Secure Coding for Databricks – SQL & PySpark (Instructor-Led, Hands-On)

This hands-on secure coding course for Databricks is designed for data engineers, analysts, DevOps professionals, cloud architects, and security-focused developers who work with big data on the Databricks platform. The course delivers practical training on how to build secure, scalable, and compliant data pipelines using both SQL and Python in Databricks. Participants will gain in-depth knowledge of platform-level security features, as well as best practices for writing injection-resistant SQL queries and secure Python UDFs within notebooks and job workflows.

As organizations migrate to cloud-native platforms, the risks of code injection, misconfigured secrets, and data leakage increase. This course helps you understand and implement defensive coding techniques to mitigate threats such as SQL injection attacks, cross-site scripting (XSS), and hardcoded credentials. You’ll also explore tools like the Databricks Secrets API, Unity Catalog row/column-level security, and environment-based configuration to reduce the risk of exposing sensitive data or access tokens.

You’ll learn how to implement parameterized SQL queries, validate and sanitize user input using Python’s re module, and use encoding libraries like html.escape and bleach to prevent output-based injection vectors. The course also addresses infrastructure-level protections, including cluster ACLs, private networking (VPC/PrivateLink), library whitelisting, and secure initialization scripts. Key distinctions between salting vs hashing techniques for protecting personally identifiable information (PII) are discussed, alongside techniques like pseudonymization and anonymization to align with GDPR, CCPA, and HIPAA standards.

Real-world case studies and anti-pattern examples are covered throughout, helping learners identify common pitfalls like hardcoded secrets, over-permissioned clusters, and unsafe string concatenation in SQL or Python. You’ll also practice setting up Unity Catalog governance features with secure table access, masking functions, and auditing through Databricks audit logs. A dedicated section on vulnerability management shows how to scan for risks using open-source tools like Snyk and detect secrets leaks or dependency issues within notebooks.

With growing demand for secure data engineering skills, mastering Databricks security best practices sets professionals apart in roles like Data Engineer, Cloud Security Analyst, DevOps Engineer, and Site Reliability Engineer (SRE). This course provides the knowledge and confidence to deploy secure workloads in Databricks, enabling your team to develop secure, high-trust analytics platforms that meet both operational and regulatory requirements.

Whether you're preparing for secure cloud certifications, migrating regulated workloads to Databricks, or just want to eliminate security vulnerabilities from your data pipelines, this is the most complete Databricks secure coding course using Python and SQL available. Join today and future-proof your coding practices.

🔐 Key Learning Outcomes

• Build SQL queries and PySpark UDFs resistant to injection attacks
• Apply secrets management and encryption using Databricks APIs
• Protect sensitive data using Unity Catalog and platform features
• Understand attack vectors in notebooks and Python scripts
• Detect and scan for vulnerabilities in code and dependencies

📘 What You'll Learn

1. Introduction to Databricks Security
   • Shared Responsibility Model
   • Databricks Security Architecture
   • Top Security Risks in Data Engineering
   • Security Best Practices Lifecycle
2. Platform Security Fundamentals
   • Workspace Access Controls (Users, Service Principals)
   • Cluster Policies, Job ACLs, Notebook Permissions
   • Unity Catalog: Governance, GRANT/REVOKE, Row/Column Masking
3. Secure SQL Development
   • Understanding SQL Injection Attacks
   • Parameterized Queries & Dynamic SQL Safety
   • Column Encryption & View-Based Masking
   • Input Escaping & Output Encoding
4. Secure Python or PySpark Development
   • Secrets API, Environment Variables
   • UDF Input Validation, Output Sanitization
   • Regex Safety, html.escape, bleach module
   • Safe DataFrame operations (PII filtering, encryption)
5. Infrastructure Hardening + Hashing & Salting
   • Library Whitelisting, Init Scripts
   • VPC, Firewall Rules, PrivateLink
   • Salting vs Hashing: Definitions, Techniques, Use Cases
6. Data Protection Techniques
   • TLS Encryption, Managed Keys
   • Anonymization & Pseudonymization
7. Vulnerability Management
   • Snyk, OSS Dependency Scanning
   • SAST for Python/SQL, Secrets Detection
8. Audit & Compliance
   • Audit Logs, Unity Catalog Tags for Data Classification
   • Compliance: GDPR, CCPA, HIPAA Considerations
9. Anti-Patterns & Case Studies
   • Hardcoded Secrets, Overprivileged Clusters
   • Real Breach Examples and Lessons Learned
10. 🔧 Hands-On Labs
    • SQL Injection Mitigation & Parameterization
    • Secrets Management & Rotation using Secrets API
    • Config Management (requirements.txt, env vars, config files)
    • Row + Column-Level Security using Unity Catalog
    • Vulnerability Scanning with Snyk and open-source tools
    • Pseudonymization, Salting, Hashing in Python
    • Eval/Exec Exploits: Why to Avoid & Safer Alternatives

👥 Who Should Enroll?

• Data Engineers & Python Developers working in Databricks
• Security Engineers supporting analytics environments
• DevOps/SREs managing secure infrastructure and notebooks
• Analysts and ML Engineers handling sensitive data

📅 Course Format & Delivery

• 💻 Live Instructor-Led (Online or Onsite)
• 🛠️ Labs with Real Databricks Workspace & Unity Catalog
• 📚 Includes Notebooks, Checklists, Secure Coding Templates
• 📈 Guided Audit of Your Codebase & Workspace (Optional)

Ready to secure your Databricks workflows? Learn secure coding techniques that align with enterprise-grade governance and real-world threat models.

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