BricksOps – Databricks Operations, Governance & Optimization

Overview: BricksOps is a Coforge Data Cosmos accelerator under the Pulsar suite that transforms Databricks operations, governance analysis, and platform observability into a unified and scalable workflow. It enables platform, engineering, and governance teams to collect workspace inventory, correlate source code and CI/CD activity with Databricks job execution timelines, detect governance signals, and generate cluster optimization recommendations — all from a single control plane. Instead of fragmented tools, manual exports, and disconnected reporting, BricksOps consolidates the operations intelligence lifecycle into one governed platform.

Why BricksOps Exists: Many organizations manage Databricks operations through disconnected workflows and manual processes leading to time-consuming metadata extraction, slow root-cause analysis, reactive cluster optimization, and high effort for governance reviews and audits. BricksOps addresses these challenges by consolidating inventory collection, operational timelines, governance analysis, and optimization planning into a single governed workflow.

Core Capabilities:

1. Automated Databricks Inventory Collection Section-based workspace inventory collection with targeted or full scans, asynchronous execution, status tracking, retry-resilient workflows, and snapshot-based storage for faster retrieval and reporting.

2. Code Repository, CI/CD & Databricks Timeline Correlation Source code commit tracking, pull request and workflow monitoring, CI/CD execution analysis, workflow log parsing, Terraform deployment signal extraction, and Databricks job lifecycle correlation with confidence and provenance indicators.

3. Governance and Audit Analysis Unified job timeline views, change traceability, pull request and deployment context visualization, runtime behavior correlation, and audit-ready evidence generation. Quickly answers: What changed? Who made the change? Which deployment introduced the issue? Which jobs were impacted?

4. Cluster Advisory & Optimization Planning Cluster configuration analysis, recommendation generation, usage pattern evaluation, optimization scenario creation, adoption planning, and rollback-aware operational planning.

5. Multi-View Operations Portal Executive dashboards, KPI and health summaries, inventory exploration, job analysis, run timeline analysis, governance traceability, cluster recommendation portals, and comparative analysis pages.

Internal Roles Architecture: • Collector: Maintains Databricks workspace inventory metadata • Correlator: Ingests repository/CI/CD evidence and maps activities to Databricks job timelines • Advisor: Builds optimization recommendations and cluster improvement scenarios • Planner: Generates operational adoption plans and rollout strategies

Where Teams Save Time: • Inventory Collection: Hours across multiple APIs → minutes via automated scans • Repository-to-Job Correlation: Multi-hour investigation → minutes via automated timeline correlation • Governance Reporting: Multi-day preparation → immediate dashboard visibility • Cluster Recommendation Preparation: Manual spreadsheets → structured recommendations auto-generated

Common Use Cases: Databricks operations, compliance & audit preparation, incident investigation, post-release validation, knowledge transfer, FinOps & cost optimization, cluster efficiency improvement.

Cloud-Native Deployment on AWS: Deployed on Amazon EKS with containerized backend (Python 3.11, Flask) and frontend (React 18, Vite). Amazon S3 for artifact storage. Databricks on AWS integration for workspace connectivity. GitHub Actions integration for CI/CD correlation. Amazon CloudWatch for monitoring.