This document outlines the architectural plan for the complete data migration, serving as Step 1 of 3 in the "Data Migration Planner" workflow. This plan details the strategic approach to move data from source systems to the target environment, ensuring data integrity, minimal downtime, and robust recovery mechanisms.
Project: Data Migration Initiative
Workflow Step: 1 of 3 - Plan Architecture
Deliverable Date: October 26, 2023
Prepared For: [Customer Name/Stakeholder Group]
This architectural plan defines the foundational strategy for migrating data from identified source systems to the new target environment. It encompasses the high-level architecture, data flow, key components, and strategic considerations for field mapping, data transformation, validation, and rollback procedures. The objective is to ensure a secure, efficient, and accurate transfer of data, minimizing business disruption and maximizing data quality in the target system.
The migration architecture will follow an Extract, Transform, Load (ETL) pattern, leveraging a dedicated staging area to ensure data quality and integrity before loading into the target system.
2.1. Architectural Diagram (Conceptual)
+-------------------+ +-----------------+ +-----------------+ +-------------------+
| Source Systems | | Data Extraction| | Data Staging | | Target System |
| (e.g., Legacy DBs,|----->| & Profiling |----->| Area (DWH/DB) |----->| (e.g., New CRM/ERP)|
| Flat Files, APIs)| | (ETL Tool) | | | | |
+-------------------+ +-----------------+ +-----------------+ +-------------------+
^ ^ ^ ^
| | | |
+-----------------------------------------------------------------------------+
|
V
+-------------------+
| Migration Control|
| & Monitoring |
| (Orchestration) |
+-------------------+
2.2. Key Components & Data Flow
* Extraction Layer: Responsible for connecting to source systems and extracting raw data. This will involve appropriate connectors (JDBC, ODBC, API clients, file readers).
* Data Profiling: Initial analysis of source data to understand its structure, quality, completeness, and identify potential issues (e.g., nulls, duplicates, inconsistencies).
* Landing Zone: Raw extracted data is first loaded here.
* Transformation Zone: Data is cleansed, standardized, de-duplicated, and transformed according to defined rules.
* Validation Zone: Pre-load validation and reconciliation occur here.
3.1. In-Scope Data Entities:
3.2. Out-of-Scope Data Entities:
3.3. In-Scope Systems:
Legacy_CRM_DB (Microsoft SQL Server 2012)Old_Product_Catalog (CSV files on Network Share)SAP_ECC_6.0 (Specific modules: SD, MM)3.4. Target System:
New_CRM_Application_DB (PostgreSQL 14)Salesforce Service Cloud (via API)A comprehensive data discovery and profiling phase is critical to understand the source data landscape.
* Schema Analysis: Documenting table structures, data types, primary/foreign keys.
* Data Type Analysis: Identifying data type mismatches between source and target.
* Completeness Check: Quantifying null values, empty strings, missing records.
* Uniqueness Check: Identifying duplicate records based on natural keys.
* Consistency Check: Verifying data format, referential integrity, and business rule adherence.
* Data Volume Assessment: Estimating record counts and overall data size per entity.
* Data Lineage Mapping: Understanding how data flows within source systems.
Field mapping will be meticulously documented to ensure every relevant source field is correctly mapped to its corresponding target field.
1. Direct Mapping: 1:1 mapping where source field directly corresponds to a target field with no transformation.
2. Derived Mapping: Target field is populated by combining or transforming multiple source fields.
3. Default Value Mapping: Target field is populated with a default value if no source data exists or is applicable.
4. Lookup Mapping: Source values are mapped to target values using reference tables (e.g., old status codes to new status codes).
* Source System & Table/File
* Source Field Name
* Source Data Type
* Source Field Description
* Target System & Table
* Target Field Name
* Target Data Type
* Target Field Description
* Transformation Rule (if any)
* Validation Rule (if any)
* Remarks/Dependencies
Data transformation is essential to align source data with target system requirements and business rules.
* Data Type Conversion: e.g., VARCHAR to INTEGER, DATE string to DATE object.
* Format Standardization: e.g., "MM/DD/YYYY" to "YYYY-MM-DD", phone number formatting.
* Data Cleansing: Handling nulls (default values, imputation), removing special characters, trimming whitespace.
* De-duplication: Identifying and merging duplicate records based on defined keys.
* Value Mapping/Lookup: Translating old codes to new codes (e.g., "Active" -> "A", "Inactive" -> "I").
* Aggregation/Splitting: Combining multiple source fields into one target field, or splitting one source field into multiple target fields.
Derivation: Calculating new values based on existing source fields (e.g., total_amount = quantity unit_price).
* Referential Integrity: Ensuring foreign key relationships are maintained or established in the target.
A multi-stage validation approach will be implemented to ensure data accuracy and completeness throughout the migration lifecycle.
* Source Data Profiling: As described in Section 4.
* Schema Comparison: Verify source and target schema compatibility.
* Business Rule Review: Ensure source data adheres to target business rules where possible.
* Record Count Verification: Compare extracted record counts with source system counts.
* Data Type & Format Validation: Ensure data conforms to defined types and formats after initial load to staging.
Constraint Checks: Verify uniqueness, nullability, and referential integrity within the staging area before* loading to target.
* Transformation Rule Validation: Spot checks and automated tests to ensure transformation logic is correctly applied.
* Record Count Reconciliation: Compare total records loaded in target with expected counts from source.
* Random Sample Data Verification: Manual review of a statistically significant sample of records in the target system against source data.
* Key Field Comparison: Verify primary keys and critical business fields match between source and target for selected records.
* Data Aggregation Checks: Run aggregate queries (SUM, COUNT, AVG) on key numeric fields in both source and target to ensure totals match.
* Application-Level Testing: User Acceptance Testing (UAT) by business users to validate data functionality within the target application.
* Report Verification: Validate key reports generated from the target system produce expected results.
A robust rollback plan is essential to mitigate risks and allow for recovery in case of critical failures during or immediately after migration.
* Source System: Ensure comprehensive backups of all source systems are taken immediately prior to migration.
* Target System: If the target system is pre-existing, a full backup/snapshot should be taken before any migration data is loaded. For new systems, this may be less critical but still advised.
* Data will be loaded in batches or phases, allowing for easier identification and isolation of issues.
* Leverage database transaction capabilities for atomic operations, enabling rollbacks of individual batches.
* Data Deletion: Clear migrated data from the target system tables (for the affected batch/entity). This requires carefully designed delete scripts.
* Restore from Backup: In severe cases, restore the target system to its pre-migration state using the snapshot/backup.
* Re-run Migration: After rectifying the identified issues, the migration process can be re-initiated from a clean state.
Data security and compliance will be paramount throughout the migration process.
The architecture will be designed to handle the anticipated data volumes efficiently.
This timeline is a high-level estimate and will be refined during subsequent planning phases.
* Data Discovery & Profiling: 2-4 weeks
* Architecture & Strategy Definition: 1-2 weeks
* Tool Selection & Setup: 1-2 weeks
Estimated Duration: 4-8 weeks*
* Detailed Data Mapping & Transformation Rule Design: 3-5 weeks
* ETL Script/Job Development: 4-8 weeks
* Validation Script Development: 2-3 weeks
* Rollback Procedure Development: 1-2 weeks
Estimated Duration: 6-10 weeks*
* Unit Testing of ETL Jobs: 2-3 weeks
* System Integration Testing (SIT): 2-4 weeks
* User Acceptance Testing (UAT): 3-5 weeks
* Performance Testing: 1-2 weeks
Estimated Duration: 5-9 weeks*
* Pre-Migration Activities (Backups, System Freeze): 1-3 days
* Pilot Migration / Dry Runs: 1-2 weeks
* Final Data Migration & Cutover: 3-7 days (depending on data volume and downtime tolerance)
* Post-Migration Validation & Reconciliation: 1-3 days
Estimated Duration: 2-4 weeks*
Total Estimated Project Duration: 17 - 31 weeks (Approx.
This document provides a comprehensive set of code templates and structural guidelines to facilitate a robust and well-planned data migration. These components are designed to define, execute, and validate your data migration process, ensuring data integrity, traceability, and recoverability.
The generated code focuses on Python due to its versatility, extensive libraries for data manipulation, and readability, making it an excellent choice for scripting data migration tasks.
The following sections provide code for the core components of your data migration framework:
These components are modular, allowing for independent development, testing, and maintenance.
This section provides a Python dictionary structure to define the mapping between source and target fields. This structure is designed to be easily extensible and human-readable.
# data_migration/config/field_mappings.py
"""
Defines the field mappings from source to target systems.
Each entry in the MAPPINGS dictionary represents a target table,
containing a list of field mapping rules.
"""
# Define common data types for reference or validation
DATA_TYPES = {
"STRING": "VARCHAR",
"INTEGER": "INT",
"DECIMAL": "NUMERIC(18, 4)",
"BOOLEAN": "BOOLEAN",
"DATETIME": "TIMESTAMP",
"DATE": "DATE",
"UUID": "UUID"
}
# --- FIELD MAPPINGS ---
# Structure:
# {
# "target_table_name": [
# {
# "source_field": "source_table.source_column_name",
# "target_field": "target_column_name",
# "target_data_type": DATA_TYPES["STRING"], # Expected target data type
# "transformation_rule": "transform_string_to_uppercase", # Optional: Name of a transformation function
# "default_value": None, # Optional: Value to use if source is null and no transformation
# "is_nullable": True, # Optional: Whether the target field can be null
# "description": "Maps the customer's name from source to target."
# },
# {
# "source_field": "source_table.creation_date",
# "target_field": "created_at",
# "target_data_type": DATA_TYPES["DATETIME"],
# "transformation_rule": "transform_date_format",
# "default_value": "CURRENT_TIMESTAMP",
# "is_nullable": False,
# "description": "Maps the record creation date, applying a date format transformation."
# },
# # Add more field mappings as needed
# ],
# "target_table_products": [
# {
# "source_field": "source_products.product_id",
# "target_field": "product_uuid",
# "target_data_type": DATA_TYPES["UUID"],
# "transformation_rule": "generate_uuid_or_map_existing", # Example for complex logic
# "is_nullable": False,
# "description": "Unique identifier for the product, potentially generating UUIDs for new records."
# },
# {
# "source_field": "source_products.price_usd",
# "target_field": "unit_price",
# "target_data_type": DATA_TYPES["DECIMAL"],
# "transformation_rule": "convert_currency_to_usd", # Example: if source has multiple currencies
# "default_value": 0.00,
# "is_nullable": False,
# "description": "Product unit price in USD."
# }
# ]
# }
MAPPINGS = {
"customer": [
{
"source_field": "CRM_Customers.customer_id",
"target_field": "customer_uuid",
"target_data_type": DATA_TYPES["UUID"],
"transformation_rule": "generate_uuid",
"is_nullable": False,
"description": "Unique identifier for the customer, generated if not present."
},
{
"source_field": "CRM_Customers.first_name",
"target_field": "first_name",
"target_data_type": DATA_TYPES["STRING"],
"transformation_rule": "trim_and_capitalize",
"is_nullable": False,
"description": "Customer's first name, trimmed and capitalized."
},
{
"source_field": "CRM_Customers.last_name",
"target_field": "last_name",
"target_data_type": DATA_TYPES["STRING"],
"transformation_rule": "trim_and_capitalize",
"is_nullable": False,
"description": "Customer's last name, trimmed and capitalized."
},
{
"source_field": "CRM_Customers.email",
"target_field": "email_address",
"target_data_type": DATA_TYPES["STRING"],
"transformation_rule": "validate_email_format",
"is_nullable": True,
"description": "Customer's email address, validated for format."
},
{
"source_field": "CRM_Customers.status",
"target_field": "customer_status",
"target_data_type": DATA_TYPES["STRING"],
"transformation_rule": "map_status_codes",
"default_value": "ACTIVE",
"is_nullable": False,
"description": "Customer's status, mapped from source codes to target standard."
},
{
"source_field": "CRM_Customers.registration_date",
"target_field": "registered_on",
"target_data_type": DATA_TYPES["DATETIME"],
"transformation_rule": "to_iso_datetime",
"is_nullable": False,
"description": "Date and time of customer registration."
},
{
"source_field": "CRM_Customers.last_activity",
"target_field": "last_active_at",
"target_data_type": DATA_TYPES["DATETIME"],
"transformation_rule": "to_iso_datetime",
"is_nullable": True,
"description": "Last known activity timestamp."
}
],
"order": [
{
"source_field": "ERP_Orders.order_id",
"target_field": "order_uuid",
"target_data_type": DATA_TYPES["UUID"],
"transformation_rule": "generate_uuid",
"is_nullable": False,
"description": "Unique identifier for the order."
},
{
"source_field": "ERP_Orders.customer_ref_id",
"target_field": "customer_uuid",
"target_data_type": DATA_TYPES["UUID"],
"transformation_rule": "lookup_customer_uuid", # Requires a lookup mechanism
"is_nullable": False,
"description": "Foreign key to the customer table, mapped via lookup."
},
{
"source_field": "ERP_Orders.order_date",
"target_field": "order_date",
"target_data_type": DATA_TYPES["DATE"],
"transformation_rule": "to_iso_date",
"is_nullable": False,
"description": "Date when the order was placed."
},
{
"source_field": "ERP_Orders.total_amount",
"target_field": "total_amount",
"target_data_type": DATA_TYPES["DECIMAL"],
"transformation_rule": "to_decimal_2dp",
"is_nullable": False,
"description": "Total amount of the order."
},
{
"source_field": "ERP_Orders.currency",
"target_field": "currency_code",
"target_data_type": DATA_TYPES["STRING"],
"transformation_rule": "standardize_currency",
"default_value": "USD",
"is_nullable": False,
"description": "ISO 4217 currency code for the order."
}
]
}
# Example of how to access a mapping:
# customer_mappings = MAPPINGS["customer"]
# for mapping in customer_mappings:
# print(f"Source: {mapping['source_field']} -> Target: {mapping['target_field']}")
This section provides a set of Python functions to apply common data transformations. These functions are designed to be generic and reusable.
# data_migration/transforms/rules.py
import datetime
import uuid
import re
from typing import Any, Optional
"""
Defines a collection of reusable data transformation functions.
Each function takes a source value and returns the transformed value.
"""
def transform_string_to_uppercase(value: Optional[str]) -> Optional[str]:
"""Converts a string to uppercase."""
if value is None:
return None
return str(value).upper()
def transform_string_to_lowercase(value: Optional[str]) -> Optional[str]:
"""Converts a string to lowercase."""
if value is None:
return None
return str(value).lower()
def trim_whitespace(value: Optional[str]) -> Optional[str]:
"""Removes leading/trailing whitespace from a string."""
if value is None:
return None
return str(value).strip()
def trim_and_capitalize(value: Optional[str]) -> Optional[str]:
"""Trims whitespace and capitalizes the first letter of each word."""
if value is None:
return None
return " ".join([word.capitalize() for word in str(value).strip().split()])
def to_integer(value: Any) -> Optional[int]:
"""Converts a value to an integer."""
if value is None or value == '':
return None
try:
return int(float(value)) # Handle potential string representations of floats
except (ValueError, TypeError):
return None
def to_decimal_2dp(value: Any) -> Optional[float]:
"""Converts a value to a float, rounded to 2 decimal places."""
if value is None or value == '':
return None
try:
return round(float(value), 2)
except (ValueError, TypeError):
return None
def to_iso_date(value: Any) -> Optional[str]:
"""Converts a date-like value to ISO 8601 date string (YYYY-MM-DD)."""
if value is None or value == '':
return None
try:
if isinstance(value, datetime.date):
return value.isoformat()
elif isinstance(value, datetime.datetime):
return value.date().isoformat()
else:
# Attempt to parse common date formats
for fmt in ["%Y-%m-%d", "%m/%d/%Y", "%d-%m-%Y", "%Y%m%d"]:
try:
return datetime.datetime.strptime(str(value), fmt).date().isoformat()
except ValueError:
continue
return None # Could not parse
except Exception:
return None
def to_iso_datetime(value: Any) -> Optional[str]:
"""Converts a datetime-like value to ISO 8601 datetime string (YYYY-MM-DDTHH:MM:SS)."""
if value is None or value == '':
return None
try:
if isinstance(value, datetime.datetime):
return value.isoformat()
elif isinstance(value, datetime.date):
return datetime.datetime(value.year, value.month, value.day).isoformat()
else:
# Attempt to parse common datetime formats
for fmt in ["%Y-%m-%d %H:%M:%S", "%m/%d/%Y %H:%M:%S", "%Y-%m-%dT%H:%M:%S", "%Y%m%d%H%M%S"]:
try:
return datetime.datetime.strptime(str(value), fmt).isoformat()
except ValueError:
continue
return None # Could not parse
except Exception:
return None
def generate_uuid(value: Any = None) -> str:
"""Generates a new UUID. If an existing value is provided and valid, it's returned."""
if value and isinstance(value, str) and len(value) == 36 and re.match(r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$", value, re.IGNORECASE):
return value
return str(uuid.uuid4())
def validate_email_format(value: Optional[str]) -> Optional[str]:
"""Validates email format using a basic regex. Returns None if invalid."""
if value is None:
return None
email_regex = r"^[a-zA
Project: [Insert Project Name]
Date: October 26, 2023
Version: 1.0
Prepared For: [Customer Name]
Prepared By: PantheraHive Solutions
This document outlines a comprehensive plan for the data migration project, detailing the strategy, processes, and procedures required to successfully transfer data from the designated source system(s) to the target system(s). It covers critical aspects including field mapping, data transformation rules, validation scripts, robust rollback procedures, and a high-level timeline estimate. The objective is to ensure a secure, accurate, and efficient migration with minimal disruption to business operations, maintaining data integrity and quality throughout the process.
This data migration project aims to consolidate and transfer critical business data from [Specify Source System(s) e.g., "Legacy CRM (Salesforce Classic instance)", "On-premise SQL Database"] to [Specify Target System(s) e.g., "New Cloud-based ERP (SAP S/4HANA)", "Upgraded CRM (Salesforce Lightning instance)"].
Objectives:
In-Scope Data Entities:
Out-of-Scope Data Entities:
Source System(s):
Target System(s):
The migration will follow a Phased Approach to minimize risk and allow for iterative testing and validation.
Field mapping is a critical component that defines how each data element from the source system will correspond to a data element in the target system. This will be documented in a comprehensive "Data Mapping Document" spreadsheet, reviewed and signed off by relevant business and technical stakeholders.
Example Structure for Data Mapping Document:
| Source System | Source Object/Table | Source Field Name | Source Data Type | Source Max Length | Source Description | Transformation Rule ID | Target System | Target Object/Table | Target Field Name | Target Data Type | Target Max Length | Target Required? | Target Description | Notes/Comments |
| :------------ | :------------------ | :---------------- | :--------------- | :---------------- | :----------------- | :--------------------- | :------------ | :------------------ | :---------------- | :--------------- | :---------------- | :--------------- | :----------------- | :------------- |
| Legacy CRM | Account | Name | String | 255 | Company Name | TR-001 | New ERP | KNA1 | NAME1 | CHAR | 35 | Yes | Customer Name | Truncate if > 35 |
| Legacy CRM | Account | BillingAddress | String | 500 | Billing Address | TR-002 | New ERP | ADRC | STREET, HOUSE_NUM | CHAR | 60, 10 | Yes | Street & House No. | Parse into components |
| Legacy CRM | Contact | IsPrimary | Boolean | 1 | Primary Contact | TR-003 | New ERP | ZCONTACT | PRIMARY_FLAG | BOOLEAN | 1 | No | Primary Contact | Map true/false to X/ ' ' |
| Legacy CRM | Opportunity | Amount | Currency | 18,2 | Opportunity Value | TR-004 | New ERP | VBAK | NETWR | CURR | 13,2 | Yes | Net Value of Sales | Convert USD to EUR |
Key Considerations:
Data transformation rules define how source data will be manipulated to fit the target system's requirements, data model, and business logic. Each rule will be documented with a unique ID and detailed specification.
Common Transformation Categories:
* TR-001 (Name Truncation): Source Account.Name (String, 255) to Target KNA1.NAME1 (CHAR, 35). If source length > 35, truncate to 35 characters. Log truncated records.
* TR-004 (Currency Conversion): Source Opportunity.Amount (Currency, USD) to Target VBAK.NETWR (CURR, EUR). Apply daily exchange rate on the CloseDate of the opportunity.
* TR-002 (Address Parsing): Source Account.BillingAddress (single String) to Target ADRC.STREET, ADRC.HOUSE_NUM, ADRC.POST_CODE, ADRC.CITY. Use regex and custom logic to parse address components.
* TR-005 (Full Name): Source Contact.FirstName and Contact.LastName to Target ZCONTACT.FULL_NAME. Concatenate with a space: FirstName + ' ' + LastName.
* TR-006 (Country Code Mapping): Source Account.Country (e.g., "United States", "Germany") to Target ADRC.COUNTRY (ISO 3166-1 alpha-2 codes: "US", "DE"). Use a predefined lookup table.
* TR-003 (Primary Contact Flag): Source Contact.IsPrimary (Boolean TRUE/FALSE) to Target ZCONTACT.PRIMARY_FLAG (CHAR(1) 'X' for true, ' ' for false).
* TR-007 (Customer Group Assignment): If Account.AnnualRevenue > $1M, assign CustomerGroup = 'Large Enterprise'. Else if AnnualRevenue > $100K, assign 'Mid-Market'. Else 'SMB'.
* TR-008 (Total Sales YTD): Calculate sum of Opportunity.Amount for current year from Source Opportunity records for each Account and map to Target KNA1.ZTOTAL_SALES_YTD.
* TR-009 (Missing Phone Number): If Contact.Phone is null/empty, set Target ZCONTACT.PHONE to 'N/A'.
A robust validation strategy is paramount to ensuring data quality and integrity throughout the migration lifecycle.
A. Pre-Migration Source Data Validation (Data Quality Assessment)
* Completeness Checks:
* Identify records with missing mandatory fields (e.g., Account.Name is empty).
* Count of null values for critical fields.
* Uniqueness Checks:
* Identify duplicate records based on key identifiers (e.g., Account.EIN, Contact.Email).
* Identify duplicate values within fields that should be unique.
* Format/Pattern Checks:
* Validate email addresses (Contact.Email) against regex patterns.
* Validate phone numbers, postal codes against expected formats.
* Referential Integrity Checks:
* Ensure child records have valid parent references (e.g., Opportunity.AccountId points to an existing Account).
* Range/Value Checks:
* Ensure numeric fields are within acceptable ranges (e.g., Opportunity.Amount > 0).
* Validate picklist values against allowed sets.
B. Post-Migration Target Data Validation (Integrity, Completeness, Accuracy)
* Record Count Validation:
* Compare total record counts for each migrated entity between source and target (e.g., COUNT(Source.Accounts) == COUNT(Target.KNA1)).
* Compare counts after applying filters/transformations (e.g., only active accounts migrated).
* Data Completeness Validation:
* Check for null values in mandatory target fields.
* Verify that all expected records from the source are present in the target.
* Data Accuracy Validation (Spot Checks & Aggregate Checks):
* Random Sample Verification: Select a random sample of records (e.g., 5-10% for high-value entities) and manually verify field-by-field accuracy against source.
* Aggregate Sums/Averages: Compare sums of critical numeric fields (e.g., SUM(Opportunity.Amount) in source vs. SUM(VBAK.NETWR) in target, considering currency conversion).
* Unique Constraint Verification: Ensure no duplicate primary keys or unique identifiers exist in the target.
* Referential Integrity Validation: Verify relationships between migrated entities in the target system (e.g., Sales Order correctly linked to Customer Master).
* Transformation Rule Verification: Select records that underwent specific transformations (e.g., address parsing, currency conversion) and verify the output.
* System Functionality Checks:
* Ensure basic CRUD (Create, Read, Update, Delete) operations work correctly with migrated data.
* Verify reports and dashboards using migrated data.
C. Validation Tools & Methods:
Identified data quality issues during pre-migration validation will be addressed systematically:
* Automated Cleansing: For common, rule-based issues (e.g., standardizing phone numbers, correcting common spelling mistakes).
* Manual Cleansing: For complex issues requiring human judgment (e.g., merging duplicate customer records, enriching missing data).
* Business Review & Decision: For ambiguous data, business owners will decide on the correct values or if the data should be excluded.
A comprehensive rollback plan is essential to mitigate risks in case of unforeseen issues during or immediately after the production migration.
A. Trigger for Rollback:
A rollback will be initiated if any of the following critical criteria are not met or if severe issues are detected: