By the end of 2025, it’s estimated that we’ll generate over 182 zettabytes of data. But data alone isn’t the advantage — it’s what organizations do with it that drives innovation. Raw data must be collected, integrated, transformed, and activated before it can fuel decision-making, automation, and AI.
As data complexity and volume grow, businesses face a choice: How should data move across systems? How should it be prepared for analytics, and now, for AI? That’s where two essential systems come into play: integration platform as a service (iPaaS) and extract, load, transform (ELT.)
These aren’t competing tools — they solve different challenges. But as the demand for real-time, AI-powered applications increases, they’re starting to overlap.
This article breaks down the differences, use cases, and what modern organizations need to consider in the era of agentic AI.
What Is iPaaS?
iPaaS is a cloud-native platform that helps organizations integrate applications, automate workflows, sync data, and manage APIs across cloud and on-prem environments.
Originally developed to handle application-to-application workflows, iPaaS has evolved into a powerful suite that enables real-time data movement, event streaming, and low-code connectivity.
Common iPaaS use cases:
- Connecting SaaS applications like Salesforce, NetSuite, and ServiceNow
- Triggering workflows across systems (e.g., if a lead is created in HubSpot, create a task in Asana)
- Integrating hybrid cloud and on-premises infrastructure
Solutions like the Boomi Enterprise Platform are leading the way in enabling fast, reliable integration across complex IT ecosystems.
What Is ELT and How Does It Work?
ELT stands for Extract, Load, Transform — a modern approach to data integration where data is extracted from sources, loaded into a cloud data warehouse like Snowflake or Databricks, and then transformed using push-down SQL or Python.
ELT is ideal for:
- Moving large volumes of data (structured and unstructured)
- Centralizing disparate data sources
- Powering analytics, dashboards, and machine learning models
Platforms like Boomi Data Integration (formerly Rivery) automate the ELT process end-to-end. Instead of hand-coding connectors and pipelines, users simply authenticate sources, choose the data they want, and load it into a warehouse with minimal code.
For example, a marketing team wants to understand ROI across ad platforms. ELT can:
- Extract data from Meta, Google Ads, and LinkedIn
- Load it into Snowflake
- Transform and normalize campaign names, formats, and spend data
- Feed ROI data into Tableau dashboards or drive AI models that predict performance
iPaaS vs. ELT: Which One Do You Need?
Use this quick guide to understand where each shines:
Use iPaaS when you need to:
- Integrate applications
- Automate cross-system workflows
- Sync operational data in real time
- Manage APIs
- Connect cloud and on-prem environments
Use ELT when you need to:
- Centralize and model large datasets
- Enable data analysts and scientists
- Prepare data for AI and reporting
- Ingest raw data and transform in the warehouse
- Power analytics in Snowflake, Databricks, or BigQuery
You likely need both if:
- You want real-time operational data to feed into your warehouse
- You’re building agentic AI systems that act on insights
- You’re syncing insights back into business apps (reverse ETL)
Who Uses iPaaS and ELT?
iPaaS Users
| Title | Use Case |
| CIO / Head of IT | Simplify complex environments and reduce integration time |
| Head of Integration / CDO | Connect dozens of apps across teams |
| Enterprise Architect | Design scalable, future-proof system interactions |
| CISO | Ensure secure, compliant data exchange between systems |
ELT Users
| Title | Use Case |
| Chief Data Officer (CDO) | Make data a strategic asset across teams |
| Head of Data | Centralize data architecture and infrastructure |
| Data Engineer | Build pipelines with SQL/Python, manage transformations |
| Data Analyst | Need fast access to clean, queryable data |
Why You Probably Need Both iPaaS and ELT (Especially in the Age of Agentic AI)
AI isn’t just changing what we do with data — it’s changing how and who does it.
Real-time AI systems, or “agentic AI,” need:
- Live event data from different apps (via iPaaS)
- Structured historical data for training and insights (via ELT)
- Feedback loops into operational systems (via reverse ETL or APIs)
Example: A predictive AI agent for eCommerce
- iPaaS integrates with Shopify, Salesforce, and shipping APIs to fetch live order status
- ELT loads clickstream data into Databricks to train purchase intent models
- Reverse ETL pushes product recommendations back into Klaviyo or Zendesk
This convergence means integration and data teams must align. And platforms must support both app orchestration and data pipeline execution in one place.
Benefits & Tradeoffs of iPaaS and ELT
| Capability | iPaaS | ELT |
| Application integration | ✅ | ❌ |
| CDC data pipelines | ❌ | ✅ |
| Real-time data movement | ✅ | Near-real-time |
| Low-code usability | ✅ | ✅ |
| Transformations in SQL/Python | ❌ | ✅ |
| Reverse ETL support | ✅ | ✅ |
| API management | ✅ | ❌ |
| AI-readiness | ✅ (for streaming) | ✅ (for modeling) |
Quick Diagnostic: Do You Need iPaaS, ELT, or Both?
Ask yourself:
- Do I need to connect business applications and automate workflows? → iPaaS
- Do I need to centralize data for reporting, ML, or AI? → ELT
- Do I need to push insights back into systems? → Both
- Am I building agentic AI or real-time intelligence? → Both
If you’re leaning “both” more than once, you’re not alone. That’s the future.
Why Boomi Offers the Best of Both Worlds
Choosing between iPaaS and ELT used to mean juggling vendors, tools, and architectures. With Boomi’s AI-driven automation platform, businesses can unify these functions under one platform. The result:
- Low-code application integration
- Powerful ELT and reverse ETL pipelines
- Real-time and batch data handling
- Built-in support for Snowflake, Databricks, and more
In the age of AI, this matters more than ever. You need data that moves across systems, lands in the right place, gets modeled properly, and drives intelligent action — all seamlessly. With Boomi, your integration team and your data team can finally operate as one.
Learn more about how Boomi can help your organization with advanced data integration.