Data Pipeline

What Is a Data Pipeline?

A data pipeline is an automated system that moves data from one or more sources, transforms it along the way, and delivers it to a destination. Like plumbing that carries water through a building, data pipelines carry information through an organization, reliably and without manual intervention.

Data pipelines:

• Extract data from source systems
• Clean and transform data for use
• Load data into target destinations
• Run on schedules or triggers
• Handle errors and retries automatically

Why Data Pipelines Matter

Without Pipelines (Manual Process)

1. Export data from ERP to CSV
2. Open in Excel, clean up formatting
3. Copy-paste into reporting template
4. Repeat for each data source
5. Hope nothing changed while you worked

Problems: Time-consuming, error-prone, stale data, no audit trail

With Pipelines (Automated)

1. Pipeline extracts data automatically
2. Transformations apply business logic
3. Clean data arrives in destination
4. Runs on schedule without intervention
5. Errors trigger alerts

Benefits: Fast, accurate, fresh data, fully documented

Anatomy of a Data Pipeline

Source

Where data originates:

• ERP systems (NetSuite, QuickBooks, SAP)
• Databases (PostgreSQL, MySQL, SQL Server)
• SaaS applications (Salesforce, HubSpot)
• Files (Excel, CSV, JSON)
• APIs (REST, GraphQL)

Extraction

Pulling data from sources:

• Full extraction (all data)
• Incremental extraction (only changes)
• CDC (change data capture)
• API calls

Transformation

Processing data for use:

• Cleaning (fix errors, handle nulls)
• Mapping (rename fields, convert types)
• Joining (combine data from multiple sources)
• Aggregating (sum, average, count)
• Enriching (add calculated fields)

Loading

Delivering data to destinations:

• Data warehouses (Snowflake, BigQuery)
• Databases (PostgreSQL, MySQL)
• Files (Excel, CSV)
• Applications (dashboards, reports)

Orchestration

Managing pipeline execution:

• Scheduling (run at specific times)
• Dependencies (run after other pipelines)
• Error handling (retry, alert, skip)
• Monitoring (track success/failure)

Types of Data Pipelines

Batch Pipelines

Process data in scheduled batches:

• Run hourly, daily, or weekly
• Process large volumes efficiently
• Good for reporting and analytics
• Example: Nightly financial data refresh

Real-Time Pipelines

Process data as it arrives:

• Continuous streaming
• Low latency (seconds to minutes)
• Good for operational dashboards
• Example: Live sales monitoring

Hybrid Pipelines

Combine batch and real-time:

• Real-time for critical metrics
• Batch for detailed analysis
• Balance freshness and efficiency

Data Pipeline Challenges

Complexity: Many sources, transformations, and destinations to manage

Reliability: Pipelines must run consistently without failure

Scalability: Handle growing data volumes over time

Maintenance: Source systems change, requiring pipeline updates

Monitoring: Know when something goes wrong

Skills: Traditional pipelines require engineering expertise

How Go Fig Simplifies Data Pipelines

Go Fig handles pipeline complexity so you don’t have to:

Pre-built connectors: 100+ integrations ready to use

Visual pipeline builder: Create pipelines without code, see the data flow builder documentation for details

Managed infrastructure: We run and monitor pipelines for you

Automatic error handling: Retries, alerts, and recovery

Change management: Adapts when source systems change

Excel delivery: Pipelines that deliver directly to spreadsheets

Your data flows automatically; you focus on analysis.

Pipeline Best Practices

1. Start simple: Begin with critical data, expand over time
2. Document everything: Future you will thank present you
3. Build in monitoring: Know immediately when things break
4. Test thoroughly: Validate data quality at each step
5. Plan for failure: Pipelines will fail; have recovery plans
6. Version control: Track changes to pipeline logic