Migration Strategies¶

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Best practices for migrating to Cloud Scale Analytics.

Migration Approaches¶

Migration Phases¶

flowchart LR
    A[Assess] --> B[Plan]
    B --> C[Migrate]
    C --> D[Optimize]
    D --> E[Operate]

Phase 1: Assessment¶

# Database assessment script
def assess_database(connection_string):
    """Assess database for migration readiness."""

    assessment = {
        "size_gb": get_database_size(connection_string),
        "table_count": get_table_count(connection_string),
        "complexity_score": analyze_stored_procedures(connection_string),
        "compatibility_issues": check_compatibility(connection_string)
    }

    # Recommend target
    if assessment["size_gb"] < 100 and assessment["complexity_score"] < 50:
        assessment["recommendation"] = "Serverless SQL"
    elif assessment["size_gb"] < 1000:
        assessment["recommendation"] = "Dedicated SQL Pool (DW1000c)"
    else:
        assessment["recommendation"] = "Dedicated SQL Pool (DW3000c+)"

    return assessment

Phase 2: Planning¶

Migration Checklist:

• Inventory all data sources
• Map source to target schemas
• Identify dependencies
• Plan downtime windows
• Create rollback procedures
• Define success criteria

Phase 3: Migration¶

# Data Factory migration pipeline
az datafactory pipeline create \
    --factory-name adf-migration \
    --resource-group rg-analytics \
    --name migrate-sales-data \
    --definition '{
        "activities": [
            {
                "name": "CopyFromOnPrem",
                "type": "Copy",
                "inputs": [{"referenceName": "OnPremSQL", "type": "DatasetReference"}],
                "outputs": [{"referenceName": "DataLake", "type": "DatasetReference"}],
                "typeProperties": {
                    "source": {"type": "SqlSource"},
                    "sink": {"type": "ParquetSink"},
                    "enableStaging": true
                }
            }
        ]
    }'

Phase 4: Validation¶

-- Data validation query
WITH SourceCounts AS (
    SELECT
        'Source' AS System,
        COUNT(*) AS RowCount,
        SUM(Amount) AS TotalAmount
    FROM SourceDatabase.dbo.Sales
),
TargetCounts AS (
    SELECT
        'Target' AS System,
        COUNT(*) AS RowCount,
        SUM(Amount) AS TotalAmount
    FROM OPENROWSET(
        BULK 'https://datalake.dfs.core.windows.net/sales/*.parquet',
        FORMAT = 'PARQUET'
    ) AS sales
)
SELECT * FROM SourceCounts
UNION ALL
SELECT * FROM TargetCounts;

Common Migration Patterns¶

On-Premises SQL Server to Synapse¶

1. Export to Parquet using ADF
2. Load to Data Lake bronze layer
3. Transform to silver/gold layers
4. Create views in Serverless SQL
5. Migrate heavy queries to Dedicated SQL

Hadoop to Databricks¶

1. Migrate HDFS data to ADLS Gen2
2. Convert Hive metastore to Unity Catalog
3. Refactor Spark jobs for Databricks
4. Implement Delta Lake format

Related Documentation¶

• Data Factory Tutorials
• Synapse Setup

Last Updated: January 2025