Accelerate Sales Analytics with Draup Data in Microsoft Azure Blob Storage

Flexible, cost-effective storage for unstructured data. Draup delivers insights seamlessly across your Azure environment.

Why Draup + Azure Blob Storage?

Draup delivers structured sales, buyer, and market intelligence into Azure Blob Storage, giving teams a secure staging layer that can feed analytics, data warehouses, ML pipelines, and BI tools without lock-in or latency concerns. It’s ideal for flexible enterprise workflows with strict governance.

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About Microsoft Azure Blob Storage

Azure Blob Storage is a cost-effective, massively scalable storage for unstructured data. It integrates deeply with the Azure ecosystem, supporting enterprise analytics and ML workloads.

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.

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GTM Teams with Azure Blob + Draup see

  • Secure, scalable access to revenue datasets.
  • Flexibility for multiple downstream consumers.
  • Better governance and cost control.
  • Simplified analytics and model pipelines.

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What You Can Do

  • Stage enriched buyer & account datasets for downstream use.
  • Feed cloud warehouses, lakes, and data platforms.
  • Build ETL/ELT flows into analytics stacks.
  • Use Blob as the central hub for GTM intelligence.

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.

Frequently Asked Questions

Why use Azure Blob Storage for Draup data?

Blob Storage provides a secure and flexible foundation for storing and distributing Draup datasets.

How do teams typically consume Draup data from Blob Storage?

Data is commonly ingested into warehouses, analytics tools, or ML pipelines.

Can multiple systems access the same datasets?

Yes, Blob Storage supports flexible access for different downstream consumers.

Is this suitable for regulated or enterprise environments?

Yes, Blob Storage supports strong access controls, encryption, and compliance standards.

Can this support AI and advanced analytics?

Absolutely, Blob Storage is often used as a staging layer for AI and analytics workflows.

Impactful insights, delivered real-time

Access insights via API, custom data feeds, the Draup platform or using MCP

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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
Learn more
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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
Learn more
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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
Book a demo

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
Learn more

Data Trust & Methodology at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

1

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

2

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

3

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company. Learn more

4

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

5

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

6

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.

SOC 2
GDPR
ISO
EAIGG

Ethical AI, Backed by Global Standards

Draup, an AI-first company, upholds global standards like SOC 2, GDPR, and ISO 27001. As an EAIGG member, we audit for bias and build trustworthy AI.

Ready to see results?

Drive better decisions with unmatched, real-time data & agentic intelligence

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