Benchmarks: Cloudera CDH/CDP vs Azure-Native Services¶

Performance, cost efficiency, and operational overhead comparisons between Cloudera components and their Azure equivalents.

Methodology¶

All benchmarks in this document use the following methodology unless otherwise noted:

• Hardware equivalence: CDH benchmarks run on the cluster as-is (bare metal or VM). Azure benchmarks run on comparable VM SKUs (Standard_DS4_v2 for workers, Standard_DS5_v2 for drivers).
• Data equivalence: Same datasets used on both platforms. Data migrated via azcopy with Parquet preserved; Delta conversion applied where noted.
• Warm cache: Each query/job run 5 times; first run discarded (cold cache). Results are the average of runs 2-5.
• Pricing: Azure list prices as of April 2026. Cloudera pricing based on published list rates and typical enterprise discount (20-30%).
• Cluster sizing: Benchmarks normalize to a 20-worker cluster for Spark workloads and a Medium SQL Warehouse for interactive SQL.

1. Spark job performance: CDH Spark on YARN vs Databricks¶

Test workload: TPC-DS 1 TB¶

TPC-DS at 1 TB scale, running the full 99-query suite plus ETL workloads (INSERT OVERWRITE with aggregations and joins).

Key findings¶

• Databricks with Photon is 2.2-3.0x faster than CDH Spark 2.4 across the TPC-DS suite
• CDP Spark 3.3 is 1.3-1.4x faster than CDH Spark 2.4 due to Spark 3.x improvements (AQE, dynamic partition pruning)
• Photon's advantage is most pronounced on scan-heavy and aggregation-heavy queries (2-4x speedup)
• Join-heavy queries show moderate improvement (1.5-2x) due to Photon's vectorized hash joins
• Cost per run is lower on Databricks because auto-termination means you only pay for actual compute time. CDH clusters run 24/7.

xychart-beta
    title "TPC-DS 1TB: Total Runtime (seconds, lower is better)"
    x-axis ["CDH Spark 2.4", "CDP Spark 3.3", "Databricks 15.4 Photon"]
    y-axis "Seconds" 0 --> 5000
    bar [4200, 3100, 1400]

2. Interactive SQL: Impala vs Databricks SQL¶

Test workload: BI dashboard queries¶

A set of 20 representative BI dashboard queries on a 500 GB retail dataset: aggregations, filtered scans, multi-table joins, window functions, and approximate distinct counts.

Key findings¶

• Databricks SQL is 1.3-1.9x faster than Impala on CDH for typical BI queries
• P95 and P99 latency improvements are more significant than median, because Databricks AQE handles skewed data better
• Concurrency scaling is Databricks' major advantage: multi-cluster auto-scaling serves 50+ concurrent users without degradation. Impala requires manual cluster sizing.
• Serverless SQL Warehouse eliminates cold start: unlike CDW which takes 30-60 seconds to spin up, Databricks Serverless starts instantly
• Cost advantage is 20-45% at equivalent performance, because Databricks charges per-DBU rather than per-node

Approximate function accuracy comparison¶

3. Data ingestion: NiFi vs Azure Data Factory¶

Test workload: batch file ingestion¶

Ingest 10,000 CSV files (10 MB each, 100 GB total) from SFTP to data lake storage, with format conversion (CSV to Parquet).

Test workload: database ingestion¶

Ingest 50 million rows from Oracle database to data lake storage.

Test workload: real-time streaming¶

Ingest Kafka events (10,000 events/second) with routing and enrichment.

Key finding: NiFi has an edge in low-latency, event-by-event routing with fine-grained provenance. ADF/Event Hubs has advantages in batch throughput, cost efficiency, and operational simplicity. Choose based on your primary pattern.

4. Orchestration: Oozie vs ADF/Databricks Workflows¶

Test workload: complex ETL pipeline¶

A multi-step ETL pipeline: ingest from 3 sources, join, aggregate, load to warehouse, send notification.

5. Cost efficiency comparison¶

Monthly cost for a typical analytical workload¶

Workload profile: 10 Spark ETL jobs (daily), 50 BI users, 5 TB data, 20 scheduled queries.

xychart-beta
    title "Monthly Cost Comparison: Typical Analytical Workload"
    x-axis ["CDH on-prem", "CDP Private", "CDP Public", "Azure-native"]
    y-axis "Monthly Cost ($)" 0 --> 25000
    bar [23600, 24300, 19300, 11150]

6. Operational overhead comparison¶

Platform team effort (hours/week)¶

Key finding: Azure-native reduces operational overhead by 87% compared to CDH and 61% compared to CDP Public Cloud. This is the single largest driver of total cost reduction.

7. Migration execution benchmarks¶

Data transfer throughput¶

Workload conversion effort¶

Summary¶

Last updated: 2026-04-30 Maintainers: CSA-in-a-Box core team