Databricks Jobs — Scheduling Batch Processing
🎬 Story Time — “The ETL That Never Slept…”
Nidhi, a data engineer at a logistics company, receives complaints from every direction.
Analytics team:
“Why is our daily ETL running manually?”
Finance:
“Why didn’t yesterday’s batch complete?”
Managers:
“Can’t Databricks run jobs automatically?”
Nidhi knows the truth:
Someone runs the ETL notebook manually every morning.
She smiles and opens Databricks.
“Time to put this into a Job and let it run like clockwork.”
That’s where Databricks Jobs come in — reliable, automated batch processing in the Lakehouse.
🚀 1. What Are Databricks Jobs?
Databricks Jobs allow you to schedule and automate:
- Notebooks
- Python scripts
- Spark jobs
- JAR files
- Delta Live Tables
- ML pipelines
- SQL tasks
Jobs ensure processing happens on schedule, with retries, alerts, logging, and monitoring — without human involvement.
🧱 2. Creating Your First Databricks Job
Nidhi starts with a simple daily ETL.
In the Databricks Workspace:
Workflows → Jobs → Create Job
She configures:
- 📘 Task: notebook path (e.g.,
/ETL/clean_orders) - ⚙️ Cluster: new job cluster (cost-optimized)
- 🕒 Schedule: daily at 1:00 AM
- 🔁 Retries: 3 attempts
- 🔔 Alert: email on failure
Within minutes — her ETL is automated.
🔧 3. Example: Notebook-Based ETL Job
The ETL notebook:
df = spark.read.format("delta").load("/mnt/raw/orders")
clean_df = df \
.filter("order_status IS NOT NULL") \
.withColumn("cleaned_ts", current_timestamp())
clean_df.write.format("delta").mode("overwrite").save("/mnt/clean/orders")
Databricks Job runs this nightly.
⏱️ 4. Scheduling Jobs
Databricks offers flexible scheduling:
🟦 Cron Schedule
0 1 * * *
🟩 UI-based Scheduling
- Daily
- Weekly
- Hourly
- Custom
🟧 Trigger on File Arrival (Auto Loader + Jobs)
Perfect for streaming-batch hybrid architectures.
🏗️ 5. Job Clusters vs All-Purpose Clusters
Nidhi must choose between:
✔ Job Cluster (recommended)
- Auto-terminated after job finishes
- Cheaper
- Clean environment per run
- Best for production
✔ All-Purpose Cluster
- Shared
- Not ideal for scheduled jobs
- More expensive
She selects job clusters to cut compute waste.
🔄 6. Multi-Step ETL With Dependent Tasks
A single Databricks Job can contain multiple tasks, such as:
- Extract
- Transform
- Validate
- Load into Delta
- Notify Slack
Example DAG:
extract → transform → load → validate → notify
📌 7. Retry Policies
Batch jobs fail sometimes.
Nidhi configures:
- 3 retries
- 10-minute delay
- Exponential backoff
Databricks handles failures automatically.
📤 8. Logging & Monitoring
Databricks Jobs provide:
- Run page logs
- Driver and executor logs
- Spark UI
- Execution graphs
- Cluster metrics
She can debug any failure easily.
📦 9. Real-World Enterprise Use Cases
⭐ E-commerce
Nightly ETL loading sales, product, and customer data.
⭐ Finance
Batch jobs calculating daily P&L and risk metrics.
⭐ Manufacturing
Daily IoT ingestion and device telemetry cleaning.
⭐ Logistics
Route optimization pipelines.
⭐ SaaS Platforms
Customer-level usage aggregation.
🧠 Best Practices
- Use job clusters for cost efficiency
- Keep each task modular
- Add alerts for failures
- Store logs in DLT + Delta tables
- Use retries for robustness
- Use version-controlled notebooks/scripts
- Document every pipeline task
🎉 Real-World Ending — “The Batch Runs Automatically Now”
With Databricks Jobs:
- No more manual ETL runs
- No more failures unnoticed
- Costs reduced by 35% with job clusters
- Alerts keep teams informed
- Nidhi sleeps peacefully
Her manager says:
“This is production-grade analytics. Our pipelines finally look professional.”
📘 Summary
Databricks Jobs enable:
-
✔ Automated scheduling
-
✔ Reliable batch processing
-
✔ Multi-task workflows
-
✔ Alerts, retries, logging
-
✔ Cost-effective orchestration
A fundamental building block for production data pipelines on Databricks.
👉 Next Topic
Multi-Task Job Workflows — Dependencies Across Tasks