Oracle PL/SQL to Databricks

Architecture

Oracle PL/SQL in. Databricks out.

Deterministic parsers read the estate and emit native Databricks code — not PL/SQL wrapped in a compatibility layer.

Oracle PL/SQL → MigryX parser → PySpark + Delta + Workflows

Oracle PL/SQL

PackagesProcs + funcs

Cursor loopsRow-by-row

CONNECT BYHierarchies

BULK COLLECT%ROWTYPE

MigryX Parser

Deterministic parseAI where it helps

Row-level parityBefore cutover

PySpark emitSet-based, not loops

Workflow emitJob DAGs

Databricks

PySpark notebooksSet-based, not loops

Delta LakeACID + time travel

WorkflowsReplaces scheduler

Unity CatalogColumn lineage

Repos + DABsCI/CD promote

MLflowWhere models live

MigryX AI handles the logic parsers cannot resolve alone, and every change it makes goes through the same parity checks. It runs on a model you approve, air-gapped if your estate requires it.

Why Databricks

Oracle PL/SQL was built for a different era

PL/SQL packages become Python modules

Oracle packages bundle specification and body into monolithic database objects. MigryX decomposes them into clean Python modules with clear class structure, public APIs preserved. 350 packages decomposed across a single engagement.

CONNECT BY hierarchies become recursive CTEs and PySpark

Oracle's proprietary START WITH...CONNECT BY PRIOR syntax has no direct Spark equivalent. MigryX converts hierarchical queries to recursive CTEs in Spark SQL or equivalent PySpark GraphFrames logic, preserving LEVEL, SYS_CONNECT_BY_PATH, and CONNECT_BY_ROOT.

BULK COLLECT becomes Spark parallel processing

Oracle's BULK COLLECT / FORALL pattern fetches rows into PL/SQL collections for batch DML. On Databricks, this maps naturally to DataFrame batch operations that distribute across the cluster. Row-by-row cursor loops become set-based transformations.

Parser output

PL/SQL procedure with cursors to PySpark DataFrames

A PL/SQL procedure using explicit cursors and BULK COLLECT for batch processing -- converted to native PySpark DataFrame operations on Databricks. No row-by-row processing, no single-node bottleneck.

Oracle PL/SQL

-- Procedure: process_customer_segments
CREATE OR REPLACE PROCEDURE process_segments AS
  TYPE t_cust IS TABLE OF customers%ROWTYPE;
  l_custs t_cust;
  CURSOR c_active IS
    SELECT * FROM customers
    WHERE status = 'ACTIVE'
      AND total_spend > 1000;
BEGIN
  OPEN c_active;
  LOOP
    FETCH c_active BULK COLLECT
      INTO l_custs LIMIT 5000;
    EXIT WHEN l_custs.COUNT = 0;

    FORALL i IN 1..l_custs.COUNT
      INSERT INTO customer_segments VALUES (
        l_custs(i).cust_id,
        DECODE(l_custs(i).total_spend,
          NULL, 'Unknown',
          'Platinum'),
        NVL(l_custs(i).region, 'Unassigned'),
        SYSDATE
      );
    COMMIT;
  END LOOP;
  CLOSE c_active;
END;

→

MigryX
converts

PySpark on Databricks

# PL/SQL procedure → PySpark on Databricks
from pyspark.sql import functions as F

def process_segments():
    df = spark.read.table("customers")

    segmented = (
        df.filter(
            (F.col("status") == "ACTIVE") &
            (F.col("total_spend") > 1000)
        )
        .withColumn("segment",
            F.when(F.col("total_spend").isNull(),
                   "Unknown")
             .otherwise("Platinum"))
        .withColumn("region",
            F.coalesce(F.col("region"),
                       F.lit("Unassigned")))
        .withColumn("processed_date",
            F.current_date())
        .select("cust_id", "segment",
                "region", "processed_date")
    )

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

process_segments()

Cursor loop with BULK COLLECT replaced by distributed DataFrame operations. DECODE mapped to F.when().otherwise(). NVL mapped to F.coalesce(). SYSDATE mapped to F.current_date(). Output writes to Delta Lake with ACID guarantees.

Coverage