Hadoop vs AWS Glue vs Databricks vs Snowflake: Complete 2025 Comparison
Srini
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
