Record linkage is the algorithmic process of identifying and merging records from disparate sources that refer to the same real-world entity, despite missing identifiers, typos, or formatting variations. In scraping pipelines, it is the critical bridge between raw extraction and usable intelligence. Without it, you deliver a fragmented list of duplicates; with it, you deliver a unified master dataset.
Why joining scraped datasets is never as simple as a SQL JOIN, and how probabilistic matching bridges the gap.
Ask a DataFlirt engineer →Record linkage identifies and merges records from different sources that represent the same real-world entity. Because scraped data lacks universal primary keys, pipelines rely on blocking strategies and fuzzy matching algorithms to calculate similarity scores and resolve entities without human intervention.
Record linkage (also known as entity resolution or data matching) is the process of identifying records in one or more datasets that refer to the same real-world entity. In web scraping, this is necessary because different websites use different identifiers, naming conventions, and schemas.
A standard linkage pipeline consists of three phases:
Deterministic linkage requires exact matches on unique keys. If two records share the same UPC barcode or domain name, they are linked. It is computationally cheap but brittle—if a target site omits the UPC, the match fails.
Probabilistic linkage assigns weights to different fields. A match on "Company Name" might contribute 60% to the score, while a match on "City" contributes 20%. If the total score crosses a defined threshold (e.g., 0.85), the records are merged. This handles typos and missing data gracefully but requires careful tuning to avoid false positives.
Without blocking, linkage is an O(N²) problem. Comparing two datasets of 1 million records each requires 1 trillion comparisons. Blocking solves this by creating a heuristic key—for example, the first three letters of the last name plus the birth year. The engine only compares records that share this block key. The tradeoff is that if the block key itself contains a typo, the true match will be missed (a false negative).
We treat record linkage as a core component of the delivery layer. Our pipelines don't just dump raw JSON; they resolve entities across targets using a multi-pass engine. We run deterministic rules first (matching on ASINs, domains, or exact phone numbers), followed by LSH (Locality-Sensitive Hashing) blocking, and finally a tuned Fellegi-Sunter probabilistic model. Conflicting fields are resolved using client-defined survivorship rules before the final dataset is written to S3.
In record linkage, you must choose your poison: false positives (merging two different entities) or false negatives (failing to merge duplicates). For financial compliance or healthcare data, false positives are catastrophic, so thresholds are set extremely high (e.g., 0.98). For marketing lead generation, false negatives are worse (you don't want to email the same person twice), so thresholds are lowered (e.g., 0.75). There is no universal "correct" threshold.
Record linkage relies on string distance metrics and weighted scoring to decide if 'Apple Inc.' and 'Apple Incorporated' are the same entity. DataFlirt uses a composite scoring model tuned per pipeline.
A live trace of a DataFlirt linkage job merging product records from three different e-commerce targets into a single master SKU.
The most common reasons entity resolution pipelines produce false positives (merging distinct entities) or false negatives (failing to merge duplicates).
without the Cartesian explosion.
Comparing every scraped record against every other record is an O(N²) operation. At 10 million rows, that is 100 trillion comparisons — computationally impossible for a daily pipeline. DataFlirt's linkage engine uses aggressive Locality-Sensitive Hashing (LSH) and deterministic blocking to group candidate pairs before applying expensive probabilistic scoring, keeping pipeline latency linear and compute costs low.
Live metrics from a B2B company directory linkage run.
Anti-bot shifts, scraping infrastructure updates, dataset delivery patterns, and business outcomes from our pipelines. Short, technical, no fluff.
About entity resolution, blocking strategies, handling conflicting data, and how DataFlirt builds master datasets from fragmented sources.
Ask us directly →20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed across millions of records — we scope, build, and operate the pipeline.
