dbmigrate-pro 1.0.0

Universal Database Migration Tool - Migrate any database to any database across 74 databases and 9 paradigms

DBMigrate - Universal Database Migration Tool

Migrate any database to any database. Any paradigm to any paradigm. Powered by AI.

The Problem

Database migrations cost companies $50,000 - $500,000+ because:

• Schema differences require manual mapping
• Stored procedures need complete rewrites (PL/SQL → PL/pgSQL)
• Data type incompatibilities cause data loss
• Cross-paradigm migrations (SQL → NoSQL) are near-impossible manually
• Consultants charge $200-400/hour

The Solution

DBMigrate automates 90% of the migration work across 60+ databases and 9 paradigms:

                               ┌──── PostgreSQL
Oracle ────────┐               ├──── MongoDB
SQL Server ────┤               ├──── Neo4j
MongoDB ───────┤  ┌─────────┐  ├──── Elasticsearch
Neo4j ─────────┼─►│DBMigrate│─►├──── Pinecone
Redis ─────────┤  │AI Engine│  ├──── Cassandra
Elasticsearch ─┤  └─────────┘  ├──── InfluxDB
InfluxDB ──────┤               ├──── Redis
Pinecone ──────┘               └──── Any Target

Features

• Multi-Paradigm Support: Relational, Document, Graph, Vector, Time-Series, Key-Value, Columnar, Search
• Cross-Paradigm Transformation: SQL → Document, Graph → Relational, and more
• Schema Extraction: Connect to 60+ databases, extract complete schema
• Intelligent Type Mapping: 500+ data type conversions with edge case handling
• AI-Powered SP Conversion: Stored procedures translated using latest LLM models
• Trigger Migration: Automatic trigger syntax conversion
• View Translation: Query syntax adapted to target dialect
• Compatibility Analysis: Automatic assessment of migration complexity and data loss risks

Quick Start

CLI Usage

# Install
pip install dbmigrate

# Migrate PostgreSQL to MySQL
dbmigrate --source postgresql --target mysql \
          --source-conn "postgresql://user:pass@localhost/mydb" \
          --output ./migration

# Migrate Oracle to PostgreSQL (the hard one!)
dbmigrate --source oracle --target postgresql \
          --source-conn "oracle://user:pass@localhost/ORCL" \
          --output ./migration

API Usage

from dbmigrate import migrate

report = migrate(
    source_type="oracle",
    target_type="postgresql",
    source_connection="oracle://user:pass@localhost/ORCL",
    output_dir="./migration"
)

print(f"Migrated {report.tables_migrated} tables")
print(f"Converted {report.procedures_converted} stored procedures")

REST API (SaaS)

# Start API server
dbmigrate-api --port 8000

# Create migration job
curl -X POST http://localhost:8000/api/v1/migrations \
  -H "Content-Type: application/json" \
  -d '{
    "source_type": "oracle",
    "target_type": "postgresql",
    "source_connection": "oracle://..."
  }'

# Convert SQL instantly
curl -X POST http://localhost:8000/api/v1/convert/sql \
  -H "Content-Type: application/json" \
  -d '{
    "source_type": "oracle",
    "target_type": "postgresql",
    "sql_code": "SELECT NVL(col, 0) FROM dual WHERE ROWNUM < 10"
  }'

Supported Databases (60+)

🗄️ Relational (SQL)

Database	Extract	Generate	SP Convert	Driver
PostgreSQL	✅	✅	✅	psycopg2
MySQL/MariaDB	✅	✅	✅	mysql-connector
Oracle	✅	✅	✅	oracledb
SQL Server	✅	✅	✅	pyodbc
SQLite	✅	✅	N/A	built-in
Azure SQL	✅	✅	✅	pyodbc
DB2	✅	✅	✅	ibm-db
SAP HANA	✅	✅	✅	hdbcli
Snowflake	✅	✅	✅	snowflake-connector
Aurora	✅	✅	✅	psycopg2/mysql-connector
Cloud SQL	✅	✅	✅	driver per dialect
CockroachDB	✅	✅	✅	psycopg2
YugabyteDB	✅	✅	✅	psycopg2
TiDB	✅	✅	✅	mysql-connector
VoltDB	✅	✅	✅	voltdb-client
SingleStore	✅	✅	✅	mysql-connector

📄 Document (NoSQL)

Database	Extract	Generate	Transform	Driver
MongoDB	✅	✅	✅	pymongo
CouchDB	✅	✅	✅	couchdb
Couchbase	✅	✅	✅	couchbase
AWS DocumentDB	✅	✅	✅	pymongo
Cosmos DB	✅	✅	✅	azure-cosmos
Firestore	✅	✅	✅	google-cloud-firestore
FaunaDB	✅	✅	✅	faunadb
SurrealDB	✅	✅	✅	surrealdb

🕸️ Graph

Database	Extract	Generate	Transform	Driver
Neo4j	✅	✅	✅	neo4j
Amazon Neptune	✅	✅	✅	gremlinpython
ArangoDB	✅	✅	✅	python-arango
JanusGraph	✅	✅	✅	gremlinpython
Dgraph	✅	✅	✅	pydgraph
TigerGraph	✅	✅	✅	pyTigerGraph

🎯 Vector

Database	Extract	Generate	Transform	Driver
Pinecone	✅	✅	✅	pinecone-client
Milvus	✅	✅	✅	pymilvus
Weaviate	✅	✅	✅	weaviate-client
Vespa	✅	✅	✅	pyvespa
Qdrant	✅	✅	✅	qdrant-client
Chroma	✅	✅	✅	chromadb
pgvector	✅	✅	✅	psycopg2

⏱️ Time-Series

Database	Extract	Generate	Transform	Driver
InfluxDB	✅	✅	✅	influxdb-client
TimescaleDB	✅	✅	✅	psycopg2
Prometheus	✅	✅	✅	prometheus-client
OpenTSDB	✅	✅	✅	requests
VictoriaMetrics	✅	✅	✅	requests
QuestDB	✅	✅	✅	psycopg2

🔑 Key-Value

Database	Extract	Generate	Transform	Driver
Redis	✅	✅	✅	redis
Memcached	✅	✅	✅	pymemcache
DynamoDB	✅	✅	✅	boto3
Riak	✅	✅	✅	riak
Aerospike	✅	✅	✅	aerospike
KeyDB	✅	✅	✅	redis
Etcd	✅	✅	✅	etcd3
FoundationDB	✅	✅	✅	foundationdb

📊 Wide-Column / Columnar

Database	Extract	Generate	Transform	Driver
Cassandra	✅	✅	✅	cassandra-driver
HBase	✅	✅	✅	happybase
ScyllaDB	✅	✅	✅	cassandra-driver
Bigtable	✅	✅	✅	google-cloud-bigtable
ClickHouse	✅	✅	✅	clickhouse-connect
Redshift	✅	✅	✅	redshift-connector
BigQuery	✅	✅	✅	google-cloud-bigquery
Druid	✅	✅	✅	pydruid

🔍 Search / Analytics

Database	Extract	Generate	Transform	Driver
Elasticsearch	✅	✅	✅	elasticsearch
OpenSearch	✅	✅	✅	opensearch-py
Solr	✅	✅	✅	pysolr
Splunk	✅	✅	✅	splunk-sdk
Typesense	✅	✅	✅	typesense
MeiliSearch	✅	✅	✅	meilisearch

💾 Embedded

Database	Extract	Generate	Transform	Driver
RocksDB	✅	✅	✅	python-rocksdb
LevelDB	✅	✅	✅	plyvel
DuckDB	✅	✅	✅	duckdb
Berkeley DB	✅	✅	✅	bsddb3

Cross-Paradigm Transformations

DBMigrate can intelligently transform schemas between different database paradigms:

From → To	Supported	Notes
Relational → Document	✅	Embeds related tables, denormalizes JOINs
Relational → Graph	✅	FKs become edges, junction tables become relationships
Document → Relational	✅	Normalizes nested objects, arrays become child tables
Document → Graph	✅	References become edges, embedded docs become connected nodes
Graph → Document	✅	Aggregates connected nodes, edges become references
Graph → Relational	✅	Nodes become tables, edges become junction tables

Transformation Strategies

from dbmigrate.transformers import CrossParadigmTransformer, EmbeddingStrategy

transformer = CrossParadigmTransformer()

# SQL to MongoDB - embed small related tables, normalize large ones
result = transformer.transform(
    source_schema,
    target_paradigm=ParadigmType.DOCUMENT,
    embedding_strategy=EmbeddingStrategy.HYBRID
)

# MongoDB to PostgreSQL - full normalization
result = transformer.transform(
    source_schema,
    target_paradigm=ParadigmType.RELATIONAL,
    normalization_strategy=NormalizationStrategy.FULL
)

Database-Specific Extraction Features

Oracle

• Tables with columns, indexes, foreign keys, primary keys
• Column comments and table comments
• Views with definitions
• Stored procedures and functions (PL/SQL source)
• Packages (spec and body)
• Triggers with timing, events, and body
• Sequences with current value for seamless continuation

SQL Server

• Full schema.table support (dbo, custom schemas)
• VARCHAR(MAX), NVARCHAR(MAX), VARBINARY(MAX) handling
• IDENTITY columns with seed/increment
• Computed columns
• Extended properties (MS_Description)
• T-SQL stored procedures and functions
• Scalar, inline table-valued, and multi-statement functions
• INSTEAD OF triggers
• Sequences (SQL Server 2012+)

MongoDB

• Collection schema inference from sample documents
• JSON Schema validators
• Index definitions (single, compound, text, geospatial)
• Validation rules and levels
• Capped collection settings

Neo4j

• Node labels and properties
• Edge types with cardinality
• Indexes and constraints
• Property type inference
• APOC procedure detection

Elasticsearch

• Index mappings and settings
• Field types and analyzers
• Index templates and aliases
• Ingest pipelines

Redis

• Key pattern analysis
• TTL configurations
• Data structure detection (hash, set, list, sorted set)
• Cluster configuration

Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                            DBMigrate                                    │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌──────────────┐    ┌──────────────┐    ┌────────────────┐            │
│  │  Extractors  │───►│  Universal   │───►│   Generators   │            │
│  │  (60+ DBs)   │    │   Schemas    │    │   (60+ DBs)    │            │
│  └──────────────┘    └──────────────┘    └────────────────┘            │
│         │                   │                    │                      │
│         ▼                   ▼                    ▼                      │
│  ┌──────────────┐    ┌──────────────┐    ┌────────────────┐            │
│  │   Paradigms  │    │ Transformers │    │  Type Mappers  │            │
│  │  • Relational│    │ Cross-Paradigm│   │  500+ types    │            │
│  │  • Document  │    │ SQL↔Document │    └────────────────┘            │
│  │  • Graph     │    │ SQL↔Graph   │                                   │
│  │  • Vector    │    │ Doc↔Graph   │    ┌────────────────┐            │
│  │  • TimeSeries│    └──────────────┘    │  SP Converter  │            │
│  │  • KeyValue  │                        │   (AI/LLM)     │            │
│  │  • Columnar  │                        └────────────────┘            │
│  │  • Search    │                                                       │
│  └──────────────┘                                                       │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Component Overview

• Extractors: Connect to source databases and extract schema metadata
• Paradigms: Unified schema representations for each database category
• Transformers: Convert schemas between paradigms (SQL↔NoSQL, etc.)
• Generators: Generate DDL/schema definitions for target databases
• Type Mappers: Handle 500+ data type conversions with edge cases
• SP Converter: AI-powered stored procedure translation

Pricing (SaaS)

Plan	Tables/Month	SP Conversions	Price
Starter	50	20	$99/mo
Pro	500	200	$499/mo
Enterprise	Unlimited	Unlimited	$2,499/mo

Compare to consultants: $50,000+ per migration project

Output Files

After running a migration, you get:

migration_output/
├── schema.sql              # Complete DDL for target database
├── stored_procedures.sql   # Converted stored procedures
├── triggers.sql            # Converted triggers
├── data_migration_guide.md # Instructions for data transfer
└── migration_report.json   # Detailed report

Requirements

• Python 3.11+
• Database drivers (installed per paradigm or individually)
• Anthropic API key (for SP conversion) - uses Claude Sonnet

Installation

# Basic install (CLI only)
pip install dbmigrate

# Install by individual database
pip install dbmigrate[postgresql]        # PostgreSQL
pip install dbmigrate[mysql]             # MySQL/MariaDB
pip install dbmigrate[oracle]            # Oracle Database
pip install dbmigrate[sqlserver]         # SQL Server (requires ODBC driver)
pip install dbmigrate[mongodb]           # MongoDB
pip install dbmigrate[neo4j]             # Neo4j
pip install dbmigrate[redis]             # Redis
pip install dbmigrate[elasticsearch]     # Elasticsearch
pip install dbmigrate[pinecone]          # Pinecone
pip install dbmigrate[influxdb]          # InfluxDB

# Install by paradigm (all databases in category)
pip install dbmigrate[relational-all]    # All relational databases
pip install dbmigrate[document-all]      # MongoDB, Couchbase, Firestore, etc.
pip install dbmigrate[graph-all]         # Neo4j, ArangoDB, etc.
pip install dbmigrate[vector-all]        # Pinecone, Milvus, Weaviate, etc.
pip install dbmigrate[timeseries-all]    # InfluxDB, TimescaleDB, etc.
pip install dbmigrate[keyvalue-all]      # Redis, DynamoDB, Memcached, etc.
pip install dbmigrate[columnar-all]      # Cassandra, ClickHouse, BigQuery, etc.
pip install dbmigrate[search-all]        # Elasticsearch, OpenSearch, Solr, etc.

# Bundles
pip install dbmigrate[enterprise]        # Common production databases
pip install dbmigrate[all]               # Every supported database

# With REST API server
pip install dbmigrate[api]

# Full install (all features)
pip install dbmigrate[all,api]

# Development
git clone https://github.com/yourcompany/dbmigrate
cd dbmigrate
pip install -e ".[dev,all,api]"

SQL Server ODBC Driver

SQL Server requires the Microsoft ODBC driver:

# Windows (usually pre-installed with SQL Server tools)
# Download: https://docs.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server

# Linux (Ubuntu/Debian)
curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add -
curl https://packages.microsoft.com/config/ubuntu/$(lsb_release -rs)/prod.list > /etc/apt/sources.list.d/mssql-release.list
apt-get update
ACCEPT_EULA=Y apt-get install -y msodbcsql17

# macOS
brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release
brew update
HOMEBREW_NO_ENV_FILTERING=1 ACCEPT_EULA=Y brew install msodbcsql17

License

Commercial license required for production use. Free for evaluation and development.

---

🏢 Enterprise Features

DBMigrate Enterprise Edition includes advanced capabilities for mission-critical migrations:

⚡ High-Performance Parallel Processing

Migrate billions of rows efficiently with intelligent scaling:

from dbmigrate.core.parallel import ParallelMigrationEngine, ScalingConfig, ScalingMode

# Auto-detect optimal configuration
config = ScalingConfig(
    mode=ScalingMode.HYBRID,      # Threads + Processes
    max_workers=0,                 # Auto-detect from CPU
    batch_size=50000,              # Rows per batch
    adaptive_batching=True,        # Auto-tune batch size
    use_server_cursors=True,       # Memory-efficient streaming
)

engine = ParallelMigrationEngine(config)
engine.migrate_tables(['users', 'orders', 'transactions'])

Scaling Modes:

• SINGLE - Debug mode
• THREADED - Best for I/O bound (network, disk)
• PROCESS - Best for CPU bound (transforms, compression)
• HYBRID - Processes with internal threads
• DISTRIBUTED - Redis-based horizontal scaling

🌐 Distributed Processing (Horizontal Scaling)

Scale across multiple servers with Redis-based coordination:

from dbmigrate.core.distributed import DistributedMigrationCoordinator

coordinator = DistributedMigrationCoordinator(
    redis_host='redis-cluster.internal',
    redis_port=6379,
    num_workers=10  # Across multiple servers
)

# Start workers on each server
coordinator.start_worker(worker_id='worker-001')

# Submit migration job
job_id = coordinator.submit_migration(
    source_conn='postgresql://source/db',
    target_conn='postgresql://target/db',
    tables=['users', 'orders', 'products'],
    priority='high'
)

# Monitor progress
status = coordinator.get_job_status(job_id)
print(f"Progress: {status['progress']}%")

🔒 Data Masking & PII Anonymization

GDPR, HIPAA, PCI-DSS compliant data masking:

from dbmigrate.core.masking import DataMaskingEngine, MaskingRule, MaskingStrategy

engine = DataMaskingEngine(deterministic=True)  # Preserve FK relationships

# Configure masking rules
engine.add_rule(MaskingRule('email', MaskingStrategy.HASH))
engine.add_rule(MaskingRule('ssn', MaskingStrategy.REDACT))
engine.add_rule(MaskingRule('salary', MaskingStrategy.RANGE, range_bucket_size=10000))
engine.add_rule(MaskingRule('dob', MaskingStrategy.DATE_SHIFT, date_shift_days=30))
engine.add_rule(MaskingRule('name', MaskingStrategy.FAKE))  # Realistic fake names
engine.add_rule(MaskingRule('phone', MaskingStrategy.MASK_PARTIAL, partial_mask_end=4))

# Or use compliance presets
engine = DataMaskingEngine.create_gdpr_compliant()
engine = DataMaskingEngine.create_hipaa_compliant()
engine = DataMaskingEngine.create_pci_compliant()

# Mask during migration
masked_row = engine.mask_row(original_row)

Masking Strategies:

Strategy	Example Input	Example Output
REDACT	123-45-6789	*********
HASH	[e-mail]	a1b2c3d4...
FAKE	John Doe	Michael Smith
PARTIAL	4111111111111111	************1111
DATE_SHIFT	1990-03-15	1990-02-28
RANGE	75000	70000-80000
NULL	any value	NULL

📡 Real-Time WebSocket Progress

Monitor migrations in real-time via WebSocket:

from dbmigrate.core.websocket import FlaskWebSocketServer

# Server setup
server = FlaskWebSocketServer(app)
server.start()

# Client-side JavaScript
const ws = new WebSocket('ws://localhost:5050/ws/progress');
ws.onmessage = (event) => {
    const progress = JSON.parse(event.data);
    console.log(`Table: ${progress.table}`);
    console.log(`Progress: ${progress.percentage}%`);
    console.log(`Rows/sec: ${progress.rows_per_second}`);
    console.log(`ETA: ${progress.eta_seconds}s`);
};

🔬 Advanced Schema Extraction

Extract enterprise-specific schema objects:

from dbmigrate.extractors.advanced import (
    PostgreSQLAdvancedMixin,
    SQLServerAdvancedMixin,
    OracleAdvancedMixin,
)

# Extract PostgreSQL-specific objects
pg_extractor = PostgreSQLAdvancedExtractor(connection)
extensions = pg_extractor.extract_extensions()  # uuid-ossp, pgcrypto, etc.
materialized_views = pg_extractor.extract_materialized_views()
check_constraints = pg_extractor.extract_check_constraints()

# Extract SQL Server computed columns
ss_extractor = SQLServerAdvancedExtractor(connection)
computed_columns = ss_extractor.extract_computed_columns()

# Extract Oracle partitions
ora_extractor = OracleAdvancedExtractor(connection)
partitions = ora_extractor.extract_partitions()

🌙 Dark Mode UI

Modern enterprise dashboard with dark mode support:

• Automatic theme detection from system preferences
• Toggle with persistent localStorage storage
• Smooth CSS transitions

---

🧪 Testing

DBMigrate includes a comprehensive testing suite:

Running Tests

# All tests
pytest tests/ -v

# Unit tests only
pytest tests/ -v -m unit

# Integration tests only
pytest tests/ -v -m integration

# Specific test files
pytest tests/test_masking.py -v
pytest tests/test_parallel.py -v
pytest tests/test_type_mapper.py -v
pytest tests/test_integration.py -v

# With coverage
pytest tests/ --cov=dbmigrate --cov-report=html

RL-Based Automated Test Agent 🤖

DBMigrate includes an innovative Reinforcement Learning test agent that automatically explores and tests all features:

# Run RL test agent
python tests/rl_test_agent.py

# Or with custom episodes
python -c "from tests.rl_test_agent import run_rl_tests; run_rl_tests(episodes=100)"

Features:

• Q-Learning based exploration
• Automatic edge case discovery
• Coverage-guided testing
• Regression detection
• Property-based test generation

Sample Output:

============================================================
RL TEST AGENT REPORT
============================================================
Episodes Run: 100
Total Reward: 487.50
Average Reward: 4.88
Feature Coverage: 85.7%
Regressions Found: 0
Unique Errors Found: 2

ACTION COVERAGE:
  CREATE_SQLITE_SOURCE              ████████████████████ (100)
  CREATE_SIMPLE_TABLE               ██████████████████ (92)
  INSERT_SMALL_DATASET              ████████████████ (81)
  MIGRATE_SINGLE_TABLE              ██████████████ (73)
  VALIDATE_ROW_COUNT                ████████████ (65)
  ...

Test Categories

Category	Description	Run Command
Unit	Individual component tests	pytest -m unit
Integration	End-to-end workflows	pytest -m integration
Slow	Performance/stress tests	pytest -m slow
Database-specific	Requires real DB	pytest -m postgres

---

Support

• Documentation: https://docs.dbmigrate.io
• Email: [e-mail]
• Enterprise: [e-mail]