ETL (Extract Transform Load)

What Is ETL?

ETL stands for Extract, Transform, Load—the three steps of moving data from source systems to a destination where it can be analyzed. ETL has been the standard approach to data integration for decades, forming the backbone of data warehousing and business intelligence.

Extract: Pull data from source systems Transform: Clean, restructure, and enrich the data Load: Write the processed data to the target system

The ETL Process

Extract

Pulling data from source systems:

Full extraction: Copy all data from the source

• Simple but slow for large datasets
• Good for initial loads

Incremental extraction: Copy only new or changed data

• Faster and more efficient
• Requires tracking changes (timestamps, flags)

Change data capture (CDC): Capture changes in real-time

• Most efficient for high-volume sources
• Requires source system support

Transform

Processing data between extraction and loading:

Data cleaning

• Handle missing values
• Fix data type issues
• Remove duplicates
• Correct errors

Data mapping

• Rename fields to target schema
• Convert codes to descriptions
• Standardize formats (dates, currencies)

Data integration

• Join data from multiple sources
• Resolve conflicts (which source wins?)
• Create unified records

Data enrichment

• Calculate derived fields
• Apply business logic
• Add lookup values

Data aggregation

• Summarize detail to totals
• Create rollups and hierarchies
• Pre-calculate metrics

Load

Writing processed data to the target:

Full load: Replace all existing data

• Simple but slow
• Good for small datasets or complete refreshes

Incremental load: Add or update changed records

• Efficient for large datasets
• Requires merge logic

Append: Add new records only

• Fastest option
• Good for transaction tables

ETL vs. ELT

Modern cloud data warehouses enable a different approach:

Traditional ETL

Source → Extract → Transform → Load → Warehouse

• Transform before loading
• Requires separate transformation server
• Limited by transformation capacity

Modern ELT

Source → Extract → Load → Transform → Ready for Use
         (in warehouse)

• Load raw data first
• Transform in the warehouse
• Leverage warehouse computing power
• More flexible and scalable

Go Fig uses ELT patterns to leverage modern cloud infrastructure. You can see how this works in practice with Go Fig’s visual workflow builder.

ETL Tools Landscape

Traditional ETL Tools

• Informatica PowerCenter
• IBM DataStage
• Microsoft SSIS
• Talend

Modern ELT/Pipeline Tools

• Fivetran
• Airbyte
• dbt (transformation)
• Go Fig (end-to-end for finance)

Cloud-Native Options

• AWS Glue
• Azure Data Factory
• Google Cloud Dataflow

ETL Challenges

Complexity: Many sources with different structures

Performance: Processing large volumes quickly

Data quality: Garbage in, garbage out

Maintenance: Source changes break pipelines

Skills gap: Requires specialized expertise

Time to value: Months to build and deploy

How Go Fig Approaches ETL

Go Fig handles ETL complexity for finance teams:

Pre-built extracts: Connectors for 100+ sources ready to use

Managed transformations: We build the transformation logic for you

Semantic layer: Business-friendly output, not raw data

Excel delivery: Final destination can be your spreadsheets

No coding required: Finance teams use it directly

White-glove service: Our team builds and maintains pipelines

You get clean, transformed data without becoming an ETL expert.

ETL Best Practices

1. Design for change: Sources will evolve; build flexibility
2. Validate early: Check data quality before loading
3. Log everything: Audit trail for debugging and compliance
4. Test with production data: Synthetic data hides real issues
5. Monitor continuously: Know when pipelines fail
6. Document transformations: Explain business logic applied