Zingg-0.3.3
  • Welcome to Zingg
  • Step By Step Guide
    • Installation
      • Working with Docker Image
    • Hardware Sizing
    • Configuration
    • Creating training data
      • findTrainingData
      • label
      • findAndLabel
      • Using preexisting training data
      • Exporting labeled data as csv
    • Building and saving the model
    • Finding the matches
    • Linking across datasets
  • Data Sources and Sinks
    • Zingg Pipes
    • Snowflake
    • Cassandra
    • MongoDB
    • Neo4j
    • Parquet
  • Running Zingg on Cloud
    • Running on AWS
    • Running on Azure
    • Running on Databricks
  • Zingg Models
    • Pretrained models
  • Improving Accuracy By Defining Own Functions
  • Generating Documentation
  • Output Scores
  • Security And Privacy
  • Updating Labeled Pairs
  • Reporting bugs and contributing
  • Community
  • Frequently Asked Questions
  • Reading Material
Powered by GitBook
On this page
  • Why?
  • Book Office Hours

Welcome to Zingg

NextStep By Step Guide

Last updated 2 years ago

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 the single source of truth of core business entities. With Zingg, the analytics engineer and the data scientist can quickly integrate data silos and build unified views at scale!

Book Office Hours

If you want to schedule a 30-min call with our team to help you get set up, please select some time directly

here
data silos
# Zingg - Data Mastering At Scale with ML