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IJONIS
Data & InfrastructureETL

ETL Pipeline

ETL

An ETL pipeline (Extract, Transform, Load) is an automated data process that extracts raw data from various sources, transforms it into a unified format, and loads it into a target system. ETL pipelines form the backbone of every data infrastructure — ensuring data from ERP, CRM, and other systems is clean and consistent.

Why does this matter?

Without ETL pipelines, enterprise data remains trapped in silos: the ERP does not know what the CRM knows, and AI cannot access either system. Clean ETL pipelines are the prerequisite for every AI project, every dashboard, and every data-driven decision in mid-sized companies.

How IJONIS uses this

We build ETL pipelines with Apache Airflow, dbt, and Python — depending on complexity and existing systems. For AI projects, we extend classic ETL with embedding generation and vector indexing. Every pipeline includes monitoring, alerting, and automatic error handling.

Frequently Asked Questions

What is the difference between ETL and ELT?
With ETL, data is transformed before loading; with ELT, afterward in the target system. ELT is common with modern cloud data warehouses because compute power is cheaper there. We recommend ELT for analytical scenarios and ETL for real-time integrations with defined data formats.
How long does it take to build an ETL pipeline?
A simple pipeline with two to three source systems is ready in one to two weeks. Complex integrations with ERP connections, data quality checks, and historical data migration take four to eight weeks. Data source quality and API availability are the key factors.
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