Microsoft Purview in Microsoft Fabric
Microsoft Purview is Microsoft's family of solutions for data governance, data security, compliance, and risk management across an organization's data estate.
In the context of Microsoft Fabric, the easiest way to understand Purview is:
Fabric processes and analyzes data; Purview helps organizations understand, govern, classify, protect, and track that data.
A useful mental model is:
DATA SOURCES
│
┌───────────┼───────────┐
▼ ▼ ▼
Azure Fabric Other Data
│ │ │
└───────────┼───────────┘
▼
MICROSOFT PURVIEW
│
┌────────────┼────────────┐
▼ ▼ ▼
Catalog Lineage Protection
│ │ │
▼ ▼ ▼
Discover Data Track Flow Classify Data
1. Why do we need Purview?
Imagine a large organization has:
5,000 Tables
2,000 Reports
500 Lakehouses
200 Warehouses
Thousands of files
Multiple databases
A user asks:
"Where can I find customer sales data?"
Another asks:
"Does this table contain personal information?"
Another asks:
"Where did this Power BI number come from?"
Another asks:
"Which reports will be affected if we change this source table?"
These are not primarily ETL problems.
They are data governance and discovery problems.
Purview capabilities help organizations address them.
2. What is Data Governance?
Data governance means managing data so that it is:
Discoverable
+
Understandable
+
Trusted
+
Protected
+
Properly Managed
For example, suppose you have:
dbo.CUST_MST_01
A business user may have no idea what it means.
Good governance adds context:
Table:
Customer Master
Description:
Contains active customer information.
Owner:
Sales Data Team
Classification:
Confidential
Contains:
Customer ID
Name
Phone
Used By:
Sales Analytics
Customer 360
Marketing Reports
Now the data is much easier to understand and govern.
3. Major Purview concepts
For Fabric learning, focus on:
Microsoft Purview
│
├── Data Catalog / Discovery
├── Metadata
├── Classification
├── Sensitivity Labels
├── Data Lineage
├── Data Governance
├── Data Protection
└── Compliance / Risk capabilities
You don't need to master every Purview product on day one.
For a Fabric Data Engineer, the most important concepts are usually discovery, cataloging, classification, sensitivity, and lineage.
4. Data Catalog
A data catalog helps users discover and understand organizational data.
Think of it like:
Google Search for enterprise data
Suppose your company has:
Sales Warehouse
Customer Lakehouse
Finance Warehouse
HR Lakehouse
Marketing Reports
Instead of asking different teams:
"Where is the customer data?"
a governed catalog can help users search and understand available data assets.
Conceptually:
User │ ▼
Search "Customer Sales"
│ ▼ Data Catalog │ ├── Customer Table ├── Sales Warehouse ├── Customer Lakehouse └── Sales Report
5. Metadata
Purview works heavily with metadata.
Metadata means:
Data about data.
Suppose:
CustomerID | Name | Country
101 | Ravi | India
That's the actual data.
Information such as:
Table Name
Column Name
Data Type
Description
Owner
Classification
Source
Relationships
is metadata.
Easy memory:
DATA
=
Actual values
METADATA
=
Information describing the data
6. Data Classification
Purview can help identify/classify sensitive types of information.
Suppose a table contains:
CustomerID
Name
Phone
CreditCard
Country
Not every column has the same sensitivity.
Conceptually:
CustomerID
↓
Identifier
↓
Personal information
Phone
↓
Personal information
CreditCard
↓
Highly sensitive financial information
Classification helps organizations understand:
What kind of information does this asset contain?
7. Sensitivity Labels
Sensitivity labels help categorize and protect information according to organizational policies.
For example, an organization might use labels such as:
Public
General
Confidential
Highly Confidential
Imagine:
Marketing Product List
↓
General
Customer Information
↓
Confidential
Employee Salary
↓
Highly Confidential
The exact labels and policies depend on the organization's Microsoft Purview Information Protection configuration.
8. Classification vs Sensitivity Label
Don't confuse these.
Classification
Describes what kind of data exists.
For example:
Email Address
Phone Number
Credit Card Number
Sensitivity Label
Describes how sensitive/protected the information should be.
For example:
Public
General
Confidential
Highly Confidential
Remember:
Classification = What is this data?
Sensitivity = How sensitive is this data?
9. Data Lineage
This is one of the most important Purview concepts for a Data Engineer.
Data lineage shows how data moves and transforms from source to destination.
For example:
SQL Server
↓
Fabric Pipeline
↓
Lakehouse
↓
Notebook
↓
Warehouse
↓
Semantic Model
↓
Power BI Report
This is lineage.
It answers:
Where did this data come from?
and
Where does this data go?
10. Why lineage matters
Suppose the CEO says:
"The Sales Dashboard says revenue is ₹20 crore. Where did this number come from?"
Without lineage:
Power BI Report
↓
???
↓
???
↓
Source?
With lineage:
Power BI Report
↓
Semantic Model
↓
Gold.FactSales
↓
Notebook
↓
Silver.Sales
↓
Pipeline
↓
ERP Sales Database
Now you can trace the data.
11. Lineage for troubleshooting
Imagine:
Source Table
↓
Pipeline
↓
Lakehouse
↓
Warehouse
↓
Power BI
A source column changes:
CustomerID
↓
CustomerNumber
You need to know:
What will this change affect?
Lineage can help identify downstream dependencies:
Customer Table
│
▼
Pipeline
│
▼
Lakehouse
│
▼
Customer Dimension
│
▼
Semantic Model
│
▼
Customer Report
This is called impact analysis.
12. Lineage vs Impact Analysis
They are related but slightly different.
Lineage
Where did the data come from and where does it flow?
Source
↓
Pipeline
↓
Lakehouse
↓
Warehouse
↓
Report
Impact Analysis
What will be affected if I change this asset?
Change Table
↓
Which Pipeline?
↓
Which Model?
↓
Which Report?
↓
Which Users?
Easy memory:
LINEAGE
=
Trace the flow
IMPACT ANALYSIS
=
Understand consequences of change
13. Purview + Fabric
Now connect Purview concepts to the Fabric items you've already learned.
MICROSOFT FABRIC
│
┌─────────────────┼─────────────────┐
▼ ▼ ▼
Lakehouse Warehouse Eventhouse
│ │ │
└─────────────────┼─────────────────┘
│
Pipelines
│
Dataflows
│
Notebooks
│
Power BI
│
▼
GOVERNANCE LAYER
│
▼
MICROSOFT PURVIEW
│
┌──────────────┼──────────────┐
▼ ▼ ▼
Catalog Lineage Protection
Purview-related capabilities help govern data across this environment.
14. Example with Fabric Lakehouse
Suppose:
Fabric Lakehouse │ ├── Customers ├── Orders ├── Products └── Sales
The Customers table contains:
CustomerID
CustomerName
Phone
Address
Governance may identify:
↓
Sensitive/Personal Information
Phone
↓
Sensitive/Personal Information
Address
↓
Sensitive/Personal Information
The asset can also have metadata describing:
Owner
Description
Business meaning
Classification
Sensitivity
15. Example with Fabric Warehouse
Suppose your Warehouse contains:
SalesWarehouse │ ├── FactSales ├── DimCustomer ├── DimProduct └── DimEmployee
DimCustomer contains:
CustomerKey
CustomerName
Phone
Country
DimEmployee contains:
EmployeeID
EmployeeName
Department
Salary
Clearly:
Salary
↓
Sensitive
↓
Personal information
Phone
↓
Personal information
Governance helps identify and appropriately protect these assets.
16. Data ownership
Another governance question is:
Who is responsible for this data?
Suppose:
FACT_SALES
Who owns it?
DBA?
Data Engineer?
Finance?
Sales?
BI Team?
Governance should establish responsibilities.
For example:
Data Asset │ ▼ FactSales │ ├── Business Owner → Sales Department │ └── Technical Owner → Data Engineering
This becomes extremely important in large organizations.
17. Business glossary
Technical teams may say:
NET_REV_AMT
Business teams say:
Net Revenue
Finance may define it as:
Gross Revenue − Discounts − Returns.
A business glossary helps establish a common business meaning.
Technical Column
NET_REV_AMT
↓
Business Term
Net Revenue
↓
Definition
Gross Revenue - Discounts - Returns
This reduces confusion between teams.
18. Data discovery example
Imagine an analyst needs:
Customer revenue data.
Without governance:
Ask DBA
↓
Ask BI Team
↓
Ask Data Engineer
↓
Search 100 databases
↓
Maybe find table
With a governed catalog:
Search
"Customer Revenue"
↓
Relevant Data Products / Assets
↓
Description
↓
Owner
↓
Classification
↓
Lineage
↓
Use appropriate trusted data
That's the business value of a data catalog.
19. Trusted data
Imagine you find:
Sales_Final
Sales_Final2
Sales_New
Sales_Updated
Sales_Prod
Sales_Report_Final
Which one should you use?
This is a common enterprise problem.
Governance practices can help users identify authoritative, approved, or endorsed data assets rather than guessing.
The goal is:
Many Data Assets
↓
Governance
↓
Trusted / Approved Assets
↓
Business Users
20. Purview and security
Purview doesn't replace Fabric security.
This distinction is important.
Your earlier Fabric Warehouse Security topic covered:
Entra ID
Workspace Roles
Item Permissions
SQL Permissions
RLS
CLS
These answer:
Who can access the data?
Purview governance/protection capabilities answer broader questions such as:
What data do we have?
Where is it?
Is it sensitive?
Where did it come from?
Where does it go?
How should it be governed/protected?
They work together.
21. Fabric Security vs Purview
| Fabric Security | Purview/Governance |
|---|---|
| Access control | Data governance |
| Workspace roles | Catalog/discovery |
| Item permissions | Metadata |
| SQL permissions | Classification |
| RLS | Sensitivity |
| CLS | Lineage |
| Who can access? | What data exists and how is it governed? |
Think:
FABRIC SECURITY
=
Can Sreehari access this table?
PURVIEW / GOVERNANCE
=
What is this table?
Who owns it?
Is it sensitive?
Where did it come from?
Where is it used?
22. Example: End-to-End governance
Imagine customer data starts in Salesforce:
Salesforce
│
▼
Fabric Pipeline
│
▼
Bronze Lakehouse
│
▼
Notebook
│
▼
Silver Customer
│
▼
Warehouse
│
▼
DimCustomer
│
▼
Semantic Model
│
▼
Customer 360 Report
Governance/lineage can help represent that journey.
If EmailAddress contains sensitive information:
EmailAddress
↓
Classification
↓
Sensitivity / Protection Policy
Now the organization knows both:
WHERE DATA CAME FROM
+
WHAT TYPE OF DATA IT IS
+
HOW IT SHOULD BE GOVERNED
23. Why Purview matters for compliance
Organizations may have regulatory or internal requirements around:
Personal information
Financial information
Employee information
Customer information
Confidential business data
They need to answer questions such as:
Where is sensitive information stored?
Who owns it?
How is it classified?
Where is it being used?
How does it flow between systems?
Purview capabilities help organizations build this governance and compliance picture.
24. Real-world banking example
Imagine a bank has:
Customer Name
Account Number
Credit Card
Phone
Transaction History
spread across:
SQL databases
Data lakes
Fabric Lakehouses
Fabric Warehouses
Power BI
Governance can help:
Discover
↓
Classify
↓
Catalog
↓
Apply governance/protection
↓
Track lineage
↓
Monitor
This is much more manageable than manually documenting thousands of systems.
25. Purview in your Fabric architecture
Combine everything you've learned:
DATA SOURCES
│
▼
DATA FACTORY
│
▼
OneLake
│
┌──────────┼──────────┐
▼ ▼ ▼
Lakehouse Warehouse Eventhouse
│ │ │
└──────────┼──────────┘
▼
Semantic Models
│
▼
Power BI
GOVERNANCE / PROTECTION
│
▼
MICROSOFT PURVIEW
│
┌──────────────┼──────────────┐
▼ ▼ ▼
Catalog Lineage Classification
│ │ │
└──────────────┼──────────────┘
▼
GOVERNED DATA ESTATE
26. Purview vs Eventhouse
Since you just studied Eventhouse, don't confuse them.
EVENTHOUSE
=
Store and analyze
real-time event data
PURVIEW
=
Govern, discover,
classify and protect data
They solve completely different problems.
27. Purview vs OneLake
Another useful distinction:
ONELAKE
=
Storage foundation
PURVIEW
=
Governance / protection capabilities
Conceptually:
Microsoft Fabric
OneLake
│
Stores Data
│
▼
Fabric Data Items
│
▼
Governance Layer
│
▼
Microsoft Purview
28. Purview vs Microsoft Entra ID
Don't confuse these either.
Microsoft Entra ID
Primarily answers:
Who is the user?
Identity
Authentication
Groups
Users
Microsoft Purview
Addresses areas such as:
What data exists and how should it be governed/protected?
Catalog
Classification
Lineage
Governance
Information Protection
Compliance
They work together as parts of Microsoft's broader security and governance ecosystem.
29. Important concepts to memorize
For Microsoft Fabric interviews, remember:
MICROSOFT PURVIEW ============================
1. Data Governance
2. Data Discovery
3. Data Catalog
4. Metadata Management
5. Data Classification
6. Sensitivity Labels
7. Data Lineage
8. Impact Analysis
9. Data Protection
10. Compliance / Risk Management
30. Interview question — What is Microsoft Purview?
A strong answer:
Microsoft Purview is Microsoft's set of data governance, security, risk, and compliance solutions. In a Microsoft Fabric environment, Purview capabilities help organizations discover and understand data assets, manage metadata, classify sensitive information, apply sensitivity and protection policies, and understand data lineage across analytical workloads.
31. Interview question — What is Data Lineage?
Data lineage shows the origin, movement, and transformation of data across systems. For example, it can help trace data from a source database through a Fabric pipeline, Lakehouse or Warehouse, semantic model, and ultimately to a Power BI report. This is useful for troubleshooting, governance, auditing, and impact analysis.
Remember:
Source
↓
Pipeline
↓
Lakehouse
↓
Warehouse
↓
Power BI
= LINEAGE
32. Interview question — Classification vs Sensitivity Label
Classification identifies what kind of information the data contains, such as an email address or financial information. A sensitivity label indicates how sensitive the information is and can be associated with protection and governance policies, such as Confidential or Highly Confidential.
33. Quick revision
MICROSOFT PURVIEW
================================
DATA ESTATE
│
▼
MICROSOFT PURVIEW
│
┌──────────┼──────────┐
│ │ │
▼ ▼ ▼
DISCOVER GOVERN PROTECT
│ │ │
▼ ▼ ▼
Catalog Lineage Sensitivity
Metadata Owners Classification
│ │ │
└──────────┼──────────┘
▼
TRUSTED DATA
One diagram to remember
MICROSOFT FABRIC
│
┌─────────────────┼─────────────────┐
▼ ▼ ▼
Lakehouse Warehouse Eventhouse
│ │ │
└─────────────────┼─────────────────┘
▼
Power BI
│
▼
MICROSOFT PURVIEW
│
┌──────────────┼──────────────┐
▼ ▼ ▼
CATALOG LINEAGE CLASSIFICATION
│ │ │
Find Data Track Data Identify Data
│ │ │
└──────────────┼──────────────┘
▼
DATA GOVERNANCE
The easiest way to remember Purview
Ask these five questions:
1. What data do we have? → Catalog 2. Where is it? → Discovery 3. What does it contain? → Classification 4. Where did it come from and where does it go? → Lineage 5. How sensitive is it/how should it be protected? → Sensitivity & protection
For a Microsoft Fabric Data Engineer, the three Purview concepts I would learn first are:
Data Catalog + Data Classification + Data Lineage
Once these three are clear, the rest of Fabric governance becomes much easier to understand.
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