How to use 1 Filter condition and send Data to Datalake

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Workflow Name:

With Target as Datalake and 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 data ingestion from multiple sources
Solution Overview:

Automated ingestion using 1 filter condition
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 and 1 Filter Condition

The Datalake 1 Filter Workflow ingests records after applying a single filter condition, ensuring only relevant data is sent to the Datalake. This approach helps maintain clean and structured datasets for downstream use.

Simple Filtering for Clean Datalake Ingestion

The system applies one predefined filter to incoming data, validates the results, and loads the filtered records into the Datalake. This workflow supports data and analytics teams by reducing noise, improving data quality, and enabling reliable analytics and reporting.

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 and 1 Filter Condition' workflow?

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

2. How does the filtering work in this workflow?

The workflow applies one predefined filter condition on the source data to select only matching records before ingesting them into the Datalake.

3. What types of data sources are supported?

The workflow can ingest data from APIs, databases, or files, applying the filter consistently across supported source types.

4. How frequently can the workflow run?

The workflow can run on a scheduled basis or on-demand depending on data freshness and analytics requirements.

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

Records that do not match the filter condition are excluded from ingestion, ensuring only relevant data is stored in the Datalake.

6. Who typically uses this workflow?

Data teams and analytics teams use this workflow to control data quality and ingest only filtered, structured records into the Datalake.

7. Is on-premise deployment supported?

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

8. What are the key benefits of this workflow?

It ensures structured, relevant data in the Datalake, reduces unnecessary storage, improves data quality, 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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