Welcome to Day 15 of the Spark Mastery Series. Until now, we focused on Spark internals and APIs.
From today, we step into real-world production data engineering.
Let’s understand how Spark actually runs in the cloud.
🌟 Why Spark on Cloud?
Modern data platforms demand:
- Elastic compute
- Fast cluster provisioning
- Managed infrastructure
- Integration with cloud storage
- Lower operational overhead
This is exactly what cloud Spark services provide.
🌟 Spark on GCP — Dataproc
Dataproc is Google’s managed Spark service.
Why teams use Dataproc:
- Spin up clusters in minutes
- Integrates with GCS, BigQuery, IAM
- Cheaper than long-running VMs
- Supports autoscaling
Typical ETL Flow:
- Data lands in GCS
- Dataproc Spark job processes data
- Output written to GCS / BigQuery
🌟 Spark on Databricks
Databricks is a Spark-first Lakehouse platform.
What makes it popular:
- Optimized Spark runtime
- Delta Lake built-in
- Excellent notebooks
- Easy collaboration
- Built-in job scheduling
Databricks is extremely popular in:
- Product companies
- ML-heavy teams
- Lakehouse architectures
🌟 Spark Cluster Types Explained
🟢 Job Clusters
- Created for a job
- Destroyed after job finishes
- Best for production pipelines
🔵 All-Purpose Clusters
- Shared clusters
- Used for development
- Should NOT be used for production jobs
🌟 Client vs Cluster Mode
Mode | Driver Location | Use
Client | Local machine | Testing
Cluster| Worker node | Production
Always use cluster mode in production.
🌟 Cost Optimization (VERY IMPORTANT)
Bad Spark jobs cost money 💸
Best practices:
✔ Auto-terminate idle clusters
✔ Use spot/preemptible workers
✔ Optimize partition size
✔ Use Parquet/Delta
✔ Avoid UDFs & shuffles
🌟 Real-World Decision Guide
Choose Dataproc if:
- You are on GCP
- Want infra-level control
- Need cheaper batch jobs
Choose Databricks if:
- You want faster development
- Heavy Delta Lake usage
- ML pipelines
🚀 Summary
We learned:
- How Spark runs in the cloud
- Dataproc vs Databricks
- Cluster types & job lifecycle
- Client vs cluster mode
- Cost optimization strategies
Follow for more such content. Let me know if I missed anything in comments. Thank you!!
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