Blog Post

Data Cataloging(Metadata) on Cloud

Srinivasa Rao • April 12, 2020

Unified Metadata design on various clouds AWS, GCP and Azure

1 Introduction

This blog covers collecting, storing and searching and discovering various metadata across different data lake work streams on various clouds.

2 Types of metadata

Technical Metadata (System generated - auto ingested)

  • Table names, Column names
  • Table descriptions, column descriptions
  • Data created; date modified

Business Metadata (User provided/inferred)

  • PII information (Table and Column level)
  • Table Owner
  • Table Source
  • Business logic to derive column
  • Retain until date
  • Data Quality Owner
  • Data Governance
  • Data Quality information
  • Environment (prod/dev/qa/temp)
  • Classify data

Data Pipeline Metadata (System generated, and User provided)

  • Data pipelines
  • ETL/ELT specific information
  • Scheduling information

Data Lineage Metadata (automated through reusable components and system level defaults - for auditing only)

  • Data last updated/created (add last updated and create timestamp to each row)
  • Who updated the data (data pipeline, job name, user name and so on - Use Map or Struct or JSON column type)?
  • How data was modified or added (storing update history where required - Use Map or Struct or JSON column type)

Any solution we use to tackle above metadata should cover the following:

➔A unified view into all the data no matter where it is stored

➔Integration with analytical tools

➔A way to automatically build all metadata and keep it in sync with our data as it evolves

➔Should have Data Governance in place through IAM controls

➔Should have a proper API to integrate it with other tools like data pipelines.

➔Should be able to search through easily


Data Catalog (GCP)

Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, manage, and understand all their data in Google Cloud. It offers a simple and easy-to-use search interface for data discovery, a flexible and powerful cataloging system for capturing both technical and business metadata, and a strong security and compliance foundation with Cloud Data Loss Prevention (DLP) and Cloud Identity and Access Management (IAM) integrations.

AWS Glue Data Catalog

AWS Glue data catalog collects metadata from different data sources like amazon RDS, S3, RedShift and Dynamo and allows users to search and discover data from AWS provided UI or through APIs.

Azure Data Catalog

Azure Data Catalog is a fully managed cloud service. It can collect metadata from different data sources and allows users to search and discover data.

Note: The above images are courteous to respective clouds and taken from their documentation.

"About Author"

The author has extensive experience in Big Data Technologies and worked in the IT industry for over 25 years at various capacities after completing his BS and MS in computer science and data science respectively. He is certified cloud architect and holds several certifications from Microsoft and Google. Please contact him at srao@unifieddatascience.com if any questions.
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