AI‑Powered Data Engineering on Azure Databricks: 1‑Hour Assessment & PoC

Transform your Microsoft Azure data platform maintenance from manual and reactive to automated AI‑driven operations.

DS Stream offers a professional services engagement on Microsoft Azure to evaluate your current data engineering bottlenecks, followed by a tailored 8‑Week Proof of Concept (PoC) proposal. This offer is delivered as a remote or on‑site professional services engagement by DS Stream consultants.

DS Stream provides a professional, complimentary 1‑Hour Assessment to help you transform data platform maintenance from manual and reactive to automated, AI‑driven operations on Azure Databricks. Our AI Powered Data Engineering solution empowers organizations to significantly reduce operational costs and manual workloads associated with maintaining data platforms (especially Azure Databricks). By leveraging advanced LLM and AI Agents, we automate data validation, incident management, and bug-fixing routines.

Why AI Powered Data Engineering?
Modern data ecosystems face significant operational bottlenecks:

• Data Drift: Frequent changes within unstructured data sources and unexpected partial data gaps.
• Silent Errors: Hidden errors in transformed data resulting in low trust regarding BI reports quality.
• High Maintenance Costs: Expensive and time-consuming incident management.
• Operational Overload: Repetitive manual validation tasks blocking new development.

Our AI Powered Data Engineering solution empowers organizations to significantly reduce operational costs and manual workloads associated with maintaining data platforms on Microsoft Azure (especially Azure Databricks). By leveraging advanced large language models (LLMs) and AI agents, we automate data validation, incident management, and bug‑fixing routines.

Core Capabilities (How it works during PoC)
During the PoC, DS Stream implements our agentic workflow based on two main pillars, seamlessly integrated with your existing tools (Databricks, JIRA, GitHub, Teams):

1. Automated Data Validation & Profiling

• Smart Profiling: AI agents automatically build column profiles based on valid reference data.
• Continuous Verification: Every new batch of data is verified. Anomalies are flagged, quarantined, and trigger automated alerts.
• Schema Evolution Management: Agents detect new or missing columns, intelligently matching renamed columns.

2. Intelligent Error Classification & Automated Fixes

• Root-Cause Classification: Agents instantly classify pipeline failures into data issues, infrastructure overloads, or unhandled code exceptions.
• Code Fix Generation: For code errors, the agent creates a draft Pull Request (PR) in GitHub for the engineer to review.
• Continuous Learning: The agent uses engineer feedback (e.g., rejected PRs) to learn and improve future recommendations.

Expected Business Outcomes (KPIs after PoC)

• 40-60% reduction in manual validation and repetitive tasks (approx. -30% on-call time).
• Bug-fixing time shortened from an average of ~2 days to under 1 hour.
• Immediate drift detection blocking "silent errors" before they affect business reports.

Engagement Model (Your Journey)
Step 1: Free 1-Hour Assessment

• Discussion of your current data pipeline infrastructure and pain points.
• Live demonstration of the AI Powered Data Engineering workflow.

Step 2: 8-Week Proof of Concept (Optional)

• Weeks 1-2: Design, setup, and baseline measurement.
• Weeks 3-5: Deployment of AI agents for automated profiling and error classification.
• Weeks 6-7: End-to-end testing, tuning error classification precision and PR accuracy.
• Week 8: Final presentation of measurable KPIs and scaling roadmap.