How Stacksync works for data teams

1. Connect Your Data Stack: Use Stacksync’s intuitive, no-code interface to link warehouses, databases, BI tools, and SaaS platforms in minutes—no custom code required.
2. Map and Sync Data: Select tables, fields, and sync rules. Stacksync handles schema mapping, transformations, and real-time, bidirectional updates automatically.
3. Automate Workflows and Alerts: Set up triggers for ETL jobs, data validation, and custom notifications. Automate remediation steps and keep stakeholders informed with real-time alerts.
4. Monitor, Govern, and Scale: Access dashboards and logs for pipeline health, enforce data governance policies, and scale integrations as your data footprint grows.

Key benefits

• Unified Data Across the Enterprise: Integrate and synchronize data from warehouses (Snowflake, BigQuery), databases (Postgres, SQL Server), SaaS apps, and internal tools for a single source of truth.
• Real-Time, Two-Way Sync: Instantly reflect data changes across all connected systems, ensuring analytics, dashboards, and stakeholders always have the latest information.
• No-Code Pipeline Automation: Build and orchestrate complex data workflows, automate ETL/ELT tasks, and trigger alerts or downstream processes, all without custom scripts.
• Data Quality and Consistency: Enforce validation, deduplication, and monitoring rules to ensure high-quality, reliable data flows across your stack.
• Scalable and Secure: Effortlessly handle high-frequency, high-volume data sync with enterprise-grade security, governance, and compliance.

Use cases

• Automated ETL/ELT Pipelines: Orchestrate and automate data ingestion, transformation, and loading between cloud warehouses, databases, and analytics platforms.
• Reverse ETL for Operational Analytics: Sync analytics-ready data from your warehouse back to CRMs, marketing, and support tools, enabling data-driven action across teams.
• Real-Time Data Monitoring and Alerting: Set up custom alerts and notifications for pipeline failures, data anomalies, or SLA breaches to ensure data reliability and rapid incident response.
• Data Quality Management: Automate validation, deduplication, and enrichment processes to deliver trusted data to BI tools and business users.
• Self-Service Data Integration: Empower analysts and business users to connect and sync data sources without engineering bottlenecks or IT tickets.