Zingg-0.3.4
  • Welcome to Zingg
  • Step-By-Step Guide
    • Installation
      • Docker
        • Sharing custom data and config files
        • Shared locations
        • File read/write permissions
        • Copying Files To and From the Container
      • Installing From Release
        • Single Machine Setup
        • Spark Cluster Checklist
        • Installing Zingg
        • Verifying The Installation
      • Compiling From Source
    • Hardware Sizing
    • Zingg Runtime Properties
    • Zingg Command Line
    • Configuration
      • Configuring Through Environment Variables
      • Data Input and Output
        • Input Data
        • Output
      • Field Definitions
      • Model Location
      • Tuning Label, Match And Link Jobs
      • Telemetry
    • 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
    • Building and saving the model
    • Finding the matches
    • Linking across datasets
  • Data Sources and Sinks
    • Zingg Pipes
    • Snowflake
    • JDBC
      • Postgres
      • MySQL
    • Cassandra
    • MongoDB
    • Neo4j
    • Parquet
    • BigQuery
  • Working With Python
  • Running Zingg on Cloud
    • Running on AWS
    • Running on Azure
    • Running on Databricks
  • 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
  • Reporting bugs and contributing
    • Setting Zingg Development Environment
  • Community
  • Frequently Asked Questions
  • Reading Material
  • Security And Privacy
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Security And Privacy

A note about telemetry and product usage data collection in Zingg

Zingg models are built on your data and deployed within your network. No data leaves your environment.

However, Zingg does collect usage metrics and writes them to Google Analytics. This is done to understand and improve the user experience. Please be assured that Zingg does not capture any user data or input data and will never do so.

The following details are captured :

  • Data source type: type of data format e.g. CSV, snowflake

  • Fields count: number of fields used for training

  • Total Data count: for match phase, number of total records

  • Execution Time: execution time of the program

  • Matched and Nonmatched records count: for the train phase, the number of matched and nonmatched records

If you do not wish to send this data, please set collectMetrics flag to false in the configuration JSON while running Zingg.

PreviousReading Material

Last updated 2 years ago