Here’s a detailed comparison of Hadoop vs AWS Glue vs Databricks vs Snowflake, focusing on architecture, processing, scalability, cost, and fault tolerance — perfect for interviews and architectural understanding 👇
🧩 1. Overview of Hadoop, AWS Glue, Databricks, and Snowflake
| Platform | Type | Primary Purpose |
|---|---|---|
| Hadoop | On-premise / Open-source big data framework | Distributed data storage + batch processing |
| AWS Glue | Serverless ETL service | Data integration & transformation in AWS |
| Databricks | Unified Data + AI platform (Spark-based) | Scalable ETL, analytics, ML, and AI |
| Snowflake | Cloud data warehouse | High-performance analytics & data sharing |
⚙️ 2. Architecture Comparison: Storage & Compute
| Feature | Hadoop | AWS Glue | Databricks | Snowflake |
|---|---|---|---|---|
| Core Engine | MapReduce / Spark | Apache Spark | Apache Spark + Delta Lake | Proprietary SQL engine |
| Storage | HDFS | S3 (or other AWS storage) | S3 / ADLS / GCS | Cloud-managed (S3 / GCS / Azure Blob) |
| Compute-Storage Separation | ❌ Tight coupled | ✅ Yes | ✅ Yes | ✅ Yes |
| Serverless | ❌ No | ✅ Fully serverless | ⚙️ Semi-serverless (managed cluster) | ✅ Fully serverless |
| Processing Type | Batch | Batch / ETL | Batch + Streaming + ML | Analytical SQL (batch + micro-batch) |
🚀 3. Parallel Processing & Query Optimization
| Feature | Hadoop | AWS Glue | Databricks | Snowflake |
|---|---|---|---|---|
| Parallelism | MapReduce (block-based) | Spark executors | Spark optimized (Photon engine in runtime) | Multi-cluster compute |
| Optimization | Manual tuning (YARN configs) | Automatic scaling | Catalyst + AQE (Adaptive Query Execution) | Automatic query optimization |
| Caching | Disk-based | In-memory (Spark) | In-memory + Delta Cache | Automatic result cache |
| Data Skew Handling | Manual | Partial | Automatic AQE handles skew | Automatic |
🧮 4. Scalability and Auto-Scaling Features
| Feature | Hadoop | AWS Glue | Databricks | Snowflake |
|---|---|---|---|---|
| Scale Type | Horizontal (add nodes) | Auto-scaled by AWS | Auto-scale / cluster pools | Auto-scale warehouses |
| Elasticity | ❌ Manual scaling | ✅ Automatic | ✅ Cluster autoscaling | ✅ Multi-cluster auto-scaling |
| Max Concurrency | Limited by cluster | High | High | Very high (multi-cluster warehouses) |
🧠 5. Fault Tolerance, Recovery & Time Travel
| Feature | Hadoop | AWS Glue | Databricks | Snowflake |
|---|---|---|---|---|
| Fault Tolerance | Yes (data replication in HDFS) | Yes (Spark job retries) | Yes (checkpointing, retries) | Yes (replication, time travel) |
| Job Recovery | Manual or via YARN | Automatic retry on node failure | Checkpointing in Spark Streaming / Delta | Built-in failover, retry at query level |
| Data Recovery | Replication factor | From S3 (durable storage) | Delta Lake time travel | Time Travel + Fail-safe |
| Checkpointing | Limited | Supported | Strong support (Streaming) | Not needed (managed snapshots) |
💸 6. Cost, Maintenance, and Serverless Options
| Feature | Hadoop | AWS Glue | Databricks | Snowflake |
|---|---|---|---|---|
| Infrastructure | User-managed | AWS managed | Managed by Databricks | Fully managed |
| Pricing Model | Hardware + Ops cost | Pay per DPU-second | Pay per compute-hour | Pay per warehouse usage (credits) |
| Maintenance | Manual (install/patch) | None | Minimal | None |
| Ease of Use | Complex | Easy (no infra mgmt) | Developer-friendly | Very easy (SQL-focused) |
📊 7. Use Cases: ETL, Analytics, Machine Learning
| Platform | Ideal Use Cases |
|---|---|
| Hadoop | Legacy big data storage and batch ETL |
| AWS Glue | Serverless ETL jobs, metadata catalog, data prep for analytics |
| Databricks | Unified data engineering, streaming, ML/AI, Delta Lake |
| Snowflake | High-performance data warehouse, BI, ELT workloads |
🧩 8. Key Takeaways: Choosing the Right Platform in 2025
| Scenario | Best Platform |
|---|---|
| Large-scale batch processing | Databricks or Hadoop |
| Simple serverless ETL pipeline | AWS Glue |
| Machine Learning + Streaming | Databricks |
| Fast SQL analytics / BI dashboards | Snowflake |
| Legacy on-prem data lake | Hadoop |
🧠 9. Interview Summary (Quick Recall)
| Concept | Hadoop | Glue | Databricks | Snowflake |
|---|---|---|---|---|
| Engine | MapReduce | Spark | Spark | SQL Engine |
| Managed | ❌ | ✅ | ✅ | ✅ |
| Serverless | ❌ | ✅ | ⚙️ Semi | ✅ |
| Scalability | Manual | Auto | Auto | Auto |
| Fault Tolerance | HDFS replication | Retry | Delta checkpoint | Time travel |
| ML Support | No | Limited | ✅ Yes | ❌ No |
| Performance | Low (disk I/O) | Medium | High (Photon) | Very High |
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