySpark, job orchestration, and table ownership. Check joins, partitioning, data types, and cluster/runtime assumptions with representative workloads.
Agree what a passing result means
- Fix the baseline. Use the same input snapshot and record source parameters and expected outputs.
- Compare the data. Check row counts, keys, duplicates, nulls, aggregates, and row-level values with agreed precision tolerances.
- Review exceptions. Keep a list of behavior that needs manual work, an owner, and a repeatable test.
- 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.