Data Platforms & AI Automation | SecureOps Systems Romania

Data Platform Provisioning

• Data Factory and Databricks configured via IaC (Bicep / Terraform)
• Workspaces, storage accounts, and data lakes (ADLS2)
• Encryption technologies: workspace, DBFS root, CMKs
• Network configuration: private endpoints, VNet integration
• Consistent environments: dev / test / UAT / production

Integrations & Configuration

• Data Factory linked services, credentials, and Managed Private Endpoints
• Integration with Key Vault / HSM, managed identities, and service principals
• Secret scopes and secure credential management
• Connectivity to data lakes and native or external services

Databricks & Compute

• Interactive clusters and job clusters
• SQL warehouses and performance tuning
• Code deployment: Python wheels, notebooks
• Automation via CLI and APIs

Governance & Security

• Unity Catalog and data-level access control
• Group and permission management
• Auditing, traceability, and access policies
• Securing data flows and identity integration

Pipelines & CI/CD

• CI/CD pipelines for data platforms
• Automated deployment for notebooks and configurations
• Versioning and change control
• Full SDLC/SSDLC automation

Observability & Operations

• Job and execution monitoring
• Centralized logging and telemetry analysis
• Data pipeline debugging and performance tuning
• Platform stabilization and continuous optimization

Data platforms engineered for production

Not just configuration or proof-of-concept — full implementation, integration, and automation for real, scalable, and well-governed data systems.

Common data platform challenges

• Manual and inconsistent deployments
• Lack of governance and access control
• Weak integration between services
• Lack of monitoring and difficult debugging

How we build platforms

• Full automation via IaC and CI/CD
• Secure integration across services and platforms
• Governance and access control from day one
• Observability and production stability

Case Study: Automated Data Platform on Azure

A company was using Azure Data Factory and Databricks without automation and with weak integration between services, leading to slow deployments and frequent production errors.

Problems

• Manual deployments
• Lack of governance
• Unstable integration

Solutions

• Full IaC (Bicep)
• CI/CD for the platform
• Secure integration

Results

• Automated deployments
• Increased stability
• Scalable platform

Data Platforms & AI