Polars vs Spark for ETL — When to Use Which¶
Polars and Spark solve overlapping problems in different ways. Polars is a Rust-backed DataFrame library built for single-node speed. Spark is a JVM-based distributed compute engine built for cluster-scale workloads. Both are excellent — but choosing the wrong one for your workload wastes either money or time.
DataCoolie runs both engines on the same metadata, so we tested them side by side. Here's what we found and when to pick each one.
The Benchmark Setup¶
We ran identical ETL workloads through DataCoolie's PolarsEngine and SparkEngine using the usecase-sim testbed. Every workload used the same metadata, the same Delta Lake outputs, and the same load strategies (append, merge_upsert, SCD2).
Hardware: 8-core, 32 GB RAM, NVMe SSD. Spark ran as a local SparkSession (no cluster) to isolate engine-level differences from infrastructure advantages.
Workload tiers:
| Tier | Row Count | File Size |
|---|---|---|
| Small | 10K rows | ~2 MB |
| Medium | 1M rows | ~200 MB |
| Large | 50M rows | ~10 GB |
| XL | 200M+ rows | ~40 GB+ |
Results Summary¶
Small workloads (< 100K rows)¶
Winner: Polars by 5–10×.
Polars finishes small jobs in under a second. Spark spends 3–8 seconds on JVM startup, session creation, and query planning before processing a single row. For CSV → Delta pipelines with < 100K rows, Polars is strictly superior.
Medium workloads (100K–10M rows)¶
Winner: Polars by 2–3×.
Polars still outperforms on a single machine. Its vectorized Rust execution processes 1M-row merges faster than Spark's Catalyst optimizer can plan them. Memory usage is lower because Polars avoids JVM overhead and GC pauses.
Large workloads (10M–100M rows)¶
Winner: Depends. On a single machine, Polars can still win if the data fits in memory. But Spark's lazy evaluation and partition-level parallelism begin to close the gap, especially with shuffle-heavy operations like SCD2 merges on wide tables.
XL workloads (100M+ rows) or cluster environments¶
Winner: Spark.
Once data exceeds single-node memory or when you need distributed processing across a cluster (Fabric, Databricks, EMR), Spark is the only option. Polars is single-node by design.
What This Means for Your Pipeline¶
| Situation | Recommended Engine |
|---|---|
| Local development and testing | Polars |
| CI pipeline validation | Polars |
| Small/medium production loads (< 10M rows) | Polars |
| Cloud-native lakehouse (Fabric, Databricks) | Spark |
| Data exceeds single-node memory | Spark |
| Need Unity Catalog / Hive Metastore integration | Spark |
How DataCoolie Makes the Choice Easy¶
The key insight is: you don't have to choose permanently. DataCoolie's metadata is engine-agnostic. Develop locally with Polars for fast iteration, then deploy the same metadata on Spark in Fabric or Databricks. Zero code changes.
# Local development — fast
engine = PolarsEngine(platform=LocalPlatform())
# Production — distributed
engine = SparkEngine(spark=spark, platform=FabricPlatform())
Same metadata. Same load strategies. Same watermarks. Different engine.
Try It Yourself¶
Run the Polars quickstart and Spark quickstart back to back with the same metadata. See the full benchmark methodology in Operations → Benchmarks.
Bottom Line¶
- Default to Polars for development, CI, and small/medium production workloads.
- Switch to Spark when you hit cluster-scale data or need native cloud platform integration.
- Use DataCoolie to avoid choosing — write metadata once, run on both.