Hadoop vs Spark
(Real-world decision table used by architects at FAANG, banks, and cloud providers)
Hadoop vs Spark
Hadoop vs Spark – The Ultimate 2025 Comparison
(Real-world decision table used by architects at FAANG, banks, and cloud providers)
| Category | Hadoop (MapReduce + HDFS + YARN) | Apache Spark (on YARN, K8s, or standalone) | Winner in 2025 |
|---|---|---|---|
| Processing Model | Batch only (MapReduce v1/v2) | Unified: Batch + Streaming + SQL + ML + Graph in one engine | Spark |
| Speed (same hardware) | 100–150 MB/s per core (disk-based) | 10–100 GB/s per core (in-memory) → 10–100× faster | Spark |
| Latency | Minutes to hours | Sub-second (Structured Streaming) | Spark |
| Programming Paradigm | Java MapReduce (verbose), Streaming (Python/Java) | Scala / Python / Java / R / SQL (DataFrame = SQL + Pandas-like) | Spark |
| Ease of Use | Extremely hard (50 lines of Java for WordCount) | 5 lines of Python/Scala | Spark |
| Real-time / Streaming | None native (only via Storm, Flink on YARN) | First-class Structured Streaming (exactly-once) | Spark |
| Machine Learning | None (you write MapReduce ML from scratch) | MLlib, Spark ML Pipelines, Koalas/Pandas API on Spark | Spark |
| Interactive Analytics | Impossible (no SQL | Spark SQL, Databricks, notebooks → instant | Spark |
| Fault Tolerance | Excellent (HDFS replication + lineage) | Excellent (RDD/DataFrame lineage) | Tie |
| Storage Cost | Cheap (HDFS on HDD, 3× replication) | Expensive if all in-memory, cheap on Delta Lake + disk | Hadoop (raw) |
| Maturity in Enterprises | 15+ years, runs 70% of world’s data lakes | 10+ years, runs 90% of new workloads | Context |
| **Still runs in production 2025? | Yes — millions of nightly batch jobs in banks, telcos, gov | Yes — everything new + most old jobs migrated | Both |
| Operational Complexity | High (NameNode HA, ZooKeeper, Kerberos) | Lower (especially on Kubernetes or Databricks) | Spark |
| Ecosystem (2025) | Hive, Pig, HBase, Oozie (many dying) | Delta Lake, Iceberg, Hudi, Kafka, Flink, Trino, dbt, MLflow | Spark |
| Cloud Support | EMR, HDP, CDP (still used) | Databricks, Snowflake, BigQuery, Synapse, EMR, GCP Dataproc | Spark |
| Cost on Cloud (same data) | Higher (more nodes, slower jobs) | Lower (fewer nodes, faster jobs) | Spark |
| GPU / Modern Hardware | Possible but clunky | Native RAPIDS, Spark + CUDA, GPU scheduling | Spark |
Performance Head-to-Head (Real Benchmarks 2025)
| Workload | Hadoop MapReduce | Spark 3.5 (on YARN) | Speedup |
|---|---|---|---|
| Terasort 100 TB | ~3–4 hours | 12–18 minutes | ~15× |
| TPC-DS 10 TB (SQL) | 6+ hours (Hive) | 8–15 minutes (Spark SQL) | ~40× |
| ML Training (Random Forest) | Days (custom MR) | ~30–60 min (MLlib) | 50×+ |
| Streaming Kafka → Dashboard | Not possible | <1 second latency | ∞× |
When Hadoop (MapReduce) Still Wins in 2025 (Yes, it happens!)
| Scenario | Why Hadoop Wins |
|---|---|
| Regulated industries with 10+ year audit trails | MapReduce jobs unchanged since 2012 = zero risk |
| Extremely cheap storage (petabytes on HDD) | HDFS + Erasure Coding cheaper than cloud lakes |
| COBOL → Hadoop nightly batch (banks) | No need to rewrite |
| Legal hold / immutable data retention | HDFS WORM + Ranger |
When Spark Wins (99% of new projects)
| Scenario | Reality in 2025 |
|---|---|
| Lakehouse (Delta/Iceberg/Hudi) | Spark is the only write engine |
| Real-time anything | Structured Streaming dominates |
| Data Science / ML / GenAI | Spark + GPUs + Pandas API |
| Cost optimization on cloud | Spark finishes in minutes → lower $ |
| Modern stack (dbt, Airflow, Trino) | All built around Spark |
Decision Matrix – What Should You Choose in 2025?
| Your Situation | Choose | Recommendation |
|---|---|---|
| New project, cloud or on-prem | → Use Spark (Delta Lake) | Spark 100% |
| Existing massive Hadoop batch cluster | → Keep Hadoop for batch, add Spark alongside | Hybrid |
| Need sub-second streaming + ML | → Spark Structured Streaming + MLlib | Spark |
| Regulated bank with 1000 MapReduce jobs | → Don’t touch — run as-is | Hadoop (legacy) |
| Building a modern data platform | → Spark + Iceberg/Delta + Trino + dbt | Spark ecosystem |
Bottom Line – 2025 Reality
| Statement | Truth in 2025 |
|---|---|
| “Hadoop is dead” | False — HDFS + YARN still run >60% of world’s data |
| “No one writes MapReduce anymore” | True for new code — but old code runs forever |
| “Spark replaced Hadoop” | Partially true — Spark replaced MapReduce engine, but runs on YARN/HDFS |
| Best architecture in 2025 | Spark + Delta Lake/Iceberg on YARN or Kubernetes or Cloud storage |
Verdict:
Spark won the war for processing.
Hadoop (HDFS + YARN) still wins the storage and multi-tenancy war in many enterprises.
Most modern clusters in 2025 are actually Spark on YARN or Spark on Kubernetes — not Hadoop vs Spark, but Hadoop AND Spark**.
Want the next step?
- “Show me a real migration plan from Hadoop MapReduce to Spark”
- “Best practices for running Spark on YARN in 2025”
- “Spark on Kubernetes vs YARN comparison”
Just say the word — I’ll give you the full migration playbook used at Netflix, Uber, JPMorgan.
Last updated: Nov 30, 2025
Hadoop vs Spark
(Real-world decision table used by architects at FAANG, banks, and cloud providers)
Hadoop vs Spark
Hadoop vs Spark – The Ultimate 2025 Comparison
(Real-world decision table used by architects at FAANG, banks, and cloud providers)
| Category | Hadoop (MapReduce + HDFS + YARN) | Apache Spark (on YARN, K8s, or standalone) | Winner in 2025 |
|---|---|---|---|
| Processing Model | Batch only (MapReduce v1/v2) | Unified: Batch + Streaming + SQL + ML + Graph in one engine | Spark |
| Speed (same hardware) | 100–150 MB/s per core (disk-based) | 10–100 GB/s per core (in-memory) → 10–100× faster | Spark |
| Latency | Minutes to hours | Sub-second (Structured Streaming) | Spark |
| Programming Paradigm | Java MapReduce (verbose), Streaming (Python/Java) | Scala / Python / Java / R / SQL (DataFrame = SQL + Pandas-like) | Spark |
| Ease of Use | Extremely hard (50 lines of Java for WordCount) | 5 lines of Python/Scala | Spark |
| Real-time / Streaming | None native (only via Storm, Flink on YARN) | First-class Structured Streaming (exactly-once) | Spark |
| Machine Learning | None (you write MapReduce ML from scratch) | MLlib, Spark ML Pipelines, Koalas/Pandas API on Spark | Spark |
| Interactive Analytics | Impossible (no SQL | Spark SQL, Databricks, notebooks → instant | Spark |
| Fault Tolerance | Excellent (HDFS replication + lineage) | Excellent (RDD/DataFrame lineage) | Tie |
| Storage Cost | Cheap (HDFS on HDD, 3× replication) | Expensive if all in-memory, cheap on Delta Lake + disk | Hadoop (raw) |
| Maturity in Enterprises | 15+ years, runs 70% of world’s data lakes | 10+ years, runs 90% of new workloads | Context |
| **Still runs in production 2025? | Yes — millions of nightly batch jobs in banks, telcos, gov | Yes — everything new + most old jobs migrated | Both |
| Operational Complexity | High (NameNode HA, ZooKeeper, Kerberos) | Lower (especially on Kubernetes or Databricks) | Spark |
| Ecosystem (2025) | Hive, Pig, HBase, Oozie (many dying) | Delta Lake, Iceberg, Hudi, Kafka, Flink, Trino, dbt, MLflow | Spark |
| Cloud Support | EMR, HDP, CDP (still used) | Databricks, Snowflake, BigQuery, Synapse, EMR, GCP Dataproc | Spark |
| Cost on Cloud (same data) | Higher (more nodes, slower jobs) | Lower (fewer nodes, faster jobs) | Spark |
| GPU / Modern Hardware | Possible but clunky | Native RAPIDS, Spark + CUDA, GPU scheduling | Spark |
Performance Head-to-Head (Real Benchmarks 2025)
| Workload | Hadoop MapReduce | Spark 3.5 (on YARN) | Speedup |
|---|---|---|---|
| Terasort 100 TB | ~3–4 hours | 12–18 minutes | ~15× |
| TPC-DS 10 TB (SQL) | 6+ hours (Hive) | 8–15 minutes (Spark SQL) | ~40× |
| ML Training (Random Forest) | Days (custom MR) | ~30–60 min (MLlib) | 50×+ |
| Streaming Kafka → Dashboard | Not possible | <1 second latency | ∞× |
When Hadoop (MapReduce) Still Wins in 2025 (Yes, it happens!)
| Scenario | Why Hadoop Wins |
|---|---|
| Regulated industries with 10+ year audit trails | MapReduce jobs unchanged since 2012 = zero risk |
| Extremely cheap storage (petabytes on HDD) | HDFS + Erasure Coding cheaper than cloud lakes |
| COBOL → Hadoop nightly batch (banks) | No need to rewrite |
| Legal hold / immutable data retention | HDFS WORM + Ranger |
When Spark Wins (99% of new projects)
| Scenario | Reality in 2025 |
|---|---|
| Lakehouse (Delta/Iceberg/Hudi) | Spark is the only write engine |
| Real-time anything | Structured Streaming dominates |
| Data Science / ML / GenAI | Spark + GPUs + Pandas API |
| Cost optimization on cloud | Spark finishes in minutes → lower $ |
| Modern stack (dbt, Airflow, Trino) | All built around Spark |
Decision Matrix – What Should You Choose in 2025?
| Your Situation | Choose | Recommendation |
|---|---|---|
| New project, cloud or on-prem | → Use Spark (Delta Lake) | Spark 100% |
| Existing massive Hadoop batch cluster | → Keep Hadoop for batch, add Spark alongside | Hybrid |
| Need sub-second streaming + ML | → Spark Structured Streaming + MLlib | Spark |
| Regulated bank with 1000 MapReduce jobs | → Don’t touch — run as-is | Hadoop (legacy) |
| Building a modern data platform | → Spark + Iceberg/Delta + Trino + dbt | Spark ecosystem |
Bottom Line – 2025 Reality
| Statement | Truth in 2025 |
|---|---|
| “Hadoop is dead” | False — HDFS + YARN still run >60% of world’s data |
| “No one writes MapReduce anymore” | True for new code — but old code runs forever |
| “Spark replaced Hadoop” | Partially true — Spark replaced MapReduce engine, but runs on YARN/HDFS |
| Best architecture in 2025 | Spark + Delta Lake/Iceberg on YARN or Kubernetes or Cloud storage |
Verdict:
Spark won the war for processing.
Hadoop (HDFS + YARN) still wins the storage and multi-tenancy war in many enterprises.
Most modern clusters in 2025 are actually Spark on YARN or Spark on Kubernetes — not Hadoop vs Spark, but Hadoop AND Spark**.
Want the next step?
- “Show me a real migration plan from Hadoop MapReduce to Spark”
- “Best practices for running Spark on YARN in 2025”
- “Spark on Kubernetes vs YARN comparison”
Just say the word — I’ll give you the full migration playbook used at Netflix, Uber, JPMorgan.
Last updated: Nov 30, 2025