Discover the key differences between data orchestration and ETL, and learn how modern ETL platforms like DataLark bridge the two approaches.
Data doesn’t live in one place anymore — it flows between SAP S/4HANA, Salesforce, Snowflake, and countless other systems. Keeping that flow seamless, accurate, and real-time is no small feat.
For decades, ETL (Extract, Transform, Load) powered data integration. But in today’s hybrid, cloud-driven world, ETL alone can’t handle the complexity of modern pipelines.
That’s where data orchestration steps in — not to replace ETL, but to make it smarter. It automates, monitors, and coordinates data across every layer of your ecosystem, connecting SAP and non-SAP systems into one unified flow.
In this guide, we’ll clarify the difference between data orchestration and ETL, explain how they work together, and show how platforms like DataLark help enterprises turn data movement into data intelligence.
In 2025, data architecture looks nothing like it did a decade ago. Enterprises now manage vast ecosystems of systems — SAP S/4HANA, SAP BW/4HANA, Salesforce, Snowflake, AWS, Databricks — all generating and consuming data at high velocity. The challenge is no longer just moving that data; it’s about synchronizing, governing, and activating it across platforms in real time.
For years, ETL (Extract, Transform, Load) was the workhorse behind data integration. It extracted information from systems like SAP, transformed it to fit a common schema, and loaded it into centralized repositories for analysis. And for many organizations, that approach worked — as long as data lived in a controlled, mostly on-premise world.
But today’s reality is far more dynamic. Data flows continuously between ERP systems, APIs, streaming platforms, and machine learning models. Batch-driven ETL pipelines, while still essential, often can’t keep pace with the real-time data processing demands of business operations or analytics. When an SAP inventory update needs to trigger an instant refresh in a cloud dashboard, waiting for an overnight ETL job just doesn’t cut it.
That’s why data orchestration has become the backbone of the modern data stack. Instead of focusing on transformation alone, orchestration manages how and when data processes run — across every system, environment, and tool. It ensures SAP data syncs with cloud warehouses, APIs, and analytics in perfect order, turning fragmented data tasks into cohesive, intelligent workflows.
The numbers tell a compelling story: the global data orchestration market is projected to reach $4.3 billion by 2034, growing at a compound annual growth rate (CAGR) of 12.1%.
As we move further into 2025, the distinction between data orchestration vs ETL isn’t just technical — it’s strategic. Companies that master orchestration gain the agility to adapt, automate, and innovate faster than ever before.
Before we explore how orchestration changes the game, it’s worth revisiting the foundation: ETL — short for Extract, Transform, Load.
At its core, ETL is a process designed to consolidate data from multiple systems into a single, consistent format for analysis. It’s been the bedrock of enterprise data management for decades — particularly in SAP-centered landscapes, where clean, governed data is non-negotiable.
Here’s how it works:
This approach has powered countless business intelligence systems, from SAP BW dashboards to modern self-service analytics. ETL ensures consistency, compliance, and data quality — three pillars that keep operations and decision-making stable.
However, the traditional ETL model was built for batch processing — running nightly or hourly jobs. That made sense when data moved slowly, but not in today’s real-time data processing, API-driven world.
When a sales transaction happens in SAP, a customer logs a support ticket in Zendesk, and a predictive model in Databricks must respond instantly — ETL alone can’t orchestrate those events. It still plays a vital role, but it operates as one component of a much larger data ecosystem.
That’s why understanding data orchestration vs. ETL is so important: orchestration doesn’t replace ETL — it makes it responsive, connected, and aware of the bigger picture.
If ETL is the engine that moves and shapes data, then data orchestration is the control tower that ensures every process runs at the right time, in the right order, and for the right reason.
In simple terms, data orchestration is the coordination and automation of all data workflows — ETL jobs, API syncs, analytics updates, and machine learning pipelines — across your entire data landscape. It’s about managing how data flows, not just where it goes.
Think of a global company running SAP S/4HANA for finance, Salesforce for CRM, and Snowflake for analytics. Each system has its own rhythm and rules.
ETL can extract and transform data from SAP to Snowflake, but orchestration determines:
That’s where orchestration tools like DataLark come in. They don’t just move data; they govern the flow. They monitor dependencies, manage retries, alert teams when something fails, and trigger downstream processes — all automatically.
In an SAP context, orchestration can:
The key distinction in the data orchestration vs ETL conversation is:
In other words, ETL moves data through a pipeline. Orchestration ensures the entire data supply chain runs like clockwork.
Now that we’ve defined both concepts, let’s look at where data orchestration and ETL truly diverge — and how they work together to power modern data ecosystems.
At a glance, ETL is a process for preparing and loading data. Data orchestration is a framework for managing how all those processes connect, depend on, and trigger each other.
The distinction might sound subtle, but in practice it’s transformational — especially in SAP-driven enterprises, where workflows span ERP systems, cloud platforms, and analytics environments.
Here’s a side-by-side view:
|
Aspect |
ETL (Extract, Transform, Load) |
Data Orchestration |
|
Primary Goal |
Move and transform data |
Coordinate and automate data workflows |
|
Scope |
Individual pipeline or dataset |
Entire data ecosystem (multiple pipelines, systems, and tools) |
|
Processing Mode |
Batch (scheduled jobs) |
Real-time and event-driven, with dependency control |
|
Control Logic |
Limited – defined within each ETL job |
Centralized – defines the logic, order, and conditions of many jobs |
|
Tools |
SAP Data Services, Fivetran, Talend, Informatica, DataLark |
Apache Airflow, Prefect, Dagster, DataLark |
|
Observability |
Basic logs |
Full visibility into workflow dependencies, success/failure states, and timing |
|
Flexibility |
Fixed workflows |
Dynamic and adaptive workflows |
|
Typical Use Case |
Load SAP data into Snowflake or SAP BW |
Automate end-to-end flows: SAP → ETL → Snowflake → Power BI or ML model |
An example of how this plays out in practice: a manufacturing company uses SAP S/4HANA to track production orders. ETL jobs extract and transform that data into Snowflake each night for reporting. With data orchestration, the process evolves — when an SAP order closes, DataLark triggers an immediate ETL pipeline, validates data quality, updates Snowflake in real time, and refreshes dashboards automatically. No delays, no manual scheduling, no stale insights.
That’s the true power of orchestration: it turns ETL into part of a living, responsive data ecosystem. Rather than focusing on a single data flow, orchestration sees the big picture — ensuring that SAP, APIs, cloud warehouses, and AI models stay in sync with minimal human intervention.
Yes — and they’re stronger together.
While ETL remains the foundation of any reliable data pipeline automation, orchestration provides the structure that keeps those pipelines aligned, efficient, and responsive. Most modern data teams rely on a combination of both: ETL to move and prepare data, and orchestration to coordinate and monitor the flow of those processes.
In practice, ETL still does the heavy lifting. It’s what extracts data from SAP S/4HANA, transforms it into a consistent model, and loads it into a target system such as Snowflake or SAP BW/4HANA. Without strong ETL logic, no orchestration layer can function effectively.
What’s changing today is the expectation that ETL alone should be smarter — more automated, event-aware, and integrated across systems.
Modern ETL platforms, including DataLark, now offer features that help close this gap with:
For example, a DataLark pipeline might extract data from SAP S/4HANA, transform it for analytics, and then automatically trigger a load into Snowflake once the SAP data refresh completes — all within a single coordinated process.
This level of automation doesn’t replace full-scale orchestration tools like Airflow or Prefect, but it gives teams a simpler, integrated way to run reliable, connected ETL pipelines without adding operational overhead.
So, while ETL and orchestration remain distinct disciplines, the line between them is becoming more fluid. Tools like DataLark make it easier for teams to bridge the two — keeping ETL at the core, but adding the intelligence and automation that modern data environments demand.
The next wave of data innovation isn’t about replacing ETL — it’s about making it intelligent and context-aware. As architectures grow more distributed, companies will expect ETL tools to behave less like static pipelines and more like adaptive systems that can learn, monitor, and optimize themselves.
Three big trends are already shaping this future:
DataLark is evolving along this path — focusing on automation, transparency, and adaptability within its ETL foundation. Rather than trying to replace full orchestration platforms, DataLark aims to make ETL smarter: easier to manage, quicker to respond, and more aware of its place in a larger data ecosystem.
In that sense, the future isn’t about ETL versus orchestration. It’s about building a connected data infrastructure where transformation, automation, and intelligence work together — turning the movement of data into a continuously optimized process.
To see how these concepts play out in practice, let’s look at a real-world example of how a global manufacturing company used DataLark to modernize its SAP data pipelines — achieving orchestration-level automation while keeping ETL at the core.
The company relied heavily on SAP S/4HANA to manage production, logistics, and financials. Every night, a series of ETL jobs extracted SAP data, transformed it, and loaded it into Snowflake for reporting.
However, the process had several limitations:
The data engineering team wanted a more responsive and reliable approach — one that didn’t require adopting a full orchestration platform, but could deliver similar coordination.
The team implemented DataLark as their central ETL platform, configuring pipelines to handle data extraction from SAP S/4HANA tables via OData APIs and transformation logic before loading to Snowflake.
To make the process more dynamic, they used DataLark’s automation and dependency management features to:
Within weeks, the company achieved:
Most importantly, the team didn’t need to introduce a complex orchestration platform — they achieved the right balance between automation and simplicity by extending their ETL workflows intelligently within DataLark.
The solution to the ETL vs. orchestration dilemma isn’t about replacing one technology with another, it’s about raising the level of intelligence in how data moves across an organization.
Enterprises running SAP, cloud, and AI systems side by side can’t afford disjointed data processes. They need automation, visibility, and adaptability — but they also need the reliability and structure that ETL provides.
That’s where platforms like DataLark play a critical role: empowering data teams to go beyond traditional ETL, without the complexity of full orchestration frameworks. By automating dependencies, improving monitoring, and connecting workflows across systems, DataLark helps organizations build pipelines that are faster, smarter, and more resilient — all while staying grounded in the proven principles of ETL.
The takeaway is simple: ETL remains the backbone of enterprise data. But as data environments grow more interconnected, tools that combine ETL strength with orchestration agility will define the next generation of data excellence. Request a demo of DataLark and check it out yourself.