Dataplex parameters

In Google Cloud Dataplex, the Catalog is a key component for organizing and managing metadata for data assets. Here’s how Tags, Aspects, and Entity Details are used in the Dataplex Catalog:

1. Tags

Tags in Dataplex Catalog are metadata annotations that help categorize and provide additional context to assets. They are often used for:

• Data classification (e.g., “PII”, “Confidential”)

• Ownership & Governance (e.g., “Finance Team”, “Compliance Required”)

• Quality indicators (e.g., “Verified”, “Needs Review”)

Tags can be assigned at different levels, such as tables, files, and entities, to help in searching, filtering, and managing metadata effectively.

2. Aspects

Aspects represent specific metadata categories or attributes that describe a data entity. They help structure metadata into different dimensions. Examples include:

• Technical aspects (e.g., schema, data format)

• Business aspects (e.g., data owner, usage policies)

• Operational aspects (e.g., freshness, update frequency)

Aspects provide a structured way to enrich metadata, making it easier to discover and manage assets in Dataplex.

3. Entity Details

An Entity in the Dataplex Catalog represents a logical abstraction of a data asset. The Entity Details include:

• Type: Table, File, Stream, etc.

• Location: Cloud Storage, BigQuery, or another storage system

• Schema: Columns, data types, and descriptions

• Lineage & Relations: Connections to other datasets

Entity details help in data discovery, governance, and integration across different Google Cloud services.

Would you like to explore how these concepts fit into your data governance strategy?

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Unity Catalog vs Dataplex

 

Unity Catalog (Databricks)

Overview:

Unity Catalog is a unified data governance and metadata management layer built into the Databricks ecosystem. It helps organizations securely manage and organize their data assets across Databricks workspaces.

Key Features:

1. Centralized Data Governance:

   • Provides fine-grained access controls at the table, row, and column level.
   • Supports role-based access control (RBAC) and data masking.

2. Cross-Workspace Governance:

   • Allows for data sharing and governance across multiple Databricks workspaces.

3. Integrated with Databricks:

   • Seamlessly integrates with Databricks SQL, notebooks, and ML workflows.
   • Designed specifically for data lakes on AWS, Azure, and GCP.

4. Metadata Management:

   • Tracks metadata for tables, views, and files in your data lake.
   • Supports a unified catalog for structured and unstructured data.

5. Support for Delta Sharing:

   • Enables secure data sharing across organizations via open standards.

6. Auditing and Lineage:

   • Provides tools for auditing data access and lineage for compliance purposes.

Primary Use Case:

Unity Catalog is ideal for organizations that use Databricks for analytics, machine learning, and data engineering and want a governance layer deeply integrated into their Databricks ecosystem.

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Dataplex (Google Cloud)

Overview:

Dataplex is Google Cloud's data fabric solution that provides centralized data governance and management across distributed data systems, including BigQuery, Google Cloud Storage, and external sources.

Key Features:

1. Unified Data Governance:

   • Centralizes policies for access control, classification, and tagging.
   • Supports data cataloging and discovery across multiple data systems.

2. Distributed Data Management:

   • Works across hybrid and multi-cloud environments.
   • Supports integration with external systems via APIs and connectors.

3. Data Quality and Monitoring:

   • Includes automated data quality checks to identify and resolve data inconsistencies.
   • Provides monitoring and reporting for data health.

4. Integrated with Google Cloud Services:

   • Seamlessly integrates with BigQuery, Cloud Storage, AI/ML tools, and Looker.

5. Data Lineage and Metadata Management:

   • Tracks and manages metadata across data assets.
   • Offers end-to-end lineage tracking to understand data dependencies.

6. Lakehouse Implementation:

   • Helps create a modern lakehouse architecture with governance and security built-in.

Primary Use Case:

Dataplex is best suited for organizations that rely on Google Cloud for their data storage, processing, and analytics needs, especially in distributed or multi-cloud environments.

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Comparison Table

Feature	Unity Catalog (Databricks)	Dataplex (Google Cloud)
Platform	Databricks	Google Cloud
Integration	Databricks (Delta Lake, Spark, SQL, ML)	BigQuery, Cloud Storage, Looker, AI tools
Data Sources	Data lakes (Delta Lake, Parquet, etc.)	Google Cloud, external sources
Governance	RBAC, fine-grained controls	Centralized policies across platforms
Lineage Tracking	Yes	Yes
Data Quality Monitoring	No	Yes
Multi-Cloud Support	Limited (Databricks-specific cloud setup)	Yes (Google Cloud + external systems)
Best Use Case	Databricks-focused workloads	Distributed or Google Cloud-focused data

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Key Differences:

• Platform Focus: Unity Catalog is tightly integrated into the Databricks ecosystem, whereas Dataplex is part of Google Cloud and supports hybrid and multi-cloud setups.
• Data Quality Tools: Dataplex includes built-in data quality monitoring, while Unity Catalog focuses more on governance and metadata.
• Integration Scope: Dataplex is designed to handle distributed environments across Google Cloud and beyond, while Unity Catalog is optimized for Databricks users.

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Conclusion:

Choose Unity Catalog if your organization is heavily invested in Databricks and you need a governance solution designed for data lakes and machine learning workflows. Opt for Dataplex if you're on Google Cloud or require governance across distributed and hybrid environments with built-in data quality features.

Dataplex catalog entity groups

In Google Cloud’s Dataplex Catalog, entity groups are logical collections of metadata entities that represent datasets, tables, or other resources stored in data repositories. These entity groups are typically organized based on the structure of the data lake and reflect the relationships between different data assets. Below are examples of common entity groups in a Dataplex catalog:

1. Data Domains or Subject Areas

Entity groups can be organized by business domains or subject areas, such as:

• Sales

• Entities: Customer Transactions, Revenue, Sales Targets

• Marketing

• Entities: Campaign Data, Leads, Engagement Metrics

• Finance

• Entities: General Ledger, Expense Reports, Budget Data

• Operations

• Entities: Inventory, Supply Chain, Workforce Data

2. Data Types

Entity groups can also be classified based on the type of data:

• Master Data

• Entities: Customer Master, Product Master, Vendor Master

• Transactional Data

• Entities: Order Details, Payment Transactions, Shipment Records

• Reference Data

• Entities: Currency Codes, Country Codes, Tax Codes

• Log Data

• Entities: System Logs, Application Logs, Audit Trails

3. Data Sources

Grouping by the original data source:

• Operational Databases

• Entities: Oracle ERP, MySQL, Postgres Tables

• Third-Party APIs

• Entities: Weather Data, Market Prices, Social Media Metrics

• Cloud Storage

• Entities: GCS Buckets, Data Files (Parquet, CSV, JSON)

4. Analytical Layers

Entity groups based on data processing layers:

• Raw Data

• Entities: Unprocessed Logs, Raw IoT Data, Ingested Files

• Processed Data

• Entities: Cleaned Data, Transformed Tables, Aggregated Metrics

• Curated Data

• Entities: BI Dashboards, Reporting Tables, Machine Learning Features

5. Data Governance Classifications

Entity groups defined by data governance requirements:

• Sensitive Data

• Entities: PII Data, Payment Information, Health Records

• Non-Sensitive Data

• Entities: Open-Access Datasets, Publicly Shared Data

6. Storage Systems

Entity groups reflecting the storage technology or systems:

• BigQuery Tables

• Entities: Fact Tables, Dimension Tables, Aggregates

• Google Cloud Storage

• Entities: Bucket Contents (e.g., Yearly Financial Reports, Logs)

• Databases

• Entities: Tables and Views from MySQL, PostgreSQL, or other DBs

7. Project-Specific Groupings

Entity groups aligned with specific projects or initiatives:

• Customer 360 Initiative

• Entities: Customer Profile, Interaction History, Behavioral Data

• Supply Chain Optimization

• Entities: Supplier Performance, Delivery Times, Inventory Levels

8. Lineage-Based Grouping

Entity groups representing the flow of data:

• Source Data

• Entities: Raw Ingested Files

• Intermediate Data

• Entities: Transformation Results, Staging Tables

• Final Outputs

• Entities: Analytical Reports, Machine Learning Outputs

9. Industry-Specific Groups

For example, in an airline business:

• Passenger Data

• Entities: PNR Records, Ticket Sales, Loyalty Program Data

• Flight Operations

• Entities: Flight Schedules, Crew Rosters, Maintenance Logs

• Revenue Management

• Entities: Fare Classes, Load Factors, Revenue Forecasts

These entity groups help maintain an organized and governed catalog, enabling efficient discovery, management, and usage of data assets. Let me know if you’d like a more detailed breakdown for a specific use case!

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