How to Ingest Data into a Datalake Using Multiple Filter Conditions

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Workflow Name:

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

Ingest filtered records into Datalake
Benefit:

Structured data in Datalake
Who Uses It:

Data Teams; Analytics
System Type:

Data Integration Workflow
On-Premise Supported:

Yes
Industry:

Analytics / Data Engineering
Outcome:

Filtered records ingested into Datalake
Category:

Description

Problem Before:

Manual ingestion from multiple sources
Solution Overview:

Automated ingestion using multiple filter conditions
Key Features:

Filter; validate; ingest; schedule
Business Impact:

Faster; accurate data availability
Productivity Gain:

Removes manual ingestion
Cost Savings:

Reduces labor and errors
Security & Compliance:

Secure connection

With Target as Datalake but More Than 1 Filter Condition

The Datalake Multi Filter Workflow ingests records after applying multiple filter conditions, ensuring only high-quality and relevant data is loaded into the Datalake. This supports reliable analytics and reporting.

Advanced Filtering for High-Quality Datalake Ingestion

The system applies multiple predefined filters to incoming data, validates the results, and loads the refined records into the Datalake in near real time. This workflow helps data and analytics teams improve data quality, reduce noise, and maintain well-structured datasets.

Watch Demo

Video Title:

API to API integration using 2 filter operations
Duration:

6:51

Outcome & Benefits

Time Savings:

Removes manual ingestion
Cost Reduction:

Lower operational overhead
Accuracy:

High via validation
Productivity:

Faster ingestion

Industry & Function

Function:

Data Ingestion
System Type:

Data Integration Workflow
Industry:

Analytics / Data Engineering

Functional Details

Use Case Type:

Data Integration
Source Object:

Multiple Source Records
Target Object:

Datalake
Scheduling:

Real-time or batch
Primary Users:

Data Engineers; Analysts
KPI Improved:

Data availability; ingestion 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 failed ingestion
Orchestration Trigger:

On upload or scheduled
Batch Size:

Configurable
Parallelism:

Multi-source concurrent
Target Type:

Datalake
Target Name:

Datalake
Target Method:

API / Batch 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 connection

FAQ

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

It is a data integration workflow that ingests records into a Datalake after applying multiple filter conditions to ensure only relevant data is processed.

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

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

3. What types of data sources are supported?

The workflow supports data ingestion from APIs, databases, and files, applying multiple filters consistently across supported sources.

4. How frequently can the workflow run?

The workflow can run on a scheduled basis or in near real-time depending on data freshness and analytics requirements.

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

Records that do not satisfy one or more filter conditions are excluded and are not ingested into the Datalake.

6. Who typically uses this workflow?

Data teams and analytics teams use this workflow to ensure high-quality, structured data ingestion into the Datalake.

7. Is on-premise deployment supported?

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

8. What are the key benefits of this workflow?

It improves data quality, ensures structured and relevant data in the Datalake, reduces noise, and supports efficient analytics and reporting.

Case Study

Customer Name:

Data Team
Problem:

Manual ingestion from multiple sources
Solution:

Automated filtered ingestion
ROI:

Faster workflows; reduced errors
Industry:

Analytics / Data Engineering
Outcome:

Filtered records ingested into Datalake

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