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    Lab 12: Legacy to Lakehouse Migration Playbook

    #databricks#migration#architecture#cdc

    Before we write code, let's understand our Goal: Your company has a massive, 20-year-old Oracle database sitting in a basement. You need to move all of that data into Databricks without the company having to shut down their website for a week.

    The Tool: Migration requires a combination of physical hardware, network engineering, and PySpark validation scripts.

    1. The Physical Data Transfer

    Real-World Analogy Mapping: Imagine moving from an old house to a new house.

    • The Long Way (Small Data): If you only have a few boxes, you put them in your car and drive them over (This is using an Internet VPN connection to COPY INTO the cloud).
    • The Smart Way (Massive Data): If you have 50 Terabytes of furniture, you hire a massive moving truck. AWS provides a physical "truck" called an AWS Snowball. They mail a ruggedized, bomb-proof hard drive to your office. You plug it into your basement server, copy the 50TB, and FedEx it back to AWS. AWS plugs it directly into the cloud.

    2. Zero-Downtime Cutover (Dual Writing)

    Problems Faced: While the FedEx truck is driving to AWS for 3 days, customers are still buying things on your website. The 50TB hard drive in the truck is now 3 days out of date!

    How Present Technology Solves It: We use Change Data Capture (CDC) or Lakeflow Connect.

    1. The 50TB arrives in the cloud (This is your baseline).
    2. We set up a lightweight streaming pipeline that listens to the old Oracle database for any new transactions that happened while the truck was driving.
    3. We stream those missed transactions into Databricks.
    4. Databricks and the old Oracle server are now 100% identical and updating in real-time.

    3. Mathematical Validation

    Before we unplug the old Oracle server forever, we must mathematically prove that the data in Databricks perfectly matches the old server. If we lose a single customer's bank account balance, we are fired.

    Anatomy Breakdown: We cannot just count the rows (1,000 rows in Oracle = 1,000 rows in Databricks). What if the rows are there, but the names are misspelled? Instead, we create a mathematical Hash of every single row.

    python
    from pyspark.sql.functions import hash, sum, col

# 1. Read the old Oracle Database
oracle_df = spark.read.jdbc(url="jdbc:oracle:thin:@//host:port/service", table="finance")

# 2. Read the new Databricks Delta Table
delta_df = spark.read.table("enterprise_data.finance")

# 3. Create a unique fingerprint (Hash) for every row, then add them all together!
oracle_fingerprint = oracle_df.select(sum(hash(*oracle_df.columns)).alias("total_hash")).collect()[0]["total_hash"]
delta_fingerprint = delta_df.select(sum(hash(*delta_df.columns)).alias("total_hash")).collect()[0]["total_hash"]

# 4. If the fingerprints match perfectly, the data is identical. Pull the plug on Oracle!
assert oracle_fingerprint == delta_fingerprint
print("Migration Successful!")

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