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.

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