Data Sync using Pagination Body (OAuth 2.0)
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| Workflow Name: |
Partner Data Sync to Datalake Using Pagination Body (OAuth 2.0) |
|---|---|
| Purpose: |
Automatically extract partner data via API with pagination and sync it to the Datalake. |
| Benefit: |
Ensures complete, accurate, and real-time partner data for analytics and operational use cases. |
| Who Uses It: |
Data Engineers; Operations Teams; Analytics Teams |
| System Type: |
Partner Data Integration Workflow |
| On-Premise Supported: |
Yes (via secure proxy) |
| Supported Protocols: |
HTTPS; REST API |
| Industry: |
E-commerce / Enterprise Data Operations |
| Outcome: |
Accurate, real-time, and fully structured partner data in Datalake |
Table of Contents
Description
| Problem Before: |
Manual partner data extraction was slow, incomplete, and prone to errors, especially for large datasets. |
|---|---|
| Solution Overview: |
Automated API calls with body-based pagination; transformation; and secure Datalake push. |
| Key Features: |
OAuth 2.0 API; pagination handling; data mapping; batch logging; error retries. |
| Business Impact: |
Improves reporting accuracy, reduces manual effort, and increases trust in partner data. |
| Productivity Gain: |
Teams avoid manual extraction and reconciliation; saving hours weekly. |
| Cost Savings: |
Reduces operational overhead and data errors through automated ingestion. |
| Security & Compliance: |
OAuth 2.0 tokens encrypted; API compliance |
Data Sync – Partner Data to Datalake Using Pagination Body (OAuth 2.0)
Data Sync automates the extraction of partner data via API using pagination and OAuth 2.0 authentication. This workflow ensures all partner data is captured completely, processed efficiently, and synchronized into the Datalake for analytics and operational reporting.
Accurate & Complete Partner Data with Seamless Pagination
The workflow leverages API pagination to retrieve large datasets without missing records, while OAuth 2.0 ensures secure access. Partner data is validated, structured, and updated in near real time, enabling reliable insights, improved reporting accuracy, and streamlined operational efficiency.
Watch Demo
| Video Title: |
How eZintegrations™ AI Document Understanding process documents & its privacy? |
|---|---|
| Duration: |
1:50 |
Outcome & Benefits
| Time Savings: |
Manual extraction eliminated; real-time updates |
|---|---|
| Cost Reduction: |
Removes manual API extraction overhead |
| Accuracy: |
High accuracy with automated validation |
| Productivity: |
Faster ingestion cycles; zero manual intervention |
Industry & Function
| Function: |
Data Extraction; Sync; Automation |
|---|---|
| System Type: |
Partner Data Integration Workflow |
| Industry: |
E-commerce / Enterprise Data Operations |
Functional Details
| Use Case Type: |
Partner Data Sync |
|---|---|
| Source Object: |
Partner data records via API |
| Target Object: |
Datalake tables for analytics & operations |
| Scheduling: |
Hourly or daily |
| Primary Users: |
Data Engineers; Ops Teams |
| KPI Improved: |
Data completeness; sync reliability; reporting accuracy |
| AI/ML Step: |
Optional anomaly detection for unusual data patterns |
| Scalability Tier: |
Mid-to-Enterprise; supports high-volume APIs |
Technical Details
| Source Type: |
REST API (Pagination Body) |
|---|---|
| Source Name: |
Partner Data Management API |
| API Endpoint URL: |
https://api.partner.com/data |
| HTTP Method: |
POST |
| Auth Type: |
OAuth 2.0 |
| Rate Limit: |
Depends on API plan and throttling rules |
| Pagination: |
Body-based pagination using limit/offset or nextPage token |
| Schema/Objects: |
Entity details; partner metadata; attributes; timestamps |
| Transformation Ops: |
Data mapping; normalization; deduplication; timestamp conversion |
| Error Handling: |
Retry logic; logging; rate-limit handling |
| Orchestration Trigger: |
Hourly; daily; or on-demand |
| Batch Size: |
500 -5,000 records per batch |
| Parallelism: |
Multi-threaded API fetch |
| Target Type: |
Cloud Datalake |
| Target Name: |
PartnerData_Datalake |
| Target Method: |
API push / cloud storage write |
| Ack Handling: |
Success/failure logs stored in monitoring dashboard |
| Throughput: |
Up to 20K records/hour |
| Latency: |
<30 seconds per batch |
| Logging/Monitoring: |
Execution logs; API response logs; error notifications |
Connectivity & Deployment
| On-Premise Supported: |
Yes (via secure proxy) |
|---|---|
| Supported Protocols: |
HTTPS; REST API |
| Cloud Support: |
AWS; Azure; GCP Datalakes |
| Security & Compliance: |
OAuth 2.0 tokens encrypted; API compliance |
FAQ
1. What is the Partner Data Sync to Datalake Using Pagination Body (OAuth 2.0) workflow?
It is an automated partner data integration workflow that securely extracts partner data from APIs using OAuth 2.0 authentication and pagination in the request body, and syncs the data to a Datalake.
2. How does this workflow extract partner data?
The workflow authenticates using OAuth 2.0, sends paginated API requests with pagination parameters in the request body, retrieves all available partner records, and loads them into the Datalake.
3. What types of partner data can be synced?
It can sync partner-related data such as partner profiles, identifiers, statuses, metadata, and any additional attributes exposed by the partner API.
4. How does pagination ensure complete data extraction?
Pagination allows the workflow to retrieve large datasets in controlled batches, ensuring that all partner records are extracted without data loss or API timeouts.
5. How frequently can the workflow run?
The workflow can run on a scheduled or near real-time basis depending on business and analytics requirements.
6. Who typically uses this workflow?
Data engineers, operations teams, and analytics teams use this workflow to maintain accurate and up-to-date partner data in the Datalake.
7. Is on-premise deployment supported?
Yes, on-premise systems are supported through a secure proxy that enables safe API communication with the Datalake.
8. What are the key benefits of this workflow?
It ensures complete and accurate partner data extraction, supports secure OAuth-based access, reduces manual data handling, and enables reliable analytics and operational reporting.
Resources
Case Study
| Customer Name: |
Partner Analytics & Data Ops Team |
|---|---|
| Problem: |
Incomplete partner data due to manual extraction and large datasets |
| Solution: |
Automated API-based pipeline with pagination and OAuth 2.0 to sync data to Datalake |
| ROI: |
Complete partner data available 2× faster for analytics |
| Industry: |
E-commerce / Enterprise Data Operations |
| Outcome: |
Accurate, real-time, and fully structured partner data in Datalake |
