Part 2: End-to-End Lakeflow Job Pipeline in Databricks — From Ingestion to DAG Scheduling

In this part, we dive into the full ETL pipeline using Databricks Lakeflow Jobs — starting from ingesting raw voter data, transforming it through silver and gold layers, and automating every task using DAG-based orchestration, scheduling, and alerts. This hands-on walkthrough shows how to build production-ready data workflows inside the Databricks Lakehouse Platform. Step 3: Ingest Raw Voter Data and Register as Delta Tables With GCS access verified, I loaded three Parquet datasets—voter_demographics, voting_records, and election_results—into Databricks and converted them into Delta tables as the bronze layer. These are stored in Unity Catalog under the election_data schema, ensuring durability, auditability, and support for downstream ETL tasks within the Lakehouse Architecture.  

Step 4: Merge Voter and Election Info To enrich the raw data, I joined voter_demographics with election_results on the region column, combining voter profiles with regional outcomes. The result forms the silver layer—a cleaned, contextual dataset ready for deeper analysis.  

Step 5: Remove Data Noise with Cleansing and Deduplication Removed incomplete records, blank regions, and duplicates to create a refined, analysis-ready cleaned_election_datatable..  

Step 6: Isolate South Region Data for Targeted Insights To focus on a specific area, I filtered the data for the South region, creating the filtered_south_region table — a lighter, targeted dataset ideal for regional analysis, reporting, or dashboards.  

Step 7: Create a Lakeflow Job Workflow in Databricks Databricks Lakeflow Jobs organize tasks using DAG Orchestration. My ETL pipeline runs every 15 minutes with built-in dependencies and email alerts for monitoring.   Open Jobs Interface in Databricks Jobs & Pipelines → Jobs → Supports DAG Orchestration for Lakeflow in Databricks.  

Create the Lakeflow Job in DatabricksCreated a Lakeflow Job called voter_data_etl to orchestrate the pipeline. The job runs a notebook task on a selected cluster, with email alerts set for start, success, and failure — forming the first node in the DAG.  

Step 8: Add Dependent Tasks to Build the DAG inside the Lakeflow Job After creating the main job, I added tasks with dependencies to follow a DAG flow — each task runs in order, triggered by the one before it. DAG structure:

• voter_data_etl → root task
• load_voter_data → depends on voter_data_etl
• clean_voter_data → depends on load_voter_data
• save_to_delta → depends on clean_voter_data

Each task is executed in a separate notebook, representing a stage in the ETL pipeline.  

Step 9: Schedule, Email, and Monitor1.Schedule the Lakeflow Job Scheduled the Lakeflow Job to run automatically every 15 minutes, starting on the hour — ensuring consistent and timely data updates.  

2.Set Up Email NotificationsI configured email alerts in the job settings to get notified on start, success, and failure — making it easy to monitor Scheduled Workflows.  

3.Monitor Scheduled Lakeflow Job RunsThe Lakeflow Job runs every 15 minutes with Real-Time Triggers, with Databricks showing real-time task statuses — Pending → Running → Succeeded — and timeline views to track execution and dependencies.  Timeline view showing successful Lakeflow Job runs at 5:00 PM and 5:15 PM.  

Bronze, Silver, and Gold Layers in a Lakeflow Pipeline

To keep the pipeline organized, a three-layer model is used:

• Bronze (Raw Data): Ingested as-is from the source for traceability and recovery.

• Silver (Cleaned Data): Joined, cleaned, and enriched for analysis.

• Gold (Curated Data): Filtered and refined for reporting and decision-making — e.g., South region insights.

Each layer builds on the previous, managed through Lakeflow Job orchestration.

Lakeflow UI Availability Note

Note: DAG orchestration is available in the Lakeflow Jobs interface. However, the full visual UI with drag-and-drop features may not be supported in Community or Free Trial editions. Users can still create DAG-style jobs with dependencies and alerts using the standard interface. Full access requires a premium or enterprise-tier workspace. Why Lakeflow Jobs Matter Lakeflow Jobs represents a major step forward in how Databricks handles workflow orchestration. With DAG-style dependencies, built-in scheduling, retries, and alerting — all within a unified interface — Lakeflow makes complex pipelines easy to build, run, and monitor.Whether for ETL, machine learning, or reporting, Lakeflow Jobs offer the flexibility and automation needed for modern, production-grade data workflows.Ready to explore Lakeflow Jobs? Try building your first scheduled pipeline using Databricks Free Trial on GCP and experience streamlined orchestration firsthand.