DevOps with Databricks, Terraform & GitHub Actions
- Chandan Kumar
- Aug 5
- 4 min read
Updated: Aug 9
In today’s fast-evolving cloud landscape, mastering DevOps practices across platforms like Databricks, Terraform, and GitHub Actions is no longer optional—it’s essential. During a recent interactive webinar, I walked participants through the practical steps of deploying Azure Databricks using Terraform, orchestrating jobs with GitHub Actions, and understanding the broader data engineering ecosystem.
Here’s a recap of the key takeaways, insights, and hands-on demos from the session
Slide Deck from the Webinar
Why Databricks?
Databricks is more than just a data platform—it’s a unified analytics engine built on Apache Spark that supports data engineering, machine learning, and analytics workflows. It’s especially powerful when integrated with cloud platforms like Azure, where it becomes a native service, simplifying deployment and scaling.
Key Concepts:
Schema-on-read vs. Schema-on-write: Databricks supports schema-on-read, allowing raw data ingestion without upfront transformation.
Delta Lake & ACID Compliance: Ensures data consistency and reliability, similar to traditional SQL databases.
Lakehouse Architecture: Combines the best of data lakes and warehouses, enabling flexible and scalable analytics.
DevOps Automation with Terraform
Infrastructure as Code (IaC) is the backbone of modern DevOps. In the demo, we used Terraform to automate the provisioning of Azure Databricks workspaces and deploy Python notebooks.
Highlights:
Two-part setup:
Infrastructure: Provisioning Databricks workspace via Azure APIs.
Application: Deploying notebooks and ML jobs using Terraform modules.
Multi-region DR Strategy: Using Terraform to replicate configurations across regions for business continuity.
Best Practices:
Avoid “ClickOps”—manual changes in the cloud console.
Use Git-based version control for all infrastructure changes.
CI/CD with GitHub Actions
GitHub Actions was used to trigger automated deployments of Databricks jobs. By storing secrets (like tokens and host URLs) securely in GitHub, we ensured a clean and secure pipeline.
Workflow:
Export Databricks token and host URL.
Use Terraform to deploy jobs from GitHub.
Monitor job status and validate deployment in Databricks workspace.
Real-World Case Study: Cost Optimization
We showcased a client project where a heavy ML workload was migrated from a VM-based setup to serverless Databricks jobs. The result? Over 90% cost savings.
Strategy:
Convert Python ML jobs into serverless Databricks jobs.
Schedule jobs using Cron expressions.
Use ephemeral compute resources to minimize idle costs.
DevOps Launchpad: Training & Mentorship
For those looking to break into DevOps, we introduced the DevOps Launchpad—a mentorship-driven program that partners with local employers to train and place candidates in real-world roles.
Program Features:
Tailored training for AWS, Azure, Kubernetes, Terraform, and more.
Resume and interview prep.
Real-world project experience.
Free and open-source curriculum.
Community Q&A Highlights
Q: Is the Databricks free account truly free? What are its limitations?
A: Yes, the Databricks community edition is completely free. However, it's limited to basic experimentation. You can't connect to custom S3 buckets or run large-scale jobs. It's ideal for learning and prototyping.
Q: What’s the difference between schema-on-read and schema-on-write?
A: Schema-on-write (used in traditional data warehouses) requires data to be structured before ingestion. Schema-on-read (used in data lakes and lakehouses like Databricks) allows raw data ingestion and applies schema during query time.
Q: How does Databricks architecture work in Azure?
A: Databricks uses a control plane managed by Databricks and a data plane within your cloud account. In Azure, Databricks is a native service, allowing seamless integration via Azure APIs and Terraform.
Q: What’s the benefit of using Terraform for Databricks deployment?
A: Terraform enables infrastructure-as-code, making deployments reproducible, version-controlled, and scalable. It avoids manual “ClickOps” and supports multi-region disaster recovery strategies.
Q: Can I deploy Databricks jobs using GitHub Actions?
A: Yes. You can automate job deployment using GitHub Actions by securely storing tokens and workspace URLs as secrets, and triggering Terraform workflows from your repo.
Q: What roles does the DevOps Launchpad program prepare you for?
A: The program targets roles like DevOps Engineer, Site Reliability Engineer (SRE), Cloud Engineer, and Support Engineer. It focuses on hands-on skills with tools like AWS, Azure, Kubernetes, and Terraform.
Q: Is it normal to feel overwhelmed by the number of tools in DevOps?
A: Absolutely. DevOps involves orchestration across many tools. The key is to pick one stack (e.g., AWS + Terraform + Kubernetes) and master it deeply rather than trying to learn everything at once.
Q: Should I get certifications to improve my chances of landing a job?
A: Certifications can help build confidence and structure learning, but they don’t guarantee jobs. Real-world projects, tailored resumes, and interview preparation are more impactful.
Q: What’s the best cloud platform to start with—AWS, Azure, or GCP?
A: All are great. AWS is the largest and most widely adopted. Azure is ideal if you come from a Microsoft background. GCP is smaller but strong in data and ML. Pick one and go deep.
Q: Should I learn Jenkins, GitLab, or GitHub for CI/CD?
A: GitHub is widely used and free. Jenkins is powerful for on-prem setups. GitLab is also popular. Choose one based on your target job environment, but GitHub is a safe starting point.
Q: Is Ansible worth learning for DevOps?
A: Only if you’re comfortable with Linux administration. Ansible is a configuration management tool best suited for managing large Linux server farms.
Q: How can I transition from a data analyst role to cloud/DevOps?
A: Build on your SQL and Python skills. Learn Git, cloud platforms (e.g., AWS or Azure), and orchestration tools like Databricks or Glue. Stitch your existing skills into cloud-native workflows.
Final Thoughts
DevOps is not just about tools—it’s about orchestration, automation, and delivering business value. Whether you're deploying Databricks jobs or building CI/CD pipelines, the key is to stay focused, pick a stack, and build real-world experience.
If you're interested in joining the next cohort or want personalized guidance, feel free to reach out via LinkedIn or email.
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