Qlik to Databricks

Architecture

Qlik in. Databricks out.

Deterministic parsers read the estate and emit native Databricks code — not Qlik apps re-pointed at a lakehouse.

Qlik → MigryX parser → PySpark + Delta + Workflows

Qlik

Sense apps.qvf / .qvw

Load script.qvs includes

ApplyMapLookup + join

QVD storeIntermediate files

MigryX Parser

Deterministic parseAI optional

Row-level parityBefore cutover

PySpark emitSet-based, not loops

Workflow emitJob DAGs

Databricks

PySpark notebooksSet-based, not loops

Delta LakeACID + time travel

WorkflowsReplaces reload

Unity CatalogColumn lineage

Repos + DABsCI/CD promote

MLflowWhere models live

AI is an optional add-on, off by default — the conversion runs end to end without it, air-gapped if your estate requires it.

Why Databricks

Qlik load scripts were not built for lakehouse-scale analytics

ApplyMap and RESIDENT stay trapped in the engine

Qlik mapping tables and RESIDENT loads run inside the associative engine. MigryX parses every ApplyMap, JOIN, and RESIDENT structurally and converts them to native PySpark DataFrame operations that run distributed across the cluster.

QVD is a dead end on Databricks

STORE ... INTO *.qvd writes a proprietary file the lakehouse cannot read. Databricks writes Delta. MigryX turns each STORE into a table write and each QVD LOAD into a table read so the intermediate layer is gone.

Reload chains lack modern orchestration

Qlik reload tasks chain apps linearly. Databricks Workflows provide DAG-based orchestration, parameterized runs, retry policies, and event-driven triggers natively.

Parser output

ApplyMap + JOIN to native PySpark

A Qlik load script with a mapping load, a derived column, and a left join — converted to PySpark DataFrame joins and withColumn transformations. No Qlik engine, no QVD hop.

Qlik load script (.qvs)

// App: Customer_Revenue_Segmentation
SET vMinAmount = 1000;

Tiers:
MAPPING LOAD CUST_ID, TIER_CODE
FROM [lib://QVD/customer_master.qvd] (qvd);

Txns:
LOAD
    CUST_ID,
    AMOUNT,
    ApplyMap('Tiers', CUST_ID, 'Standard') as SEGMENT
FROM [lib://QVD/transactions.qvd] (qvd)
WHERE AMOUNT > $(vMinAmount);

LEFT JOIN (Txns)
LOAD CUST_ID, TOTAL_SPEND
FROM [lib://QVD/customer_spend.qvd] (qvd);

Seg:
LOAD
    if(TOTAL_SPEND > 50000, 'Platinum',
      if(TOTAL_SPEND > 10000, 'Gold', SEGMENT)) as SEGMENT,
    Sum(AMOUNT) as SEGMENT_REVENUE,
    Count(CUST_ID) as CUSTOMER_COUNT
RESIDENT Txns
GROUP BY SEGMENT, TOTAL_SPEND;

STORE Seg INTO [lib://QVD/customer_segments.qvd] (qvd);

→

MigryX
converts

PySpark on Databricks

# ApplyMap + JOIN + STORE → PySpark
from pyspark.sql import functions as F

txns = spark.read.table("transactions") \
    .filter(F.col("amount") > 1000)
tiers = spark.read.table("customer_master") \
    .select("cust_id", "tier_code")
spend = spark.read.table("customer_spend")

segmented = (
    txns.join(tiers, "cust_id", "left")
    .join(spend, "cust_id", "left")
    .withColumn("segment",
        F.when(F.col("total_spend") > 50000, "Platinum")
         .when(F.col("total_spend") > 10000, "Gold")
         .otherwise(F.coalesce("tier_code", F.lit("Standard"))))
    .groupBy("segment")
    .agg(
        F.sum("amount").alias("segment_revenue"),
        F.count("*").alias("customer_count")
    )
)

segmented.write.format("delta").mode("overwrite") \
    .saveAsTable("customer_segments")

ApplyMap becomes a left join with coalesce. Nested if() becomes .when(). STORE to QVD becomes a Delta write with ACID guarantees.

Coverage