How to Route Data to a Database and Datalake Using Multiple Filters

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Workflow Name:

More Than 1 Filter Condition with Database and Datalake as Target
Purpose:

Route filtered records to Database & Datalake
Benefit:

Simultaneous data availability in DB & Datalake
Who Uses It:

Data Teams; IT
System Type:

Data Integration Workflow
On-Premise Supported:

Yes
Industry:

Analytics / Data Engineering
Outcome:

Filtered records sent to Database & Datalake
Category:

Description

Problem Before:

Manual routing of filtered data
Solution Overview:

Automated routing to Database and Datalake using multiple filters
Key Features:

Filter; validate; route; schedule
Business Impact:

Faster; accurate dual-target ingestion
Productivity Gain:

Removes manual routing
Cost Savings:

Reduces labor and errors
Security & Compliance:

Secure connections

More Than 1 Filter Condition with Database and Datalake as Target

The Database & Datalake Multi Filter Workflow routes records after applying multiple filter conditions, sending them simultaneously to both a database and a Datalake. This ensures relevant and high-quality data is available across platforms.

Advanced Filtering for Synchronized Data Storage

The system applies multiple predefined filters to incoming data, validates the results, and loads the refined records into the target database and Datalake in near real time. This workflow helps data and IT teams maintain consistent, structured datasets while reducing manual effort and improving data reliability.

Watch Demo

Video Title:

API to API integration using 2 filter operations
Duration:

6:51

Outcome & Benefits

Time Savings:

Removes manual routing
Cost Reduction:

Lower operational overhead
Accuracy:

High via validation
Productivity:

Faster dual-target ingestion

Industry & Function

Function:

Data Routing
System Type:

Data Integration Workflow
Industry:

Analytics / Data Engineering

Functional Details

Use Case Type:

Data Integration
Source Object:

Multiple Source Records
Target Object:

Database & Datalake
Scheduling:

Real-time or batch
Primary Users:

Data Engineers; Analysts
KPI Improved:

Data availability; processing speed
AI/ML Step:

Not required
Scalability Tier:

Enterprise

Technical Details

Source Type:

API / Database / Email
Source Name:

Multiple Sources
API Endpoint URL:

–
HTTP Method:

–
Auth Type:

–
Rate Limit:

–
Pagination:

–
Schema/Objects:

Filtered records
Transformation Ops:

Filter; validate; normalize
Error Handling:

Log and retry failures
Orchestration Trigger:

On upload or scheduled
Batch Size:

Configurable
Parallelism:

Multi-source concurrent
Target Type:

Database & Datalake
Target Name:

Database & Datalake
Target Method:

Insert / Upload
Ack Handling:

Logging
Throughput:

High-volume records
Latency:

Seconds/minutes
Logging/Monitoring:

ingestion logs

Connectivity & Deployment

On-Premise Supported:

Yes
Supported Protocols:

API; DB; Email
Cloud Support:

Hybrid
Security & Compliance:

Secure connections

FAQ

1. What is the 'More Than 1 Filter Condition with Database and Datalake as Target' workflow?

It is a data integration workflow that routes filtered records to both a database and a Datalake after applying multiple filter conditions, ensuring simultaneous data availability in both targets.

2. How do multiple filter conditions work in this workflow?

The workflow evaluates more than one predefined filter condition on the source data and routes only the records that satisfy all conditions to the database and Datalake.

3. What types of source systems are supported?

The workflow supports APIs, databases, and file-based sources, applying the filters consistently before routing data to the targets.

4. How frequently can the workflow run?

The workflow can run on a scheduled basis, near real-time, or on-demand depending on data processing and operational requirements.

5. What happens to records that do not meet the filter conditions?

Records that do not satisfy all filter conditions are excluded and are not routed to either the database or the Datalake.

6. Who typically uses this workflow?

Data teams and IT teams use this workflow to ensure filtered, high-quality data is available simultaneously in both the database and Datalake for analytics and operations.

7. Is on-premise deployment supported?

Yes, this workflow supports on-premise data sources as well as hybrid environments.

8. What are the key benefits of this workflow?

It ensures simultaneous data availability in both database and Datalake, improves data quality, reduces manual routing effort, and supports efficient analytics and operational workflows.

Case Study

Customer Name:

Data Team
Problem:

Manual routing of filtered data
Solution:

Automated dual-target ingestion
ROI:

Faster workflows; reduced errors
Industry:

Analytics / Data Engineering
Outcome:

Filtered records sent to Database & Datalake

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