Use Filters Condition for API to Datalake Integration

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Workflow Name:

API to Datalake Workflow with 8 Filters operations
Purpose:

Filter and structure incoming data for Datalake
Benefit:

Reduces manual effort; ensures quality
Who Uses It:

Data Engineers; Analysts
System Type:

ETL / Data Integration / Datalake Platform
On-Premise Supported:

Yes
IPSec Guide Link:

https://your-domain.com/help/ipsec
Supported Protocols:

HTTPS; SFTP; JDBC; API
Industry:

Finance; Healthcare; Retail; Tech; All data-driven
Outcome:

3x faster ingestion; 90% fewer errors; ready analytics.
Category: Tags: , , , , , , , , , , Industry:

Description

Problem Before:

Manual filtering caused errors and delays.
Solution Overview:

Automates 8-filter workflow for clean Datalake ingestion.
Key Features:

Validation; transformation; filtering; batch load; logging.
Business Impact:

Faster ingestion; fewer errors; ready analytics.
Productivity Gain:

3x throughput with same team.
Cost Savings:

30% operational cost reduction.
Security & Compliance:

Audit-ready logs

API to Datalake Workflow with 8 Filters – Streamlined Data Pipeline

This workflow applies an filter layer to incoming API data before it reaches the Datalake. The automation validates, cleans, and structures raw API responses using eight predefined filters, ensuring the data entering the Datalake is accurate, consistent, and analytics ready.

Automated Data Filtering & Structuring for Reliable Datalake Ingestion

By enforcing automated transformation rules, the workflow eliminates manual cleanup, reduces errors, and maintains high-quality datasets across the Datalake. This enables data engineers and analysts to work with trusted, well-structured data without operational overhead.

Watch Demo

Video Title:

Integrating Google Sheets with any Datalake using eZintegrations
Duration:

4:56

Outcome & Benefits

Time Savings:

Cut processing time 90%
Cost Reduction:

Save $40K/yr
Accuracy:

99.9% valid records
Productivity:

+35% throughput/FTE

Industry & Function

Function:

Data Engineering; Analytics; BI
System Type:

ETL / Data Integration / Datalake Platform
Industry:

Finance; Healthcare; Retail; Tech; All data-driven

Functional Details

Use Case Type:

ETL / Data Pipeline
Source Object:

JSON API payloads
Target Object:

Structured Datalake tables
Scheduling:

Hourly / Daily / Event-driven
Primary Users:

Data Engineers; BI Analysts
KPI Improved:

Data accuracy; ingestion speed
AI/ML Step:

Optional anomaly detection
Scalability Tier:

Enterprise-ready; cloud/on-prem

Technical Details

Source Type:

API
Source Name:

Source API
API Endpoint URL:

https://api.source.com/data
HTTP Method:

GET/POST
Auth Type:

OAuth2 / API Key
Rate Limit:

1000 requests/min
Pagination:

Offset / Cursor-based
Schema/Objects:

JSON payloads
Transformation Ops:

Filter; Map; Normalize; Aggregate
Error Handling:

Retry; Dead-letter queue; Alerts
Orchestration Trigger:

Scheduled / Event-driven
Batch Size:

10k-50k records
Parallelism:

Multi-threaded / Concurrent pipelines
Target Type:

Datalake
Target Name:

Cloud Datalake
Target Method:

Bulk upload / API ingest
Ack Handling:

Confirmed on successful load
Throughput:

50k-100k records/hour
Latency:

5–15 mins per batch
Logging/Monitoring:

Centralized logs; dashboards; alerts

Connectivity & Deployment

On-Premise Supported:

Yes
IPSec Guide Link:

https://your-domain.com/help/ipsec
Supported Protocols:

HTTPS; SFTP; JDBC; API
Cloud Support:

AWS; Azure; GCP
Security & Compliance:

Audit-ready logs

FAQ

1. What is the API to Datalake Workflow with 8 ETL Filters?

It is an automated data pipeline that retrieves data from APIs, applies eight ETL filters for cleaning and transformation, and loads structured data into the Datalake.

2. How does the workflow ingest and process API data?

The workflow connects to API endpoints, fetches raw JSON or XML data, validates and transforms the dataset using the defined eight filters, and stores the processed data in the Datalake.

3. What ETL filters are included in this workflow?

Common filters include null removal, duplicate cleanup, schema mapping, field standardization, type validation, enrichment rules, transformations, and quality scoring.

4. Why are ETL filters important for API-to-Datalake pipelines?

API data can be inconsistent or messy. The filters ensure accuracy, consistency, and readiness for analytics and downstream processing.

5. Can the eight filters be customized?

Yes, the filters can be customized based on API response formats, business rules, or Datalake schema requirements.

6. Who typically uses this workflow?

Data Engineers and Analysts rely on this workflow to maintain high-quality, standardized data in the Datalake.

7. What platforms or tooling does this workflow support?

It works across ETL/ELT platforms, API integration tools, and cloud Datalakes such as AWS, Azure, and GCP.

8. What are the key benefits of automating API-to-Datalake processing?

It eliminates manual data preparation, improves data quality, accelerates ingestion, and ensures reliable datasets for analytics and reporting.

Case Study

Customer Name:

ACME Corp
Problem:

Manual data cleaning caused 50% delays in analytics pipelines.
Solution:

Deployed 8-filter automated API → Datalake workflow.
ROI:

30% operational cost reduction; ROI in 6 months.
Industry:

Finance; Healthcare; Retail; Tech; All data-driven
Outcome:

3x faster ingestion; 90% fewer errors; ready analytics.

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