Zingg
  • Welcome To Zingg
  • Step-By-Step Guide
    • Installation
      • Docker
        • Sharing Custom Data And Config Files
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        • Setting up Zingg
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    • Configuration
      • Configuring Through Environment Variables
      • Data Input And Output
        • Input Data
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      • Field Definitions
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      • Model Location
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    • Working With Training Data
      • Finding Records For Training Set Creation
      • Labeling Records
      • Find And Label
      • Using Pre-existing Training Data
      • Updating Labeled Pairs
      • Exporting Labeled Data
    • Verification of Blocking Model
    • Building And Saving The Model
    • Finding The Matches
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    • Linking Across Datasets
    • Explanation of Models
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    • Model Difference
    • Persistent ZINGG ID
  • Data Sources and Sinks
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      • Postgres
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  • Working With Python
    • Python API
  • Running Zingg On Cloud
    • Running On AWS
    • Running On Azure
    • Running On Databricks
    • Running on Fabric
  • Zingg Models
    • Pre-Trained Models
  • Improving Accuracy
    • Ignoring Commonly Occuring Words While Matching
    • Defining Domain Specific Blocking And Similarity Functions
  • Documenting The Model
  • Interpreting Output Scores
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  • Steps to run zingg on S3
  • Model location

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  1. Data Sources and Sinks

AWS S3

Zingg can use AWS S3 as a source and sink

Steps to run zingg on S3

  • Set a bucket, for example - zingg28032023 and a folder inside it,for example - zingg

  • Create aws access key and export via env vars (ensure that the user with below keys has read/write access to above) export AWS_ACCESS_KEY_ID=<access key id> export AWS_SECRET_ACCESS_KEY=<access key> (if mfa is enabled AWS_SESSION_TOKEN env var would also be needed )

  • Download hadoop-aws-3.1.0.jar and aws-java-sdk-bundle-1.11.271.jar via maven

  • Set above in zingg.conf spark.jars=//hadoop-aws-3.1.0.jar,//aws-java-sdk-bundle-1.11.271.jar

  • Run using below commands

 ./scripts/zingg.sh --phase findTrainingData --properties-file config/zingg.conf  --conf examples/febrl/config.json --zinggDir  s3a://zingg28032023/zingg
 ./scripts/zingg.sh --phase label --properties-file config/zingg.conf  --conf examples/febrl/config.json --zinggDir  s3a://zingg28032023/zingg
 ./scripts/zingg.sh --phase train --properties-file config/zingg.conf  --conf examples/febrl/config.json --zinggDir  s3a://zingg28032023/zingg
 ./scripts/zingg.sh --phase match --properties-file config/zingg.conf  --conf examples/febrl/config.json --zinggDir  s3a://zingg28032023/zingg

Model location

Models etc. would get saved in 
Amazon S3 > Buckets > zingg28032023 > zingg > 100
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Last updated 2 months ago

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