AWS Entity Resolution vs Python Record Linkage Toolkit

AWS Entity Resolution links records that may describe the same customer, product, business, or healthcare entity across applications, channels, and data stores. It offers rule-based, machine learning, and data service provider matching. Rule-based users can adjust input fields, priorities, and rules; the preconfigured machine learning model compares consumer fields such as names, email addresses, phone numbers, addresses, and birth dates, then assigns match groups confidence scores from 0.0 to 1.0. Inputs come from AWS Glue, with a limit of 20. Rule-based matching can process new Amazon S3 data incrementally, and the GetMatchId API can synchronously look up an existing match ID while hashing personally identifiable attributes. Access is through the AWS console and API. Personally identifiable data is encrypted at rest by default with AWS-owned keys, with customer-managed symmetric AWS KMS keys also supported. Provider matching includes LiveRamp, TransUnion, and Unified ID 2.0 identifiers, subject to a provider license agreement. AWS states that the service meets PCI DSS and SOC 1, SOC 2, and SOC 3 measures. Pricing is paid, from $0.10; there is no free plan.

Python Record Linkage Toolkit is a free Python library for linking records across data sources and finding duplicates within one source. It is intended for research involving small or medium-sized files. Its workflow covers cleaning and standardizing data, generating candidate pairs, comparing records, classifying matches, and evaluating results. Cleaning tools include string standardization and phonetic encoding. Blocking and sorted neighbourhood indexing create candidate pairs. Comparisons cover strings, numbers, dates, and geographic data, with string methods including Jaro, Jaro-Winkler, Levenshtein, Damerau-Levenshtein, q-gram, and cosine similarity. Supervised and unsupervised classifiers include logistic regression, Naive Bayes, and support vector machines. Evaluation tools and built-in datasets are included. Users can add indexing algorithms, comparison measures, similarity measures, and classifiers. The toolkit uses pandas and NumPy and can fit existing pandas workflows. It is self-hosted, offers fuzzy matching and API access, and installs with pip or from GitHub. The stable documentation requires Python 3.6 or higher. The repository identifies a BSD-3-Clause license. Full indexing can become slow on large DataFrames because comparisons grow quadratically.