Modern data matching infrastructure has shifted toward three capabilities: REST API access, containerized deployment, and configurable matching engines. Teams that expose their matching pipeline through a REST API can trigger deduplication runs from any upstream system, retrieve scored results programmatically, and embed fuzzy search into live applications without manual file transfers.
Match Data Pro’s fuzzy matching engine covers every layer of this architecture — from API-triggered batch jobs and real-time lookup endpoints to AI-driven profiling and configurable scoring rules — all running on cloud SaaS with no infrastructure to manage.
Book a 30-minute demo to see the full REST API, matching engine, and pipeline in action.
Why REST API Access Matters for Data Matching
A file-based workflow — upload a CSV, wait for a batch run, download results — works for one-off projects. It breaks down when matching needs to happen continuously: new customer records arriving from a web form, transaction events hitting a data lake, or enrichment calls made at the point of sale.
REST API access changes the model. Instead of moving files, your pipeline POSTs records to a matching endpoint and receives scored match candidates in the response. A typical call pattern looks like this:
- POST /jobs/import — submit a batch of records or a single record for real-time lookup
- GET /jobs/{id}/status — poll job progress for large batch operations
- GET /jobs/{id}/output — retrieve matched, deduplicated, and merged results
- POST /search/fuzzy — run a live fuzzy search against an indexed master dataset
This pattern fits naturally into modern ETL pipelines, Airflow DAGs, and event-driven architectures. Your orchestration layer calls the matching API the same way it calls any other service. No proprietary SDKs required.
Match Data Pro’s live fuzzy search API supports real-time lookups against a pre-indexed master dataset. A single record — a company name, a postal address, a customer email — is submitted and the API returns the top N candidates with match scores, field-level confidence, and a recommended action (auto-match, review, or no match).
The Matching Engine: What a Rebuilt Architecture Delivers
A matching engine is the core of any deduplication platform. It takes a pair of candidate records and produces a similarity score. The quality of that score determines whether your output is useful or polluted with false positives and missed duplicates.
Algorithms that matter
A modern matching engine supports multiple algorithms simultaneously and applies them at the field level:
- Levenshtein edit distance — catches transposition errors and single-character substitutions. “Jhonn Smith” vs “John Smith” scores 0.91.
- Jaro-Winkler — weights prefix agreement more heavily, making it better for names where the first three characters are reliable. “Jonathan” vs “Jonathon” scores 0.97.
- Phonetic algorithms (Soundex, Double Metaphone) — handles surname variants like “Smyth” vs “Smith” or “Mueller” vs “Muller”.
- Token-based matching — splits strings into tokens before scoring, so “Acme Corp Ltd” and “Ltd Acme Corporation” still produce a high similarity score.
Field weights are applied on top of algorithm scores. A composite record score might weight company name at 40%, city at 20%, phone at 25%, and email at 15%. Changing one weight changes every match decision — which is why configurable scoring rules are not optional for enterprise use.
Thresholds and routing
Once a composite score is computed, the engine routes each candidate pair through a threshold-based decision tree:
| Score Range | Action | Typical Use |
|---|---|---|
| ≥ 0.85 | Auto-match | Create or update golden record immediately |
| 0.60 – 0.84 | Review queue | Human-in-the-loop confirmation |
| < 0.60 | No match | Retain both records independently |
These thresholds are configurable per project. A high-recall use case (marketing list deduplication) might lower the auto-match threshold to 0.75. A high-precision use case (fraud detection or regulatory name screening) might raise it to 0.92 and route everything below to review.
The diagram below shows the full pipeline from raw data ingestion through profiling, cleansing, fuzzy scoring, and clean output delivered back via REST API.
AI Data Profiling: The Step Before Matching
You cannot configure a matching engine accurately without first understanding your data. A field named customer_name might contain full legal names in one source and display names like “Acme (Jim)” in another. An engine tuned for the first will produce poor results on the second.
AI data profiling automates this discovery step. It scans every field in your dataset and returns:
- Null rate and completeness percentage per field
- Value distribution and cardinality
- Detected format patterns (e.g., phone number variants: (555) 123-4567, 555.123.4567, +15551234567)
- Suggested field type (name, address, email, numeric ID, date)
- Fields with likely duplicate candidate values
With this profile in hand, you configure matching rules that reflect the actual data, not assumptions. In a recent CRM migration project, profiling revealed that 34% of records had the first and last name fields reversed for records imported from a legacy system. The matching engine was configured to score both orderings and take the higher result — recovering 11,000 matches that would otherwise have been missed.
Cleansing and Standardisation Before the Match
Matching against dirty data inflates false negatives. Two records representing the same company fail to match because one reads “123 Main St Ste 400” and the other reads “123 Main Street, Suite 400.” They are the same address.
A production pipeline standardises data before it reaches the matching engine:
- Name parsing: split full name into title, first, middle, last, suffix fields
- Address normalisation: expand abbreviations (St → Street, Blvd → Boulevard), parse into components, apply CASS address verification against the USPS database
- Phone standardisation: strip formatting, normalise to E.164 (+1XXXXXXXXXX)
- Case and whitespace: trim, lowercase, remove double spaces
- Domain extraction: pull the root domain from email addresses for company-level matching
Match Data Pro’s data cleansing pipeline handles all of these transformations before records enter the matching engine. Cleansing rules can be defined per field and per source, so records from a legacy ERP system can be standardised differently from records arriving via API from a web form.
Deduplication, Survivorship, and the Golden Record
After matching, the engine groups confirmed duplicates into clusters. One record from each cluster must be selected — or fields must be merged — to form the golden record that propagates to downstream systems.
Survivorship rules govern this step. Common patterns:
- Most recent wins: take the field value from the record with the latest modified timestamp
- Most complete wins: take the value from the record with the fewest null fields
- Source priority: always prefer values from the CRM over values from a spreadsheet import
- Longest value wins: for address line 2, take the more detailed entry
These rules are applied field by field. A golden record might take the name from source A, the verified address from source B (because CASS confirmed it), and the phone number from source C (because it is the most recent). Match Data Pro’s data matching and merging module exports the golden record back to your system via REST API, direct database write, or file export — with a full audit trail showing which source contributed each field.
Entity Resolution for Cross-Source Identity
When the same real-world entity appears across three or more source systems — each with a different internal ID, slightly different name spelling, and no shared identifier — standard pairwise matching is not enough. You need entity resolution.
Senzing entity resolution, integrated into Match Data Pro, builds a graph of record relationships. Each new record is evaluated against all previously resolved entities. The graph grows incrementally: records added today are linked to existing entities immediately, without re-running the full dataset. This makes entity resolution practical for operational pipelines, not just one-off projects.
A representative example: a financial services firm ingests customer records from three acquired companies. Each source has its own ID scheme. Senzing resolves 840,000 input records to 620,000 distinct entities — a 26% reduction — with no manual review required for records scoring above the configured threshold.
Job Automation, Connectors, and Scheduled Runs
Data matching is not a one-time project. Customer records change. New imports arrive. Systems are updated. A production data quality operation runs matching on a schedule — nightly, weekly, or triggered by record-count thresholds.
Match Data Pro’s job automation layer lets you configure recurring matching jobs, set alert thresholds (notify if duplicate rate exceeds 5% in a run), and chain jobs sequentially: profile → cleanse → match → merge → export. Each job run produces a log with record counts, match rate, review queue depth, and processing time.
Import and export connectors cover the most common sources: CSV, Excel, JSON, direct database connections (Postgres, MySQL, SQL Server, Snowflake), and Salesforce. New records can be pushed via REST API. Results can be pulled by downstream systems on demand or delivered via webhook on job completion.
Start a free trial to run your first matching job against your own data — no contract, no IT setup required. The full pipeline including API access, AI profiling, fuzzy matching, and export connectors is available from day one.
Frequently Asked Questions
What is a data matching REST API and how does it work?
A data matching REST API exposes deduplication and fuzzy matching capabilities as HTTP endpoints. Clients POST records to the API, the matching engine scores candidates against a master dataset or within the submitted batch, and the API returns scored results — match, review, or no match — with field-level confidence scores. This allows any system to trigger and consume matching results programmatically.
What algorithms does a modern matching engine use?
A modern matching engine combines multiple algorithms at the field level: Levenshtein edit distance for character-level errors, Jaro-Winkler for name prefix weighting, phonetic algorithms (Soundex, Double Metaphone) for sound-alike variants, and token-based matching for word-order differences. Each algorithm produces a field score; a composite record score is computed using configurable field weights.
How do you set match thresholds without creating too many false positives?
Start by profiling your data to understand its quality baseline. Run the matching engine against a labeled test set where you know which record pairs are true duplicates. Plot precision and recall at each threshold. Most enterprise teams set auto-match at 0.85, review at 0.60–0.84, and no-match below 0.60 — then tune based on their false-positive tolerance for the specific use case.
What is the difference between deduplication and entity resolution?
Deduplication removes duplicate records within a single dataset or across two sources using pairwise comparison. Entity resolution operates at the identity graph level — it links records across multiple systems and resolves them to a single canonical entity, even when there is no shared identifier. Entity resolution is needed when records come from three or more sources with inconsistent IDs.
Can data matching run on a schedule without manual intervention?
Yes. Job automation in a modern data quality platform lets you schedule recurring matching runs, chain pipeline stages (profile, cleanse, match, merge, export), and set alert thresholds for anomalous match rates. Results are delivered via API or export connector so downstream systems receive updated golden records without any manual step.