Benchmarks: Hadoop vs Azure Performance and Cost¶

Comparative benchmarks covering compute performance (MapReduce vs Spark/Databricks), storage throughput (HDFS vs ADLS Gen2), query performance (Hive vs Databricks SQL), and cost efficiency across common workload patterns.

Methodology¶

These benchmarks represent aggregated results from publicly available sources (Databricks, Microsoft, and independent testing) combined with patterns observed across enterprise migrations. Your results will vary based on cluster configuration, data characteristics, and query complexity.

All benchmarks use the same hardware baseline:

• Hadoop: 100-node CDH 6.3 cluster (D16s_v5 equivalent: 16 vCPU, 64 GB RAM per node)
• Azure: Databricks with auto-scaling (D16s_v5 workers, Photon enabled)
• Data: TPC-DS 10 TB dataset and real-world enterprise workload patterns
• Storage: HDFS (3x replication) vs ADLS Gen2 (ZRS)

1. Compute: MapReduce vs Spark vs Databricks Photon¶

Batch ETL workload (10 TB daily aggregation)¶

Key takeaways:

• MapReduce to Spark: 6x faster
• Spark on YARN to Spark on Databricks: 1.5x faster (better auto-scaling, AQE)
• Databricks + Photon: 3.5x faster than Spark on YARN, 21x faster than MapReduce

Word count benchmark (classic MapReduce comparison)¶

Complex ETL (multi-join, aggregation, window functions)¶

2. Storage: HDFS vs ADLS Gen2¶

Sequential read throughput¶

Analysis: HDFS with SSDs can outperform ADLS for single-stream reads due to data locality. However, ADLS Gen2 scales bandwidth by adding parallel streams without adding hardware. For most Spark workloads (highly parallel), ADLS Gen2 provides comparable or better aggregate throughput.

Sequential write throughput¶

Metadata operations (NameNode vs ADLS namespace)¶

Analysis: HDFS NameNode is faster for metadata operations because metadata is in-memory. However, HDFS NameNode is a single point of failure and bottleneck at scale. ADLS Gen2's metadata service scales horizontally with no upper bound.

Storage cost comparison (500 TB)¶

3. Query performance: Hive vs Databricks SQL¶

TPC-DS benchmark results (10 TB scale)¶

Real-world query examples¶

Query 1: Daily sales report

SELECT
    d_date,
    SUM(ss_net_profit) AS net_profit,
    COUNT(DISTINCT ss_customer_sk) AS unique_customers
FROM store_sales
JOIN date_dim ON ss_sold_date_sk = d_date_sk
WHERE d_year = 2024
GROUP BY d_date
ORDER BY d_date;

Query 2: Customer cohort analysis (complex)

WITH first_purchase AS (
    SELECT ss_customer_sk, MIN(d_date) AS cohort_date
    FROM store_sales JOIN date_dim ON ss_sold_date_sk = d_date_sk
    GROUP BY ss_customer_sk
),
monthly_revenue AS (
    SELECT
        fp.cohort_date,
        DATE_TRUNC('month', d.d_date) AS purchase_month,
        COUNT(DISTINCT ss.ss_customer_sk) AS active_customers,
        SUM(ss.ss_net_profit) AS revenue
    FROM store_sales ss
    JOIN date_dim d ON ss.ss_sold_date_sk = d.d_date_sk
    JOIN first_purchase fp ON ss.ss_customer_sk = fp.ss_customer_sk
    GROUP BY fp.cohort_date, DATE_TRUNC('month', d.d_date)
)
SELECT * FROM monthly_revenue
ORDER BY cohort_date, purchase_month;

Concurrency benchmarks¶

Databricks SQL warehouses scale horizontally to handle concurrent queries. Hive LLAP has fixed capacity that creates queueing at high concurrency.

4. Streaming benchmarks¶

Event ingestion throughput¶

Streaming ETL (Kafka/Event Hubs to Delta Lake)¶

5. Cost comparison by workload pattern¶

Workload A: Nightly batch ETL (10 TB, runs 2 hours)¶

Note: The Hadoop cluster runs 24/7 even though the ETL job runs for 2 hours. The remaining 22 hours are idle capacity you pay for.

Workload B: Interactive analytics (200 users, business hours)¶

Workload C: 24/7 streaming pipeline¶

Workload D: ML training (weekly, 8 hours GPU)¶

Combined workloads: total cost¶

This comparison is intentionally favorable to Azure because it highlights Hadoop's fundamental weakness: you pay for 24/7 capacity even when workloads are bursty. Real-world savings are typically 40-60% after accounting for personnel, licensing, and migration costs (see TCO Analysis).

6. Data format benchmarks¶

Query performance by format (1 TB scan + filter + aggregate)¶

Key insight: Converting to Delta Lake with Z-ORDER provides the largest performance improvement — more than the engine upgrade itself.

Storage efficiency by format (same 1 TB dataset)¶

Summary of key findings¶

Last updated: 2026-04-30 Maintainers: CSA-in-a-Box core team Related: TCO Analysis | Why Azure over Hadoop | Migration Hub