Ship fuzzy matching
in Databricks —
without the fuzzy pipeline
Run large-scale dedupe and reconciliation from Databricks notebooks, jobs, or workflows using a single API call. Keep Databricks for orchestration and data access. Offload the fuzzy-matching system itself.
# Dedupe messy names from your Delta table
import requests
names = df.select("company_name").toPandas()["company_name"].tolist()
r = requests.post("https://api.similarity-api.com/dedupe",
headers={"Authorization": f"Bearer {API_KEY}"},
json={
"data": names,
"config": {
"similarity_threshold": 0.85,
"to_lowercase": True,
"output_format": "deduped_strings"
}
}
)The Problem
Fuzzy matching inside Spark
is harder than it should be
You already have the data in Databricks. But the moment you need to deduplicate or reconcile messy strings, you're building infrastructure instead of solving the problem.
Cross joins that explode
Comparing every row to every other row creates O(N²) complexity. At 500K rows, that's 250 billion comparisons — cluster-crashing territory.
Blocking logic you maintain
To avoid the cross join, you write blocking keys, phonetic encoders, n-gram indexes. Each dataset needs different rules. Each rule needs tuning.
Spark UDFs that don't scale
Wrapping Levenshtein or Jaro-Winkler in a PySpark UDF serializes the work. You lose Spark's parallelism and end up with single-threaded bottlenecks.
The Alternative
One HTTP call from your notebook.
Matching handled externally.
Keep Databricks for what it's great at — data access, orchestration, and scheduling. Offload the fuzzy-matching compute to an API that was built for it.
Build it yourself
Pipeline to build, test, and maintain
Call Similarity API
Similarity API
1 API CallWhy This Works
Built for scale. Designed for simplicity.
Sub-linear matching engine
Proprietary algorithm that avoids O(N²) comparisons. 1M rows deduplicated in ~7 minutes — without clusters or tuning.
Works with any Databricks setup
Notebooks, scheduled jobs, Workflows, Delta Live Tables — anywhere you can make an HTTP request.
Configurable without code
Thresholds, preprocessing (lowercasing, punctuation removal, token sorting), output formats — all via JSON config.
Data never stored
Your data is processed in memory, encrypted in transit, and never persisted. Nothing is logged or retained.