Zingg
  • 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
      • Enterprise Installation for Snowflake
        • Setting up Zingg
        • Snowflake Properties
        • Match Configuration
        • Running Asynchronously
        • 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
      • User Defined Mapping Match Types
      • Deterministic Matching
      • Pass Thru Data
      • 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
    • Verification of Blocking Model
    • Building And Saving The Model
    • Finding The Matches
    • Adding Incremental Data
    • Linking Across Datasets
    • Explanation of Models
    • Approval of Clusters
    • Combining Different Match Models
    • Model Difference
    • Persistent ZINGG ID
  • Data Sources and Sinks
    • Zingg Pipes
    • Databricks
    • Snowflake
    • JDBC
      • Postgres
      • MySQL
    • AWS S3
    • Cassandra
    • MongoDB
    • Neo4j
    • Parquet
    • BigQuery
    • Exasol
  • 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
  • Reporting Bugs And Contributing
    • Setting Up Zingg Development Environment
  • Community
  • Frequently Asked Questions
  • Reading Material
  • Security And Privacy
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Welcome To Zingg

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NextStep-By-Step Guide

Last updated 1 month ago

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This is the latest documentation for Zingg. Release wise documentation can be accessed through:

Why?

Data silos hurt all business functions - customer analytics, supplier consolidation, risk and compliance, and sales and marketing.

Zingg is a quick and scalable way to build a single source of truth for core business entities. With Zingg, the analytics engineer and the data scientist can quickly integrate data silos and build unified views at scale!

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v0.3.4
Data Silos
# Zingg - Data Mastering At Scale with ML