How to Route Data to Datalake and Database Using Multiple Filters

$0.00

Book a Demo
Workflow Name:

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

Aggregate & distribute filtered records to Datalake & DB
Benefit:

Faster dual-target data availability
Who Uses It:

Data Teams; IT
System Type:

Data Integration Workflow
On-Premise Supported:

Yes
Industry:

Analytics / Data Engineering
Outcome:

Filtered records sent to Datalake & Database
Category:

Description

Problem Before:

Manual distribution of filtered data
Solution Overview:

Automated aggregation and distribution to Datalake and Database using filters
Key Features:

Filter; validate; aggregate; distribute
Business Impact:

Faster; accurate dual-target ingestion
Productivity Gain:

Removes manual distribution
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 aggregates and distributes records after applying multiple filter conditions, sending them simultaneously to both a database and a Datalake. This ensures dual-target data is available quickly and accurately.

Advanced Filtering for Efficient Dual-Target Data Delivery

The system applies multiple predefined filters to incoming data, validates the results, and routes the refined records to the target database and Datalake in near real time. This workflow helps data and IT teams maintain consistent, structured datasets, improve processing speed, and reduce manual effort.

Watch Demo

Video Title:

API to API integration using 2 filter operations
Duration:

6:51

Outcome & Benefits

Time Savings:

Removes manual distribution
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:

Datalake & Database
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:

Datalake & Database
Target Name:

Datalake & Database
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 (Aggregate & Distribute)?

It is a data integration workflow that aggregates filtered records and distributes them to both a database and a Datalake, ensuring faster dual-target data availability.

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

The workflow evaluates multiple predefined filter conditions on the source data and aggregates records that meet all conditions before distributing them to both targets.

3. What types of sources are supported?

The workflow supports data from APIs, databases, and files, applying all filters consistently before aggregation and distribution.

4. How frequently can the workflow run?

The workflow can run on a schedule, in near real-time, or on-demand depending on operational and analytics needs.

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 sent to either the database or Datalake.

6. Who typically uses this workflow?

Data teams and IT teams use this workflow to ensure aggregated, filtered data is quickly available in both the database and Datalake for analytics and operations.

7. Is on-premise deployment supported?

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

8. What are the key benefits of this workflow?

It enables faster dual-target data availability, improves data quality, reduces manual effort, ensures consistent aggregation, and supports efficient analytics and operational workflows.

Case Study

Customer Name:

Data Team
Problem:

Manual distribution of filtered data
Solution:

Automated dual-target aggregation & distribution
ROI:

Faster workflows; reduced errors
Industry:

Analytics / Data Engineering
Outcome:

Filtered records sent to Datalake & Database

Related products