Qlik to Snowflake

Architecture

Qlik in. Snowflake out.

Deterministic parsers read the estate and emit native Snowflake code — not Qlik apps re-pointed at a cloud warehouse.

Qlik → MigryX parser → SQL + Snowpark + Tasks

Qlik

Sense apps.qvf / .qvw

Load script.qvs includes

ApplyMapLookup + join

QVD storeIntermediate files

MigryX Parser

Deterministic parseAI optional

Row-level parityBefore cutover

SQL emitSet-based procedures

Snowpark emitWhere SQL is not enough

Snowflake

Snowflake SQLDDL from schemas

SnowparkPython DataFrames

TasksReplaces reload

StreamsChange data

Git + cloneDev -> prod

RBAC + tagsColumn security

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 Snowflake

Qlik load scripts were not built for cloud-native warehousing

ApplyMap belongs in SQL, not the associative engine

Qlik mapping tables and JOIN/RESIDENT loads run inside a single-node engine. Snowflake pushes joins, filters, and aggregations into its massively parallel SQL engine. Lookups that reload an app finish as SQL on the warehouse.

QVD stores are not a warehouse stage

STORE ... INTO *.qvd writes a proprietary file. Snowflake COPY INTO and MERGE load from stages at warehouse speed. MigryX turns each STORE into a table write so the QVD tree is not rehosted.

SET / LET are fragile configuration

Qlik script variables manage environment-specific paths and thresholds inside the app. Snowflake session variables, account parameters, and Snowpark configuration provide native, auditable environment management with role-based access control.

Parser output

ApplyMap + JOIN to Snowflake CTE + MERGE

A Qlik load script with a mapping load and conditional segmentation — converted to a Snowflake CTE pipeline with MERGE for upsert logic. No Qlik engine, no QVD hop.

Qlik load script (.qvs)

// App: Customer_Revenue_Update
SET vMinAmount = 1000;

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

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

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

Snowflake SQL

-- ApplyMap + RESIDENT → Snowflake SQL
WITH segmented AS (
    SELECT
        t.cust_id,
        CASE
            WHEN t.total_spend > 50000 THEN 'Platinum'
            WHEN t.total_spend > 10000 THEN 'Gold'
            ELSE COALESCE(c.tier_code, 'Standard')
        END AS segment,
        t.amount
    FROM customer_transactions t
    LEFT JOIN customer_master c
        ON t.cust_id = c.cust_id
    WHERE t.amount > 1000
),
agg AS (
    SELECT
        segment,
        SUM(amount) AS segment_revenue,
        COUNT(*) AS customer_count
    FROM segmented
    GROUP BY segment
)
MERGE INTO customer_segments tgt
USING agg src
    ON tgt.segment = src.segment
WHEN MATCHED THEN UPDATE SET
    tgt.segment_revenue = src.segment_revenue,
    tgt.customer_count = src.customer_count
WHEN NOT MATCHED THEN INSERT
    (segment, segment_revenue, customer_count)
    VALUES (src.segment, src.segment_revenue,
            src.customer_count);

ApplyMap becomes LEFT JOIN + COALESCE. Nested if() becomes CASE WHEN. STORE to QVD becomes MERGE instead of another proprietary file.

Coverage