Apache Spark is a modern distributed computing framework that processes data 10-100x faster than Hadoop MapReduce through in-memory computing, while Hadoop is a mature ecosystem designed primarily for batch processing with disk-based storage. Spark has largely superseded Hadoop for most new big data projects.
Apache Spark vs Hadoop — Side-by-side comparison on A Versus B
In-memory distributed computing framework for fast batch, streaming, and interactive data processing.
Mature distributed computing framework with HDFS storage and MapReduce processing engine.
Processing Speed. Processing Model. Learning Curve. Ecosystem Maturity. Fault Tolerance. Setup Complexity. Real-time Capability
Choose Apache Spark if you need fast, flexible data processing with support for streaming, machine learning, and interactive queries—it's the industry standard for modern big data projects and most new deployments. Choose Hadoop if you have legacy systems already running it, need proven long-term stability in highly distributed storage environments, or work in organizations with deep Java expertise and existing Hadoop investments.
Apache Spark: 30-120 minutes | Hadoop: N/A
Apache Spark: Python, Scala, SQL, R, Java | Hadoop: N/A
Apache Spark: 8,000-15,000ms | Hadoop: N/A
Apache Spark: 60-120 (cluster setup) | Hadoop: N/A
Apache Spark: 35,900 stars | Hadoop: N/A
Apache Spark: Native support | Hadoop: N/A
Apache Spark: Yes (RDD lineage-based) | Hadoop: N/A
Apache Spark: 40-80 hours (dedicated DevOps engineer) | Hadoop: N/A
SQL Query Performance (TPC-DS Benchmark)
Apache Spark: 45-120 seconds | Hadoop: N/A
Apache Spark: 1-5 (via Jupyter sharing) | Hadoop: N/A
Apache Spark: 0 (manual implementation required) | Hadoop: N/A
Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library) | Hadoop: N/A
Apache Spark: 25,000+ questions | Hadoop: N/A
Apache Spark: $1,500-$5,000+ | Hadoop: N/A
Apache Spark: 5-15 minutes | Hadoop: N/A
Apache Spark: Python, Scala, Java, R, SQL | Hadoop: N/A
Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes) | Hadoop: N/A
Apache Spark: 0 (requires external frameworks) | Hadoop: N/A
Apache Spark: 30-120 seconds | Hadoop: N/A
Apache Spark: Python, Scala, Java, SQL, R | Hadoop: N/A
Apache Spark: Unlimited (depends on storage) | Hadoop: N/A
Apache Spark: 4+ (AWS, Azure, GCP, on-prem) | Hadoop: N/A
Apache Spark: 50+ (MLlib + custom models) | Hadoop: N/A
Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON) | Hadoop: N/A
Apache Spark: 30-60 seconds (in-memory) | Hadoop: N/A
Apache Spark: 10-60 seconds | Hadoop: N/A
Apache Spark: 500-5000 ms micro-batches | Hadoop: N/A
Apache Spark: Lineage-based recovery (RDD parents) | Hadoop: N/A
Apache Spark: 200+ via ecosystem integrations | Hadoop: N/A
Apache Spark: 40,100 stars | Hadoop: N/A
Apache Spark: 65% (Databricks, AWS, Google, Meta deployments) | Hadoop: N/A
Apache Spark: ~400-800MB (GC overhead) | Hadoop: N/A
Apache Spark: Partial in Structured Streaming, limited out-of-order support | Hadoop: N/A
Apache Spark: ~500K-1M events/sec | Hadoop: N/A
Apache Spark: Unified via Structured Streaming/Dataset API | Hadoop: N/A
Apache Spark: 0.5-2 seconds | Hadoop: 30-60 seconds
Apache Spark: 8-64 GB per node | Hadoop: 2-4 GB per node
Apache Spark: 5 (Scala, Python, Java, R, SQL) | Hadoop: 1 (Java)
Apache Spark: 100-500 ms (micro-batch) | Hadoop: Not natively supported
Apache Spark: 12 years (since 2013) | Hadoop: 20 years (since 2005)
Apache Spark: 74% currently use | Hadoop: 45% legacy deployments
Apache Spark: Full MLlib with algorithms, pipelines | Hadoop: Requires third-party libraries
Apache Spark: Depends on underlying storage | Hadoop: 3x replication (HDFS default)
Apache Spark: 100-2,000ms (Spark Streaming micro-batch interval) | Hadoop: N/A
Apache Spark: 500,000 - 2,000,000 (batch-optimized) | Hadoop: N/A
Apache Spark: 8-12GB (caching overhead) | Hadoop: N/A
Apache Spark: 7,200,000 (StackOverflow, job postings 2024) | Hadoop: N/A
Apache Spark: 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.) | Hadoop: N/A
Apache Spark: 2-4 weeks (larger talent pool, more examples) | Hadoop: N/A
Apache Spark: 80-150 lines (custom state handling needed) | Hadoop: N/A
Apache Spark: 2 types (RDD state, DataFrame state) | Hadoop: N/A
Apache Spark: 45,000-55,000 | Hadoop: N/A
Apache Spark: 8GB (per node, 3+ nodes recommended) | Hadoop: N/A
Apache Spark: 5-30 seconds | Hadoop: N/A
Apache Spark: 100,000+ (petabyte scale) | Hadoop: N/A
Apache Spark: 45-120 (including cluster setup) | Hadoop: N/A
Apache Spark: 6 (Scala, Python, SQL, R, Java, Go) | Hadoop: N/A
Apache Spark: 1-10 (cluster dependent) | Hadoop: N/A
Typical Processing Speed (iterative queries)
Apache Spark: 2-5 seconds | Hadoop: 20-60 seconds
Apache Spark: 16-64 GB (in-memory caching) | Hadoop: 8-16 GB (disk-based)
Apache Spark: Native (Spark Streaming, Structured Streaming) | Hadoop: Not native (requires external tools like Storm/Flink)
Apache Spark: 450,000+ globally | Hadoop: 120,000+ (declining)
Apache Spark: 1-2 hours | Hadoop: 4-8 hours
Apache Spark: Highly active (Apache Foundation priority) | Hadoop: Maintenance mode (legacy support only)
Approximate Learning Time for Developers
Apache Spark: 2-4 weeks | Hadoop: 6-10 weeks
How to choose between Apache Spark and Hadoop
Apache Spark vs Hadoop — Structured Comparison Data
Machine-readable comparison dataset for Apache Spark vs Hadoop. Contains 90 attribute comparisons, 5 FAQ pairs.
Apache Spark vs Hadoop — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Apache Spark vs Hadoop.
Yes, for most modern use cases. Spark can run on YARN (Hadoop's resource manager) and doesn't require HDFS. However, if you need Hadoop's distributed storage (HDFS) specifically for data durability across unreliable hardware, you may still use HDFS alongside Spark. Most new projects start with Spark only, often on Kubernetes or cloud storage (S3, GCS) instead of HDFS.
Yes, but primarily for legacy systems and maintenance. Industry surveys show 78% of organizations starting new big data projects now choose Spark or cloud-native alternatives like Databricks, while existing Hadoop deployments remain in maintenance mode. Some enterprises maintain Hadoop for petabyte-scale data warehouses where the cost per TB is critical.
Why is Spark so much faster than Hadoop MapReduce?
Spark keeps data in RAM between processing steps, while Hadoop MapReduce writes to disk after each map and reduce phase. For iterative algorithms (like machine learning), Spark avoids the 10-100x slowdown from repeated disk I/O. For one-time batch jobs, the speed difference is smaller (2-5x faster).
Spark costs more per node due to higher RAM requirements (32 GB vs 12 GB typical), but achieves results faster, reducing cluster utilization time. For a 100-node cluster processing the same workload: Hadoop might take 40 hours with cheaper nodes, while Spark takes 4 hours with more expensive nodes. Total cost often favors Spark when factoring in operational expenses and developer time.
Yes. Spark can use YARN as its resource manager while reading from HDFS, giving you Spark's speed with Hadoop's storage layer. This is a common migration path for organizations transitioning from pure Hadoop. However, Spark on Kubernetes is becoming more popular for new deployments due to better multi-tenancy and elasticity.
How to Choose Between Apache Spark and Hadoop
Processing Speed: Apache Spark has 10-100x faster (in-memory), while Hadoop has Baseline (disk-based).
Processing Model: Apache Spark has In-memory + real-time streaming, while Hadoop has Disk-based batch processing only.
Learning Curve: Apache Spark has Moderate (Scala/Python/SQL), while Hadoop has Steep (Java-heavy MapReduce).
Ecosystem Maturity: Apache Spark has Mature (released 2014), while Hadoop has Very mature (released 2006).
Fault Tolerance: Apache Spark has RDD lineage-based, while Hadoop has Replication-based (HDFS).
Setup Complexity: Apache Spark has Can run standalone or on YARN, while Hadoop has Requires full cluster setup.
Real-time Capability: Apache Spark has Native streaming support, while Hadoop has Batch only (requires external tools).