How to Send Data to a Database Using Multiple Filter Conditions

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Workflow Name:

With Target as Database but More Than 1 Filter Condition
Purpose:

Store filtered records in database
Benefit:

Fast and organized data storage
Who Uses It:

Data Teams; IT
System Type:

Data Integration Workflow
On-Premise Supported:

Yes
Industry:

Analytics / Data Engineering
Outcome:

Filtered records stored in database
Category:

Description

Problem Before:

Manual database updates
Solution Overview:

Filter and store records automatically in database
Key Features:

Filter; validate; insert; schedule
Business Impact:

Improved data processing
Productivity Gain:

Removes manual DB inserts
Cost Savings:

Reduces labor
Security & Compliance:

Secure connection

With Target as Database but More Than 1 Filter Condition

The Database Multi Filter Workflow stores records in a database after applying multiple filter conditions, ensuring only relevant and high-quality data is persisted. This keeps databases organized and ready for analytics or operational use.

Advanced Filtering for Efficient Database Storage

The system applies multiple predefined filters to incoming data, validates the results, and inserts the refined records into the target database in near real time. This workflow helps data and IT teams improve storage efficiency, reduce noise, and maintain structured datasets.

Watch Demo

Video Title:

API to API integration using 2 filter operations
Duration:

6:51

Outcome & Benefits

Time Savings:

Removes manual DB updates
Cost Reduction:

Lower labor
Accuracy:

High via validation
Productivity:

Faster storage

Industry & Function

Function:

Data Storage
System Type:

Data Integration Workflow
Industry:

Analytics / Data Engineering

Functional Details

Use Case Type:

Data Integration
Source Object:

Multiple Source Records
Target Object:

Database
Scheduling:

Real-time or batch
Primary Users:

Data Engineers; IT
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
Target Name:

Database
Target Method:

Insert / Update
Ack Handling:

Logging
Throughput:

High-volume records
Latency:

Seconds/minutes
Logging/Monitoring:

DB logs

Connectivity & Deployment

On-Premise Supported:

Yes
Supported Protocols:

API; DB; Email
Cloud Support:

Hybrid
Security & Compliance:

Secure connection

FAQ

1. What is the 'With Target as Database but More Than 1 Filter Condition' workflow?

It is a data integration workflow that stores records in a database after applying multiple filter conditions, ensuring only relevant and high-quality data is persisted.

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

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

3. What types of sources are supported?

The workflow supports APIs, databases, and file-based sources, applying multiple filters consistently before storing data.

4. How frequently can the workflow run?

The workflow can run on a schedule, near real-time, or on-demand depending on data storage 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 stored in the database.

6. Who typically uses this workflow?

Data teams and IT teams use this workflow to ensure fast, organized, and controlled storage of filtered database records.

7. Is on-premise deployment supported?

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

8. What are the key benefits of this workflow?

It provides fast, organized, and relevant data storage, reduces unnecessary records, improves data quality, and supports efficient analytics and downstream processing.

Case Study

Customer Name:

Data Team
Problem:

Manual DB updates
Solution:

Automated filtered database insert
ROI:

Faster workflows; reduced errors
Industry:

Analytics / Data Engineering
Outcome:

Filtered records stored in database

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