DeltaFlow

Intelligent Incremental Data Synchronization for GCP Dataflow

DeltaFlow is a production-ready Google Cloud Dataflow template for intelligent, automated cross-platform data synchronization. Built on Apache Beam, it provides seamless data pipelines between PostgreSQL, MongoDB, and BigQuery with zero-configuration schema detection and intelligent incremental syncing.

Overview

DeltaFlow eliminates the complexity of building and maintaining data synchronization pipelines. Whether you need to sync operational databases to your data warehouse, replicate data across projects, or build real-time analytics, DeltaFlow provides a robust, scalable solution that just works.

Why DeltaFlow?

Smart Sync: Automatically syncs only new data since the last run - no manual timestamp management required
Auto-Schema: Detects source schemas and creates optimized BigQuery tables with proper types, partitioning, and clustering
Zero Config: Works out of the box with sensible defaults while remaining fully configurable
Production Ready: Battle-tested error handling, automatic retries, and comprehensive monitoring
Cost Efficient: Incremental syncing reduces compute costs by up to 90% compared to full table scans

Key Features

Smart Sync - Intelligent Incremental Synchronization

Never manually manage timestamps again. Smart Sync queries your destination BigQuery table to find the latest timestamp, then automatically syncs only new data since that point.

# First run: Syncs all historical data
# Subsequent runs: Automatically sync only new records since last run
# After outage: Automatically catches up on missed data

enable_smart_sync=true

Benefits:

Eliminates duplicate data processing
Automatic gap detection and recovery
Self-healing pipeline behavior
Reduces processing time by 90%+

Auto-Schema - Zero-Configuration Schema Detection

Point DeltaFlow at your source table and it automatically:

Detects the source schema
Maps types to BigQuery equivalents (including JSON, arrays, timestamps)
Creates optimized tables with time partitioning
Configures clustering for query performance

enable_auto_schema=true
source_table_for_schema=public.users
partition_field=created_at
clustering_fields=user_id,status

Multi-Source Support

Seamlessly sync data from:

PostgreSQL - Direct psycopg2 connectivity, no JDBC required
MongoDB - Native pymongo support with BSON handling
BigQuery - Cross-project and cross-dataset replication

Enterprise Features

VPC Support: Private network connectivity with subnet configuration
Service Accounts: Fine-grained IAM permissions
Parallel Processing: Multiple sources can run concurrently
Flexible Transformations: JSON-based field mapping and data transformation
Write Modes: APPEND, TRUNCATE, or WRITE_EMPTY support
Comprehensive Logging: Cloud Logging integration with structured logs

Quick Start

Prerequisites

Google Cloud Project with Dataflow API enabled
Docker installed (for template building)
gcloud CLI configured
Source database credentials

5-Minute Setup

1. Set environment variables:

export PROJECT_ID="your-project-id"
export BUCKET_NAME="${PROJECT_ID}-dataflow-templates"
export IMAGE_NAME="gcr.io/${PROJECT_ID}/dataflow/deltaflow:v1.0.0"

2. Build and deploy:

# Build Docker image
docker build -t $IMAGE_NAME .

# Push to registry
docker push $IMAGE_NAME

# Deploy template
gcloud dataflow flex-template build gs://${BUCKET_NAME}/templates/deltaflow \
  --image $IMAGE_NAME \
  --sdk-language PYTHON \
  --metadata-file metadata.json

3. Run your first sync:

gcloud dataflow flex-template run my-first-sync-$(date +%Y%m%d-%H%M%S) \
  --template-file-gcs-location=gs://${BUCKET_NAME}/templates/deltaflow \
  --region=us-central1 \
  --parameters="data_source=postgresql,\
postgresql_host=YOUR_HOST,\
postgresql_database=YOUR_DB,\
postgresql_username=YOUR_USER,\
postgresql_password=YOUR_PASSWORD,\
postgresql_query=SELECT * FROM users,\
destination_bigquery_project=${PROJECT_ID},\
destination_bigquery_dataset=analytics,\
destination_bigquery_table=users"

Installation

From Source

# Clone the repository
git clone https://github.com/your-org/deltaflow.git
cd deltaflow

# Install dependencies
pip install -r requirements.txt

# Run locally (for testing)
python main.py \
  --runner=DirectRunner \
  --data_source=postgresql \
  --postgresql_host=localhost \
  --postgresql_database=testdb \
  --postgresql_username=user \
  --postgresql_password=pass \
  --postgresql_query="SELECT * FROM test_table" \
  --destination_bigquery_project=your-project \
  --destination_bigquery_dataset=test \
  --destination_bigquery_table=test

Docker Deployment

# Build production image
docker build -t deltaflow:latest .

# Run via Docker
docker run deltaflow:latest \
  python main.py \
  --runner=DataflowRunner \
  --project=your-project \
  --region=us-central1 \
  # ... additional parameters

Usage Examples

PostgreSQL to BigQuery with Smart Sync

Sync only new records based on updated_at timestamp:

gcloud dataflow flex-template run postgresql-incremental-sync \
  --template-file-gcs-location=gs://${BUCKET_NAME}/templates/deltaflow \
  --region=us-central1 \
  --parameters="\
data_source=postgresql,\
postgresql_host=10.0.0.5,\
postgresql_database=production_db,\
postgresql_username=dataflow_user,\
postgresql_password=secure_password,\
enable_smart_sync=true,\
postgresql_base_query=SELECT * FROM orders WHERE updated_at > '{start_timestamp}' AND updated_at <= '{end_timestamp}' ORDER BY updated_at ASC,\
smart_sync_timestamp_column=updated_at,\
destination_bigquery_project=analytics-project,\
destination_bigquery_dataset=production,\
destination_bigquery_table=orders"

How it works:

First run: Syncs all historical data (or configure fallback period)
Subsequent runs: Automatically detects latest timestamp in BigQuery and syncs only new records
After gaps: Automatically catches up on any missed time periods

MongoDB to BigQuery with Auto-Schema

Automatically detect schema from MongoDB documents:

gcloud dataflow flex-template run mongodb-auto-schema-sync \
  --template-file-gcs-location=gs://${BUCKET_NAME}/templates/deltaflow \
  --region=us-central1 \
  --parameters="\
data_source=mongodb,\
mongodb_host=mongodb.example.com,\
mongodb_database=user_data,\
mongodb_collection=profiles,\
mongodb_query={\"status\":\"active\",\"verified\":true},\
enable_auto_schema=true,\
source_table_for_schema=user_data.profiles,\
partition_field=created_at,\
clustering_fields=user_id,country,\
destination_bigquery_project=analytics-project,\
destination_bigquery_dataset=user_analytics,\
destination_bigquery_table=user_profiles"

BigQuery Cross-Project Replication

Replicate data between projects or datasets:

gcloud dataflow flex-template run bigquery-replication \
  --template-file-gcs-location=gs://${BUCKET_NAME}/templates/deltaflow \
  --region=us-central1 \
  --parameters="\
data_source=bigquery,\
source_bigquery_project=source-project,\
source_bigquery_dataset=raw_data,\
source_bigquery_table=events,\
destination_bigquery_project=analytics-project,\
destination_bigquery_dataset=processed_data,\
destination_bigquery_table=events"

Multi-Source Parallel Sync

Combine data from multiple sources:

# Configure multiple sources (comma-separated)
data_source=postgresql,mongodb,bigquery

# Each source streams data in parallel
# Results are combined before writing to BigQuery

Smart Sync Example with Scheduling

Run every 2 hours to continuously sync new data:

# Create Cloud Scheduler job
gcloud scheduler jobs create http users-sync-every-2hours \
  --location=us-central1 \
  --schedule="0 */2 * * *" \
  --time-zone="UTC" \
  --uri="https://dataflow.googleapis.com/v1b3/projects/${PROJECT_ID}/locations/us-central1/flexTemplates:launch" \
  --http-method=POST \
  --headers="Content-Type=application/json" \
  --message-body='{
    "launch_parameter": {
      "jobName": "users-sync-'$(date +%Y%m%d-%H%M%S)'",
      "containerSpecGcsPath": "gs://'${BUCKET_NAME}'/templates/deltaflow",
      "parameters": {
        "data_source": "postgresql",
        "postgresql_host": "YOUR_HOST",
        "postgresql_database": "YOUR_DB",
        "postgresql_username": "YOUR_USER",
        "postgresql_password": "YOUR_PASSWORD",
        "enable_smart_sync": "true",
        "postgresql_base_query": "SELECT * FROM users WHERE updated_at > '\''{start_timestamp}'\'' AND updated_at <= '\''{end_timestamp}'\'' ORDER BY updated_at ASC",
        "destination_bigquery_project": "'${PROJECT_ID}'",
        "destination_bigquery_dataset": "analytics",
        "destination_bigquery_table": "users"
      }
    }
  }'

Result: Every 2 hours, only new data since the previous run is synced - efficient, automatic, and self-healing.

Configuration

DeltaFlow supports 40+ configuration parameters for fine-grained control. Here are the most important ones:

Core Parameters

Parameter	Description	Example
`data_source`	Source type: postgresql, mongodb, bigquery	`postgresql`
`destination_bigquery_project`	Target GCP project	`analytics-prod`
`destination_bigquery_dataset`	Target dataset	`production_data`
`destination_bigquery_table`	Target table	`users`

Smart Sync Parameters

Parameter	Default	Description
`enable_smart_sync`	`false`	Enable intelligent incremental sync
`smart_sync_timestamp_column`	`updated_at`	Column used for sync detection
`postgresql_base_query`	-	Query template with `{start_timestamp}` and `{end_timestamp}` placeholders
`sync_all_on_empty_table`	`true`	Sync all historical data when table is empty
`fallback_days`	`1`	Days to look back if not syncing all data

Auto-Schema Parameters

Parameter	Default	Description
`enable_auto_schema`	`true`	Enable automatic schema detection
`source_table_for_schema`	-	Source table to detect schema from
`partition_field`	`updated_at`	Field for BigQuery time partitioning
`clustering_fields`	-	Comma-separated clustering fields
`delete_existing_table`	`false`	Drop and recreate table

For complete parameter reference, see CONFIGURATION.md.

Architecture

DeltaFlow is built on Apache Beam and follows a modular, extensible architecture:

┌─────────────────┐
│  Data Sources   │
│  - PostgreSQL   │
│  - MongoDB      │
│  - BigQuery     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Smart Sync     │ ◄─── Query destination for latest timestamp
│  (Optional)     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Schema Detect  │ ◄─── Analyze source schema
│  (Optional)     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Table Create   │ ◄─── Create optimized BigQuery table
│  (Optional)     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Data Reader    │ ◄─── Stream data from source
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Transform &    │ ◄─── Apply transformations
│  Validate       │      Sanitize for BigQuery
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  BigQuery Write │ ◄─── Write to destination
└─────────────────┘

Key Components:

Single-File Design: All logic in main.py for Dataflow template compatibility
DoFn-Based Processing: Modular, testable components
ValueProvider Pattern: Runtime parameter resolution for templates
Parallel Streams: Multiple sources processed concurrently

For detailed architecture documentation, see ARCHITECTURE.md.

Deployment

Development Environment

# Set up local development
export PROJECT_ID="dev-project"
export BUCKET_NAME="${PROJECT_ID}-dataflow-templates"

# Build and deploy to dev
docker build -t gcr.io/${PROJECT_ID}/dataflow/deltaflow:dev .
docker push gcr.io/${PROJECT_ID}/dataflow/deltaflow:dev

gcloud dataflow flex-template build gs://${BUCKET_NAME}/templates/deltaflow-dev \
  --image gcr.io/${PROJECT_ID}/dataflow/deltaflow:dev \
  --sdk-language PYTHON \
  --metadata-file metadata.json

Production Deployment

# Production with versioned tags
export PROJECT_ID="prod-project"
export VERSION="1.0.0"
export IMAGE_NAME="gcr.io/${PROJECT_ID}/dataflow/deltaflow:${VERSION}"

# Build production image
docker build -t $IMAGE_NAME .
docker tag $IMAGE_NAME gcr.io/${PROJECT_ID}/dataflow/deltaflow:latest

# Push both versioned and latest
docker push $IMAGE_NAME
docker push gcr.io/${PROJECT_ID}/dataflow/deltaflow:latest

# Deploy template
gcloud dataflow flex-template build gs://${BUCKET_NAME}/templates/deltaflow \
  --image $IMAGE_NAME \
  --sdk-language PYTHON \
  --metadata-file metadata.json

Network Configuration (VPC)

For private database access:

gcloud dataflow flex-template run secure-sync \
  --template-file-gcs-location=gs://${BUCKET_NAME}/templates/deltaflow \
  --region=us-central1 \
  --network=projects/${PROJECT_ID}/global/networks/vpc-network \
  --subnetwork=regions/us-central1/subnetworks/private-subnet \
  --disable-public-ips \
  --service-account-email=dataflow-worker@${PROJECT_ID}.iam.gserviceaccount.com \
  --parameters="..."

For comprehensive deployment guide, see DEPLOYMENT.md.

Monitoring and Troubleshooting

View Job Status

# List recent jobs
gcloud dataflow jobs list --region=us-central1 --limit=5

# Get job details
gcloud dataflow jobs describe JOB_ID --region=us-central1

# View job metrics
gcloud dataflow jobs show JOB_ID --region=us-central1

View Logs

# Tail logs for a specific job
gcloud logging read "resource.type=dataflow_job AND resource.labels.job_id=JOB_ID" \
  --limit=50 \
  --format="table(timestamp,severity,textPayload)"

# Filter for errors
gcloud logging read "resource.type=dataflow_job AND resource.labels.job_id=JOB_ID AND severity>=ERROR" \
  --limit=20

Common Issues

Issue	Cause	Solution
"Query cannot be empty"	Smart Sync failed to build query	Check `postgresql_base_query` format with placeholders
"Template not found"	Invalid GCS path	Verify: `gsutil ls gs://bucket/templates/`
"Connection refused"	Database not accessible	Check VPC/firewall rules and credentials
"BigQuery JSON error"	Invalid data types	Records are auto-sanitized; check source data for null bytes

Contributing

We welcome contributions! Here's how to get started:

Development Setup

# Clone and setup
git clone https://github.com/your-org/deltaflow.git
cd deltaflow
pip install -r requirements.txt

# Run tests (when available)
pytest tests/

# Run local pipeline
python main.py --runner=DirectRunner --data_source=postgresql ...

Adding New Data Sources

Create a new DoFn class inheriting from beam.DoFn
Implement setup(), process(), and teardown() methods
Add configuration parameters to CustomPipelineOptions
Integrate into the main pipeline in run_pipeline()
Update metadata.json with new parameters
Add tests and documentation

Example:

class NewSourceReaderDoFn(DoFn):
    def __init__(self, options: CustomPipelineOptions):
        self.options = options

    def setup(self):
        # Initialize connections in worker context
        import your_library
        self._client = your_library.connect(...)

    def process(self, element):
        # Read and yield data
        for record in self._client.read():
            yield sanitize_for_bigquery_json(record)

    def teardown(self):
        if self._client:
            self._client.close()

Code Style

Follow PEP 8 guidelines
Use type hints where applicable
Add docstrings to all classes and functions
Keep functions focused and testable

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Copyright 2025 kavyasoni

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Roadmap

Version 1.1 (Planned)

Apache Kafka source support
Google Cloud SQL (MySQL) source support
Schema evolution detection and automatic migration
Dead letter queue for failed records
Metrics export to Cloud Monitoring
Built-in data quality checks

Version 1.2 (Future)

Streaming mode support for real-time syncing
Multi-destination writes (write to multiple BigQuery tables)
Cloud Spanner source support
Advanced transformation DSL
Web UI for pipeline configuration
Cost optimization recommendations

Version 2.0 (Vision)

Change Data Capture (CDC) support
Bi-directional sync capabilities
Machine learning-based schema mapping
Auto-tuning performance optimizer
GraphQL API for pipeline management

Support and Community

Documentation: docs/
Issues: GitHub Issues
Discussions: GitHub Discussions
Stack Overflow: Tag questions with deltaflow and google-cloud-dataflow

Acknowledgments

Built with:

Apache Beam - Unified data processing framework
Google Cloud Dataflow - Fully managed data processing service
psycopg2 - PostgreSQL adapter for Python
PyMongo - MongoDB driver for Python
Google Cloud BigQuery - Serverless data warehouse

Authors

kavyasoni - Initial work and maintenance

Made with ❤️ for the data engineering community

For detailed documentation, see:

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
.github		.github
docs		docs
examples		examples
tests		tests
.gitignore		.gitignore
CHANGES_SUMMARY.md		CHANGES_SUMMARY.md
CLAUDE.md		CLAUDE.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
DEPENDENCY_UPGRADE_NOTES.md		DEPENDENCY_UPGRADE_NOTES.md
Dockerfile		Dockerfile
IMPLEMENTATION_DETAILS.md		IMPLEMENTATION_DETAILS.md
LICENSE		LICENSE
README.md		README.md
main.py		main.py
metadata.json		metadata.json
pytest.ini		pytest.ini
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
setup.py		setup.py

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DeltaFlow

Overview

Key Features

Smart Sync - Intelligent Incremental Synchronization

Auto-Schema - Zero-Configuration Schema Detection

Multi-Source Support

Enterprise Features

Quick Start

Prerequisites

5-Minute Setup

Installation

From Source

Docker Deployment

Usage Examples

PostgreSQL to BigQuery with Smart Sync

MongoDB to BigQuery with Auto-Schema

BigQuery Cross-Project Replication

Multi-Source Parallel Sync

Smart Sync Example with Scheduling

Configuration

Core Parameters

Smart Sync Parameters

Auto-Schema Parameters

Architecture

Deployment

Development Environment

Production Deployment

Network Configuration (VPC)

Monitoring and Troubleshooting

View Job Status

View Logs

Common Issues

Contributing

Development Setup

Adding New Data Sources

Code Style

License

Roadmap

Version 1.1 (Planned)

Version 1.2 (Future)

Version 2.0 (Vision)

Support and Community

Acknowledgments

Authors

About

Resources

License

Code of conduct

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