What's the difference between ETL and ELT?

ETL transforms data before loading - good when destination has limited compute. ELT loads raw data then transforms in the destination - leverages cloud warehouse compute power. ELT is increasingly popular.

How often should ETL run?

Depends on needs. Batch ETL: nightly, hourly, or scheduled. Real-time: continuous streaming. Consider: data freshness requirements, source system impact, and transformation complexity.

How do I handle ETL failures?

Implement: alerting, restart capabilities, idempotent loads (can rerun safely), checkpointing for long jobs, and audit logging. Plan for failures - they will happen.

Should I build or buy ETL tools?

Usually buy/use existing tools. Modern tools (Fivetran, Airbyte) handle common sources. Build custom only for unique requirements. Focus effort on transformation logic, not plumbing.

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ETL

Extract, Transform, Load

A data integration process that extracts data from sources, transforms it to fit operational needs, and loads it into a destination system like a data warehouse.

In-Depth Explanation

ETL (Extract, Transform, Load) is a data integration pattern for moving data between systems. It's fundamental to data warehousing, analytics, and data migration projects.

ETL stages:

Extract: Pull data from source systems (databases, APIs, files)
Transform: Clean, validate, restructure, enrich data
Load: Write to destination (warehouse, database, lake)

Common transformations:

Data type conversion
Deduplication
Null handling
Joining/merging datasets
Aggregation and calculations
Data validation and quality checks
Format standardisation

ETL vs ELT:

ETL: Transform before loading (traditional)
ELT: Load raw, transform in destination (modern cloud approach)

ETL tools:

Traditional: Informatica, Talend, SSIS
Modern: dbt, Fivetran, Airbyte, Stitch

Business Context

ETL powers business intelligence and analytics. Without ETL, data stays siloed and unusable for cross-system reporting and analysis.

How Clever Ops Uses This

We implement ETL pipelines for Australian businesses to consolidate data for analytics, migrate between systems, and enable data-driven decision-making.

Example Use Case

"Nightly ETL pulling sales from POS, inventory from ERP, and customers from CRM into a data warehouse for unified reporting dashboards."

Frequently Asked Questions

Related Terms

Data Warehouse Data Pipeline

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