Meteor Metadata Model

We have a set of defined metadata models which define the structure of metadata that meteor will yield. To visit the metadata models being used by different extractors please visit here. We are currently using the following metadata models:

• Bucket: Used for metadata being extracted from buckets. Buckets are the basic containers in Google cloud services, or Amazon S3, etc., that are used for data storage, and quite popular because of their features of access management, aggregation of usage and services and ease of configurations. Currently, Meteor provides a metadata extractor for the buckets mentioned here

• Dashboard: Dashboards are an essential part of data analysis and are used to track, analyze, and visualize. These Dashboard metadata model includes some basic fields like urn and source, etc., and a list of Chart. There are multiple dashboards that are essential for Data Analysis such as metabase, grafana, tableau, etc. Please refer to the list of 'Dashboard' extractors meteor currently supports here.

  • Chart: Charts are included in all the Dashboard and are the result of certain queries in a Dashboard. Information about them includes the information of the query and few similar details.

• User: This metadata model is used for defining the output of extraction on User accounts. Some of these sources can be GitHub, Workday, Google Suite, LDAP. Please refer to the list of 'User' extractors meteor currently supports here.

• Table: This metadata model is being used by extractors based around databases, typically for the ones that store data in tabular format. It contains various fields that include schema of the table and other access related information. Please refer to the list of 'Table' extractors meteor currently supports here.

• Job: A job can represent a scheduled or recurring task that performs some transformation in the data engineering pipeline. Job is a metadata model built for this purpose. Please refer to the list of 'Job' extractors meteor currently supports here.

• Topic: A topic represents a virtual group for logical group of messages in message bus like kafka, pubsub, pulsar etc. Please refer to the list of 'Topic' extractors meteor currently supports here.

• Machine Learning Feature Table: A Feature Table is a table or view that represents a logical group of time-series feature data as it is found in a data source. Please refer to the list of 'Feature Table' extractors meteor currently supports here.

• Application: An application represents a service that typically communicates over well-defined APIs. Please refer to the list of ' Application' extractors meteor currently supports here.

• Machine Learning Model: A Model represents a Data Science Model commonly used for Machine Learning(ML). Models are algorithms trained on data to find patterns or make predictions. Models typically consume ML features to generate a meaningful output. Please refer to the list of 'Model' extractors meteor currently supports here.

Proto has been used to define these metadata models. To check their implementation please refer here.

Usage

import(
    assetsv1beta1 "github.com/raystack/meteor/models/raystack/assets/v1beta1"
    "github.com/raystack/meteor/models/raystack/assets/facets/v1beta1"
)

func main(){
    // result is a var of data type of assetsv1beta1.Table one of our metadata model
    result := &assetsv1beta1.Table{
        // assigining value to metadata model
        Urn:  fmt.Sprintf("%s.%s", dbName, tableName),
        Name: tableName,
    }

    // using column facet to add metadata info of schema

    var columns []*facetsv1beta1.Column
    columns = append(columns, &facetsv1beta1.Column{
            Name:       "column_name",
            DataType:   "varchar",
            IsNullable: true,
            Length:     256,
        })
    result.Schema = &facetsv1beta1.Columns{
        Columns: columns,
    }
}