Predict Market Shifts Faster Using Draup Data in PostgreSQL / MySQL
Open-source and widely adopted. Draup data shares here enable direct SQL access for flexible queries, reporting, and integrations.
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Why Draup + PostgreSQL & MySQL
Draup delivers enriched sales intelligence directly into your PostgreSQL or MySQL databases, enabling teams to combine external account and buyer signals with internal CRM and pipeline data. This gives sales, RevOps, and analytics teams a unified foundation for faster insights, better forecasting, and smarter targeting without moving data between environments.
About PostgreSQL / MySQL
PostgreSQL and MySQL are widely adopted open-source relational databases. They provide flexibility, SQL-driven analytics, and robust integrations for reporting and custom applications.
How the Integration Works
- 1Stream Draup’s enriched account, buyer, and market intelligence flows directly into your data platform, aligning with existing GTM, revenue, and analytics pipelines.
- 2Enrich Datasets surface high-value signals, buyer intent, account readiness, competitive activity, hiring momentum, firmographics, and market shifts.
- 3Analyze Revenue, sales, and RevOps teams run real-time analytics, forecasting, and segmentation by blending Draup intelligence with internal CRM and pipeline data.
- 4Activate Insights power targeting, territory planning, forecasting, AI copilots, and GTM execution, helping teams prioritize the right accounts and close faster.
GTM Teams with PostgreSQL & MySQL + Draup see
- Deeper targeting driven by external buyer and account context.
- More accurate forecasting using real revenue signals.
- Stronger alignments between sales and analytics teams.
- Faster insights with internal +external data combined.
What You Can Do
- Prioritize high-growth accounts using intent and hiring signals.
- Build churn and upsell prediction models.
- Segment buyers dynamically with enriched data.
- Power cross-functional analytics from a single source of truth.
Draup Works Wherever Your Teams Work
Draup connects with 30+ CRMs and sales platforms, so your sellers, marketers, and RevOps leaders get intelligence in the tools they already use.
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Frequently Asked Questions
What type of data does Draup deliver into PostgreSQL/MySQL?
Draup provides structured buyer, account, and market intelligence designed to work seamlessly with relational databases.
How do teams typically use Draup data in databases?
Teams often combine Draup data with CRM and revenue data to power analytics, reporting, and predictive models.
Can Draup data support custom dashboards and BI tools?
Yes, Draup datasets are commonly used with BI and analytics tools layered on top of databases.
How is data freshness maintained?
Draup updates datasets on a regular cadence to reflect changes in markets, buyers, and accounts.
Is this suitable for AI and forecasting use cases?
Yes, the structured format supports revenue forecasting, churn modeling, and AI-driven analysis.
Impactful insights, delivered real-time
Access insights via API, custom data feeds, the Draup platform or using MCP
APIs & Integrations
Best for
Embedding live insights in workflows without storing data
- Native integrations with 33+ CRMs, like Salesforce, Hubspot, Microsoft Dynamics CRM, etc.
- Real-time access to critical data
- Enhanced security and data Integrity
- Efficient API performance with flexible limits
Custom Data Feed
Best for
Analytics at scale & joining Draup with internal data
- Highly customizable feeds for workflow needs
- Scheduled pushes to data lakes/warehouses (S3, ADLS, BigQuery, SFTP)
- Scalable use cases with the data
- Integrates with internal data assets for co-pilots/agents
Draup Platform
Best for
Fastest time-to-value,
no build required
- Ready-to-use UI with 200+ productized use cases & workflows
- Leverage visualizations & workflows to drive seller action with no overhead
- Enterprise controls: SSO, RBAC, governance
- Integrates UI & functionality into CRM apps
Model Context Protocol
Best for
Real-time Al workflows & LLM applications
- Native integration with Claude, OpenAl, and MCP-compatible Al tools
- Zero ETL, models query live data without pipelines or reindexing
- Governed access with token-based scopes, Pll masking, and audit trails
- Grounded, real-time data prevents LLMs from generating outdated or inaccurate insight
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