Consumer Getting Started

This guide walks you through connecting to the EA Consent Delta Share as a consumer and running your first consent queries.

Table of Contents

  1. Consumer Getting Started
    1. Prerequisites
      1. 1. Delta Sharing Client
      2. 2. Credentials from Your Data Provider
      3. 3. Understanding of EA Consent Tables
    2. Connection Methods
      1. Option A: Python Client (delta-sharing)
      2. Option B: Databricks
      3. Option C: Apache Spark
    3. Testing Your Connection
      1. List Available Tables
      2. Verify Table Access
    4. Your First Query
      1. Basic Query: Recent Consents
      2. Understanding the Response
      3. Filter Active Consents
    5. Next Steps
    6. Troubleshooting
      1. Common Issues
      2. Getting Help

Prerequisites

Before connecting as a consumer, you’ll need:

1. Delta Sharing Client

Choose a Delta Sharing client for your environment:

Client Use Case Installation
Python Data analysis, scripting pip install delta-sharing
Apache Spark Large-scale processing Delta Sharing connector
Databricks Enterprise analytics Built-in Unity Catalog support

External Resources:

2. Credentials from Your Data Provider

Your data provider will supply:

Credential Description
Profile File JSON file containing endpoint URL and bearer token
Share Name Name of the share containing EA Consent tables
Schema Name Schema within the share (typically ea-consent-schema)

Example Profile File (profile.json):

{
  "shareCredentialsVersion": 1,
  "endpoint": "https://delta-sharing.your-provider.com/delta-sharing/",
  "bearerToken": "your-bearer-token-here"
}

Store this file securely - it contains your authentication credentials.

The EA Consent Delta Share exposes two tables:

Table Purpose When to Use
ea-consent-tb Full Trust Block payload Discover consents using subject binding digest or linkage tokens, retrieve complete Verifiable Credentials for verification
ea-consent-verification-keys Signature verification Get public keys to verify Trust Block signatures

See EA Consent Delta Sharing Schema for complete specifications.

Connection Methods

Choose the connection method that matches your environment.

Option A: Python Client (delta-sharing)

Install the package:

pip install delta-sharing

Connect and list available tables:

import delta_sharing

# Path to your profile file
profile_file = "/path/to/profile.json"

# Create a sharing client
client = delta_sharing.SharingClient(profile_file)

# List all available tables
tables = client.list_all_tables()
for table in tables:
    print(f"{table.share}.{table.schema}.{table.name}")

Load a table as a pandas DataFrame:

# Construct table URL: profile_path + "#share.schema.table"
table_url = f"{profile_file}#ea-consent-share.ea-consent-schema.ea-consent-tb"

# Load the table
df = delta_sharing.load_as_pandas(table_url)

# Or load a sample for testing
df_sample = delta_sharing.load_as_pandas(table_url, limit=10)

Option B: Databricks

If your organization uses Databricks, you can connect using Unity Catalog’s Delta Sharing recipient feature.

Configure the share in Databricks:

  1. Navigate to Data > Delta Sharing > Shared with me
  2. Click Add provider and enter your provider’s sharing identifier
  3. Accept the share invitation using the activation link from your provider

Query the shared tables:

-- List available shares
SHOW SHARES IN PROVIDER your_provider_name;

-- Query the consent Trust Block table
SELECT *
FROM your_provider_name.ea_consent_schema.`ea-consent-tb`
LIMIT 10;

Note: In Databricks SQL, table names with hyphens require backticks.

Option C: Apache Spark

For Spark environments outside Databricks:

# Configure Spark with Delta Sharing
spark = SparkSession.builder \
    .config("spark.jars.packages", "io.delta:delta-sharing-spark_2.12:1.0.0") \
    .getOrCreate()

# Load a shared table
df = spark.read.format("deltaSharing") \
    .load("/path/to/profile.json#share.schema.table")

Testing Your Connection

After setting up your client, verify connectivity:

List Available Tables 

import delta_sharing

client = delta_sharing.SharingClient("/path/to/profile.json")
tables = client.list_all_tables()

print("Available tables:")
for t in tables:
    print(f"  - {t.share}.{t.schema}.{t.name}")

Expected output:

Available tables:
  - ea-consent-share.ea-consent-schema.ea-consent-tb
  - ea-consent-share.ea-consent-schema.ea-consent-verification-keys

Verify Table Access 

# Try loading a small sample from the Trust Block table
table_url = f"{profile_file}#ea-consent-share.ea-consent-schema.ea-consent-tb"
df = delta_sharing.load_as_pandas(table_url, limit=5)
print(f"Successfully loaded {len(df)} rows")
print(f"Columns: {list(df.columns)}")

Your First Query

Once connected, query the ea-consent-tb table to discover and retrieve consents.

Basic Query: Recent Consents 

import delta_sharing

profile_file = "/path/to/profile.json"
table_url = f"{profile_file}#ea-consent-share.ea-consent-schema.ea-consent-tb"

# Load recent consents
df = delta_sharing.load_as_pandas(table_url, limit=100)

# View key fields
print(df[['consent_id', 'consent_issuer', 'trust_block_id', 'status_code', 'issuance_ts']])

Understanding the Response

Key fields in ea-consent-tb:

Field Description
consent_id Unique identifier for this consent
consent_issuer Entity that issued the consent
trust_block_id Trust Block identifier containing this consent
status_code "active" or "inactive"
issuance_ts When the consent was issued (UTC)
subject_binding_digest Privacy-preserving subject correlation key
linkage_1_token Cross-party correlation token (if present)
trust_block Complete Trust Block JWT

Filter Active Consents 

# Load and filter in pandas
df = delta_sharing.load_as_pandas(table_url)
active_consents = df[df['status_code'] == 'active']
print(f"Found {len(active_consents)} active consents")

Next Steps

Now that you’re connected:

  1. Query Patterns - Learn common patterns for incremental retrieval, subject lookup, and joining tables

  2. Trust Block Verification - Verify cryptographic signatures on consent records

  3. Schema Reference - Complete column definitions and table specifications

Troubleshooting

Common Issues

Issue Cause Solution
ConnectionError Invalid endpoint URL Verify endpoint in profile file matches provider’s URL
AuthenticationError Invalid or expired token Request new credentials from your data provider
TableNotFoundError Wrong share/schema/table name Use client.list_all_tables() to see available tables
Permission denied Token lacks access to requested table Contact your data provider to verify permissions

Getting Help