The Core Concept

Data integration is the process of combining data from different sources into a unified, consistent view. It’s how you turn disconnected systems into a coherent picture of your business.

If your CRM, billing system, and product database each have their own version of “customer,” data integration is what connects them. Without it, every team works with a partial view, reports contradict each other, and simple questions require manual data gathering.

Data integration isn’t optional anymore. It’s the foundation that makes analytics, reporting, and data-driven decisions possible.

Why Data Integration Matters

The Problem It Solves

Most companies have data scattered across:

  • Operational databases - PostgreSQL, MySQL, SQL Server
  • SaaS applications - Salesforce, HubSpot, Stripe, Zendesk
  • Files and spreadsheets - Excel exports, CSVs, Google Sheets
  • APIs and events - Webhooks, message queues, logs
  • Third-party data - Market data, enrichment services

Each system serves its purpose but knows nothing about the others. A customer in Salesforce has a different ID than the same customer in Stripe. Product names don’t match between inventory and e-commerce. Marketing attribution data lives in five different tools.

Data integration bridges these gaps.

What Changes With Good Integration

  • Single source of truth - One definition of customer, product, revenue
  • Self-service analytics - Analysts query unified data instead of gathering from systems
  • Faster decisions - Questions answered in hours, not weeks
  • Trusted numbers - Executives see consistent metrics across reports
  • Automation - Systems can share data without manual intervention

Integration Methods

ETL (Extract, Transform, Load)

The traditional approach:

  1. Extract data from source systems
  2. Transform it to match the target structure
  3. Load into the destination (usually a data warehouse)

When to use: When you need to reshape data significantly before loading, or when transformation logic is complex.

Drawbacks: Transformation happens before loading, making it harder to debug and iterate.

ELT (Extract, Load, Transform)

The modern approach:

  1. Extract data from source systems
  2. Load raw data into the warehouse
  3. Transform inside the warehouse

When to use: Most modern analytics use cases. Cloud warehouses make in-warehouse transformation cheap and fast.

Benefits: Raw data is preserved, transformations are versioned and testable, iteration is faster.

Change Data Capture (CDC)

Tracks changes in source databases and replicates them:

  • Captures inserts, updates, deletes as they happen
  • Near real-time data synchronization
  • Lower load on source systems than full extraction

When to use: When you need near-real-time data, or when source databases are large and full extraction is slow.

Tools: Debezium, Fivetran CDC, AWS DMS, Airbyte CDC

API Integration

Pulls data from web APIs:

  • REST APIs, GraphQL, webhooks
  • Common for SaaS applications
  • Often rate-limited and paginated

When to use: When integrating with SaaS tools that don’t expose database access.

Challenges: Rate limits, API changes, authentication management, pagination handling.

Streaming Integration

Processes data continuously as it arrives:

  • Event-driven architectures
  • Real-time dashboards and alerting
  • Sub-second latency requirements

When to use: When business requirements genuinely need real-time data. Most companies don’t - batch is simpler and cheaper.

Tools: Kafka, Kinesis, Pub/Sub, Flink

Integration Patterns

Data Warehouse Pattern

Centralize everything in one warehouse:

Sources → Ingestion → Warehouse → Transformation → Consumption
  • All data lands in Snowflake, BigQuery, or similar
  • Transformations happen in SQL (often dbt)
  • Single query engine for all analytics

Best for: Analytics-focused organizations, modern data stack adopters.

Data Lake Pattern

Store raw data in object storage, process on demand:

Sources → Object Storage → Processing Engine → Consumption
  • Data lands in S3, GCS, or Azure Blob
  • Spark, Presto, or similar engines process data
  • More flexibility for unstructured data

Best for: Data science workloads, large unstructured datasets, cost-sensitive high-volume scenarios.

Hub and Spoke Pattern

Central integration layer connecting sources to multiple destinations:

           ┌─→ Warehouse
Sources → Hub ─→ Operational DB
           └─→ ML Platform
  • One integration platform routes to multiple consumers
  • Decouples sources from destinations
  • Easier to add new destinations

Best for: Organizations with diverse consumption patterns.

Point-to-Point Pattern

Direct connections between systems:

Source A ─→ Destination A
Source B ─→ Destination B
  • Simple for single integrations
  • Becomes unmaintainable at scale
  • Often how integration starts, rarely how it should stay

Best for: Quick wins, proof of concepts. Migrate to centralized patterns as you scale.

Integration Challenges

Schema Evolution

Sources change without warning:

  • New columns appear
  • Column types change
  • Fields get renamed or removed

Solution: Schema drift detection, alerting, and handling strategies. Tools like Fivetran handle this automatically; custom integrations need explicit handling.

Data Quality

Integration surfaces data quality problems:

  • Null values where data should exist
  • Duplicates from multiple systems
  • Inconsistent formats (dates, currencies, identifiers)

Solution: Quality checks at ingestion, monitoring, and clear ownership of source data quality.

Identity Resolution

The same entity has different identifiers across systems:

  • Customer email in CRM, customer ID in billing, anonymous ID in analytics
  • Product SKU vs. product name vs. internal ID

Solution: Master data management, matching algorithms, or maintaining mapping tables.

Historical Data

Integration often starts with “current state” but analytics needs history:

  • What was the customer status last month?
  • When did this product price change?

Solution: Slowly changing dimensions (SCD), snapshot tables, or event sourcing patterns.

Performance and Cost

Integration at scale is expensive:

  • API rate limits slow extraction
  • Full table scans stress source databases
  • Storage costs grow with data volume

Solution: Incremental extraction, CDC instead of full loads, tiered storage, archival policies.

Build vs Buy

Buy (Managed Tools)

Tools: Fivetran, Airbyte, Stitch, Hevo

Pros:

  • Fast to implement (days, not months)
  • Handles schema changes, retries, monitoring
  • Connectors maintained by vendor
  • Predictable pricing

Cons:

  • Less control over extraction logic
  • Can get expensive at high volume
  • Some sources not supported

When to choose: Standard SaaS sources, need to move fast, don’t have dedicated integration engineers.

Build (Custom Pipelines)

Tools: Airflow + custom code, Dagster, Prefect, Meltano

Pros:

  • Full control over logic
  • Can optimize for specific needs
  • No per-row or per-connector fees

Cons:

  • Significant engineering investment
  • Maintenance burden for API changes
  • Need to build monitoring, alerting, retry logic

When to choose: Custom sources, complex transformation requirements, high volume where managed tool costs don’t make sense.

Hybrid Approach

Most mature organizations use both:

  • Managed tools for standard SaaS connectors
  • Custom pipelines for proprietary databases or complex logic

Integration and Data Architecture

Data integration is a component of your broader data architecture. It’s the “how data moves” part of the blueprint.

Good integration supports:

Poor integration undermines everything downstream. If the foundation is shaky, nothing built on top will be reliable.

Getting Started

1. Inventory Your Sources

List every system that has data you need:

  • What data does it contain?
  • How do you access it? (database, API, export)
  • How often does it change?
  • Who owns it?

2. Define Your Destination

Where will integrated data live?

  • Cloud warehouse (Snowflake, BigQuery) for most analytics use cases
  • Data lake for unstructured or high-volume scenarios
  • Operational database for real-time application needs

3. Start With High-Value Sources

Don’t integrate everything at once. Pick 3-5 sources that:

  • Drive the most business decisions
  • Are requested most often
  • Have the clearest ownership

4. Choose Your Tools

For most companies starting out:

  • Managed ingestion (Fivetran, Airbyte) for SaaS sources
  • dbt for transformation
  • Cloud warehouse (Snowflake, BigQuery) for storage

5. Build Quality In

From day one:

  • Monitor freshness (did data arrive on time?)
  • Check row counts (did we get what we expected?)
  • Validate schemas (did structure change?)

Frequently Asked Questions

What is data integration?
Data integration is the process of combining data from different sources into a unified, consistent view. It connects disparate systems like CRMs, databases, and SaaS tools so that teams can work with a complete picture rather than fragmented data.
What is the difference between ETL and ELT?
ETL (Extract, Transform, Load) transforms data before loading it into the destination. ELT (Extract, Load, Transform) loads raw data first, then transforms it in the destination. ELT is the modern standard because cloud warehouses make in-warehouse transformation cheap and fast.
What is change data capture (CDC)?
Change data capture tracks changes in source databases (inserts, updates, deletes) and replicates them to the destination. It provides near-real-time synchronization with lower source system load than full extraction.
Should I build or buy data integration tools?
Most companies should buy managed tools like Fivetran or Airbyte for standard SaaS connectors - they’re faster to implement and handle maintenance. Build custom pipelines only for proprietary sources, complex logic, or when managed tool costs don’t scale.
What are common data integration challenges?
Common challenges include schema evolution (sources changing without warning), data quality issues surfaced during integration, identity resolution (matching entities across systems), and managing historical data. Good integration practices address these proactively.