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Sensitivity Level & Trust Scores

Sensitivity Level & Trust Score

This hands-on workshop teaches you how to implement an automated solution for bulk updating Trust Scores and Sensitivity levels across your entire data catalog. You'll learn to extract entity data, calculate metrics, join data using Pentaho, and perform bulk updates efficiently.

By the end of this workshop, you will be able to:

  • Extract entity data from your data catalog with hierarchical names

  • Join calculated Trust Score and Sensitivity values using Pentaho Data Integration

  • Perform bulk updates across all schemas, tables, and columns

  • Validate and monitor the update process

  • Troubleshoot common issues

Dataflow

The solution consists of three main components:

  1. Entity Extraction Tool - Extracts all entities with hierarchical names from OpenSearch

  2. Pentaho Data Integration - Joins your calculated values with entity data

  3. Bulk Update Tool - Updates Trust Score and Sensitivity via API or OpenSearch

Expected Outcomes

  • Automated bulk updates of Trust Score (0-100) and Sensitivity (HIGH/MEDIUM/LOW)

  • Support for schema, table, and column level updates

  • Validation and error reporting

  • Scalable solution for thousands of entities

x

Entity Extraction

The extraction process retrieves all entities from your data catalog with their hierarchical relationships intact.

What Gets Extracted:

  • Entity unique identifiers (UUIDs)

  • Entity types (SCHEMA/TABLE/COLUMN)

  • Hierarchical names for joining

  • Current Trust Score and Sensitivity values

  • Fully qualified domain names (FQDNs)

Learning Objectives:

  • Understand the entity extraction process

  • Extract all entities with hierarchical names from your data catalog

  • Analyze the extracted data structure

  • Prepare data for joining with calculated metrics

  1. Run the extraction script:

# Change to Key_Metrics directory
cd
cd /home/pdc/Projects/APIs/Key_Metrics

# Extract all entities with full details
extract-entities \
    --opensearch-url http://localhost:9200 \
    --output data/output/entity_extraction.csv \
    --verbose

Expected output:

🔍 Extracting all entities from data catalog...
📊 Processing batch 1/25 (100 entities)...
📊 Processing batch 2/25 (100 entities)...
...
✅ Extracted 1,247 entities to data/output/entity_extraction.csv

📈 Entity Summary:
   - COLUMN: 892
   - TABLE: 343
   - SCHEMA: 12

  1. Take a look at the entity_extraction.csv

Column
Description
Example

entity_id

Unique identifier

ef60e629-4261-4ce6-8635-961ca4b1b420

entity_type

Type of entity

SCHEMA, TABLE, COLUMN

entity_name

Entity's actual name

Employee

schema_name

Schema name for joining

HumanResources

table_name

Table name (empty for schemas)

Employee

column_name

Column name (empty for schemas/tables)

FirstName

fqdn

Internal fully qualified name

688cc7b9c5759eae5fdcba07/...

fqdn_display

Human-readable path

mssql:adventureworks2022/...

current_trust_score

Existing trust score

48

current_sensitivity

Existing sensitivity

HIGH

new_trust_score

For your calculated values

(empty)

new_sensitivity

For your calculated values

(empty)

  1. Run a Data Quality Analysis

# Analyze extracted data
echo "=== Entity Type Distribution ==="
cut -d',' -f2 data/output/entity_extraction.csv | tail -n +2 | sort | uniq -c

echo -e "\n=== Top 5 Schemas ==="
cut -d',' -f4 data/output/entity_extraction.csv | tail -n +2 | sort | uniq -c | sort -rn | head -5

echo -e "\n=== Entities with Complete Hierarchy ==="
grep -v ',,,,' data/output/entity_extraction.csv | wc -l

echo -e "\n=== Sample Complete Entities ==="
grep -v ',,,,' data/output/entity_extraction.csv | head -3

# Count by schema
cut -d',' -f4 data/output/entity_extraction.csv | sort | uniq -c | head -10

# Find all schemas
awk -F',' '$2=="SCHEMA" {print $4}' data/output/entity_extraction.csv | sort | uniq

# Find all tables in HumanResources schema
awk -F',' '$2=="TABLE" && $4=="HumanResources" {print $5}' data/output/entity_extraction.csv

# Find all columns in Employee table
awk -F',' '$2=="COLUMN" && $4=="HumanResources" && $5=="Employee" {print $6}' data/output/entity_extraction.csv

  1. x

  2. x

  3. x

3. Run

Run Complete Extraction

cd /home/pdc/Projects/APIs/Key_Metrics

# Extract all entities with full details
extract-entities --opensearch-url http://localhost:9200 --output data/output/entity_extraction.csv
Expected Output Messages
 Extracting all entities from data catalog...
✅ Extracted 1,247 entities to data/output/entity_extraction.csv
 Edit the 'new_trust_score' and 'new_sensitivity' columns with your desired values

 Entity Summary:
  COLUMN: 892
  SCHEMA: 12
  TABLE: 343
 Step 2: Analyze Extracted Data
View Sample Data
bash
# Check file was created
ls -la data/output/entity_extraction.csv

# View first 5 rows
head -5 data/output/entity_extraction.csv

# Count total entities
wc -l data/output/entity_extraction.csv
Understanding the CSV Structure
csv
entity_id,entity_type,entity_name,schema_name,table_name,column_name,fqdn,fqdn_display,current_trust_score,current_sensitivity,new_trust_score,new_sensitivity
Column Explanations:

entity_id: Unique ID needed for bulk updates
entity_type: SCHEMA/TABLE/COLUMN for filtering
entity_name: The actual name of the entity
schema_name: Schema name for joining
table_name: Table name for joining (empty for schema-level)
column_name: Column name for joining (empty for schema/table-level)
fqdn
: Internal fully qualified name
fqdn_display: Human-readable path
current_trust_score: Existing trust score (if any)
current_sensitivity: Existing sensitivity (if any)
new_trust_score: Empty - for your calculated values
new_sensitivity: Empty - for your calculated values
 Step 3: Data Quality Checks
Check for Missing Names
bash
# Count entities with missing schema names
grep -c ',,,' data/output/entity_extraction.csv

# View entities with complete hierarchical names
grep -v ',,,' data/output/entity_extraction.csv | head -10
Analyze Entity Distribution
bash
# Count by entity type
cut -d',' -f2 data/output/entity_extraction.csv | sort | uniq -c

# Count by schema
cut -d',' -f4 data/output/entity_extraction.csv | sort | uniq -c | head -10
Sample Analysis Commands
bash
# Find all schemas
awk -F',' '$2=="SCHEMA" {print $4}' data/output/entity_extraction.csv | sort | uniq

# Find all tables in HumanResources schema
awk -F',' '$2=="TABLE" && $4=="HumanResources" {print $5}' data/output/entity_extraction.csv

# Find all columns in Employee table
awk -F',' '$2=="COLUMN" && $4=="HumanResources" && $5=="Employee" {print $6}' data/output/entity_extraction.csv
 Step 4: Prepare Sample Data for Testing
Create Test Dataset
bash
# Extract first 50 entities for testing
head -51 data/output/entity_extraction.csv > data/output/test_entities.csv
Create Sample Join Data
bash
# Create a sample calculated metrics CSV for testing
cat > data/input/sample_calculated_metrics.csv << 'EOF'
schema_name,table_name,column_name,calculated_trust_score,calculated_sensitivity
HumanResources,,,75,HIGH
HumanResources,Employee,,85,MEDIUM
HumanResources,Employee,FirstName,90,LOW
HumanResources,Employee,LastName,90,LOW
HumanResources,Employee,EmailAddress,70,HIGH
Sales,,,80,MEDIUM
Sales,Customer,,85,LOW
Sales,Customer,CustomerID,95,LOW
EOF
 Step 5: Validate Extraction Results
Check Data Completeness
bash
# Verify all expected columns are present
head -1 data/output/entity_extraction.csv | tr ',' '\n' | nl

# Check for any parsing errors
grep -n 'ERROR\|WARN' data/output/entity_extraction.csv || echo "No errors found"
Verify Hierarchical Names
bash
# Check schema-level entities
echo "=== SCHEMA ENTITIES ==="
awk -F',' '$2=="SCHEMA" {print "Schema: " $4 " (ID: " $1 ")"}' data/output/entity_extraction.csv | head -5

# Check table-level entities  
echo "=== TABLE ENTITIES ==="
awk -F',' '$2=="TABLE" {print "Table: " $4 "." $5 " (ID: " $1 ")"}' data/output/entity_extraction.csv | head -5

# Check column-level entities
echo "=== COLUMN ENTITIES ==="
awk -F',' '$2=="COLUMN" {print "Column: " $4 "." $5 "." $6 " (ID: " $1 ")"}' data/output/entity_extraction.csv | head -5
 Common Issues & Solutions
Issue: "No entities found"
bash
# Check OpenSearch connection
curl -s "http://localhost:9200/pdc_entities/_search?size=1"

# Check if OpenSearch container is running
docker ps | grep opensearch
Issue: "Empty schema/table/column names"
This is normal for some entity types
Focus on entities with complete hierarchical names for joining
Issue: "Extraction takes too long"
bash
# Extract smaller subset for testing
extract-entities --opensearch-url http://localhost:9200 --output data/output/small_test.csv
# Then manually limit the query size in the extraction tool
 Section 3 Checklist
 Full entity extraction completed successfully
 CSV file created with expected structure
 Entity counts match expected numbers (schemas, tables, columns)
 Hierarchical names (schema_name, table_name, column_name) populated correctly
 Sample calculated metrics CSV created for testing
 Data quality checks completed
 No critical errors in extraction process
 Ready for Next Section
With entity extraction complete, you now have:

Complete entity inventory with IDs
Hierarchical names for joining
Current Trust Score and Sensitivity values
Test data for validation
Next: Section 4 - Pentaho Data Integration (Joining Process)

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