Bottom line up front: If you have data engineers who write Python, use Airflow or Prefect. If you need business users to build workflows without IT, use Mammoth or Alteryx.
The data orchestration market has exploded. What used to be a choice between Airflow and maybe two alternatives is now 50+ tools.
Here’s what matters: most tools were built by data engineers, for data engineers. Your marketing analyst shouldn’t need to learn Python to automate a weekly report.
This guide reviews 11 tools across three categories. We’ll tell you what each tool is actually good at and when to choose it.
Tool | Best For | Starting Price | Ease of Use | Best Scale |
|---|---|---|---|---|
Mammoth | Business users | $19/user/month | Very Easy | 1M to 1B+ rows |
Alteryx | Advanced analytics | $5,000/user/year | Moderate | 100K to 100M rows |
Tableau Prep | Tableau users | $70/user/month | Easy | Under 10M rows |
Apache Airflow | Data engineers | Free (infrastructure costs) | Difficult | Unlimited |
Prefect | Modern engineering teams | Free to $199/month | Moderate | Unlimited |
Dagster | Asset-centric pipelines | Free (Cloud by usage) | Moderate | Unlimited |
Kestra | Declarative workflows | Free (Cloud available) | Moderate | Unlimited |
Azure Data Factory | Azure infrastructure | Pay per execution | Moderate | Unlimited |
AWS Step Functions | AWS serverless | Pay per transition | Moderate | Unlimited |
Databricks Workflows | Databricks users | Included with platform | Moderate | Unlimited |
Astronomer | Managed Airflow | $2,500/month | Difficult | Unlimited |
What Is Data Orchestration?
Data orchestration automates moving and transforming data across multiple systems. Think of it as a traffic controller for your data.
The key difference from basic automation is dependency management. Orchestration tools know that Step 2 can’t run until Step 1 completes.
Real example: Your finance team needs a monthly close report combining ERP, CRM, and billing data. Without orchestration, someone manually exports three CSVs, cleans them in Excel, and uploads to Power BI.
With orchestration, this runs automatically on the first of each month. Learn more about data workflow automation.
How We Evaluated These Tools
We evaluated each tool based on conversations with hundreds of teams. Here’s what we looked at:
Ease of use: Can non-technical users build workflows?
Scalability: Does it handle millions of rows?
Integrations: How many pre-built connectors exist?
Maintenance: Who fixes things when they break?
Total cost: Licensing plus training and IT support.
Best Data Orchestration Tools for Business Users
These tools prioritize ease of use over technical power. Analysts and operations teams can build workflows without involving IT.
1. Mammoth Analytics
Best for: Non-technical teams who need automation without complexity
Pricing: Starts at $19/month per user with 7-day free trial
Mammoth solves the core problem with most orchestration tools. They’re too technical for the people who actually need to move data.
While Airflow requires Python and weeks of training, Mammoth lets business users build workflows in minutes. It uses natural language and visual interfaces instead of code.
The platform handles everything: connecting to APIs and databases, exploring and cleaning data, transforming with no-code functions, and auto-scheduling refreshes. Starbucks processes over 1 billion rows monthly across 17 countries with Mammoth.
They reduced report generation from 20 days to hours. One finance director cut monthly close time from 40+ hours to minutes.
What makes it different: Most tools require you to know what transformations you need before building. Mammoth lets you investigate data first, then convert findings into automated pipelines.
Real customer results: Bacardi achieved 193% ROI in year one and eliminated 40+ hours of manual work monthly. MUFG automated global KYC across 19 countries.
Arla saves 1,200 manual hours annually processing 1B+ rows. Read the full Starbucks case study to see how they reduced report generation from 20 days to hours.
When to choose Mammoth: Your team understands business logic but doesn’t have Python skills. You’re tired of waiting for IT to fix workflows.
When to skip it: You have data engineers who prefer writing code. You need advanced ML orchestration.
2. Alteryx
Best for: Analysts who need advanced analytics capabilities
Pricing: Starts around $5,000 per user annually
Alteryx pioneered visual data workflows. The drag and drop canvas builds complex workflows without code.
What makes it different: Alteryx includes spatial analytics, predictive modeling, and statistical functions. It goes beyond basic data movement.
The tradeoffs: Teams report a steep learning curve even for analysts. One operations manager said it took three months to become proficient.
The bigger challenge is cost. At $5K+ per user, pricing spirals when expanding beyond a small team.
When to choose Alteryx: You need advanced spatial or predictive analytics. Budget isn’t a constraint.
When to skip it: You need to democratize workflows across many users. Compare Alteryx alternatives for more cost-effective options.
3. Tableau Prep
Best for: Teams already using Tableau
Pricing: Included with Tableau Creator at $70 per user per month
Tableau Prep prepares data before visualization. The visual interface shows transformations in real time.
What makes it different: Tight Tableau integration means workflows publish directly to Tableau Server. Visual profiling spots data quality issues immediately.
The limitations: Tableau Prep focuses narrowly on data preparation. It doesn’t handle complex orchestration like conditional workflows.
When to choose it: You’re a Tableau shop needing basic prep. Your workflows are straightforward cleaning.
When to skip it: You need complex orchestration logic. You want to automate beyond Tableau.
Best Data Orchestration Tools for Data Engineers
These tools assume you write code. They’re designed for data engineers who think in DAGs.
4. Apache Airflow
Best for: Data teams who need battle-tested orchestration
Pricing: Open source and free, but plan for infrastructure costs
Apache Airflow is the default for code-first orchestration. Built at Airbnb, it’s now the industry standard.
What makes it different: Python-based DAG definition means anything you can code, you can orchestrate. Hundreds of operators connect to different systems.
The reality: Multiple engineers told us Airflow takes weeks to master. One team spent $120K on consulting just for initial setup.
When the engineer who built your DAGs leaves, the next person faces a steep climb. Maintenance burden is real.
When to choose it: You have data engineers who prefer code. You need maximum flexibility.
When to skip it: Your team is primarily analysts. You want business users modifying workflows.
5. Prefect
Best for: Teams who want Airflow with better developer experience
Pricing: Free open source, Cloud starts at $199 per month
Prefect was built by former Airflow users wanting a modern approach. It maintains code-first philosophy with better error handling.
What makes it different: Hybrid execution separates workflow definition from infrastructure. You develop locally and deploy flexibly.
What engineers say: Teams report 40 to 70% reduction in code complexity versus Airflow. One ML team said dynamic workflows finally let them build adaptive pipelines.
The tradeoff: Prefect is newer than Airflow. The ecosystem is smaller.
When to choose it: You want code-first with modern developer experience. Your workflows need dynamic task generation.
When to skip it: You need the largest operator ecosystem. You have years of Airflow DAGs.
6. Dagster
Best for: Data teams building asset-centric pipelines
Pricing: Open source core, Cloud pricing by usage
Dagster takes a different approach. You think about data assets instead of tasks and DAGs.
What makes it different: Software-defined assets mean you define what to produce, Dagster figures out execution. Testing is dramatically better than Airflow.
What teams say: One team said switching from Airflow reduced incident response time by 60%. They could see what was wrong with data, not just which task failed.
The consideration: Asset-centric model is a paradigm shift. Learning curve exists if you’re used to task-based DAGs.
When to choose it: You’re building a modern data platform from scratch. Data quality is critical.
When to skip it: You have extensive Airflow infrastructure. Your team needs something simpler.
7. Kestra
Best for: Teams who want declarative workflows
Pricing: Open source, Cloud offerings available
Kestra bridges code and no-code approaches. Workflows are defined in YAML instead of Python.
What makes it different: Declarative YAML means less code to maintain. The UI helps non-technical stakeholders understand workflows.
What users say: “After looking at Airflow and Prefect, Kestra’s YAML just made sense.” Declarative config with usable UI appeals to teams wanting power without complexity.
The tradeoff: YAML is less flexible than Python for complex logic. Some teams hit limitations.
When to choose it: You want maintainable workflows. Your team values DevOps practices.
When to skip it: Your workflows require complex Python logic. You need the largest plugin ecosystem.
Best Enterprise Data Orchestration Platforms
These platforms handle massive data volumes. They offer enterprise features like robust security and multi-team deployments.
8. Azure Data Factory
Best for: Microsoft shops with Azure infrastructure
Pricing: Pay per execution model
Azure Data Factory provides deep Azure integration. It’s designed for cloud-scale data integration.
What makes it different: Tight Azure integration works seamlessly with Azure SQL, Blob Storage, and Synapse. Pay per execution can be economical at scale.
The reality: Teams report ADF works well for straightforward data movement. Anything complex becomes challenging.
One architect said: “ADF is fine for 80% of our needs. The other 20% requires workarounds.”
When to choose it: You’re heavily invested in Azure. Your workflows move data between Azure services.
When to skip it: You have multi-cloud requirements. Your workflows are complex.
9. AWS Step Functions
Best for: AWS-native serverless orchestration
Pricing: Pay per state transition
AWS Step Functions orchestrates AWS services through visual workflows. Deep integration with Lambda, S3, and DynamoDB.
What makes it different: Serverless model means zero infrastructure to manage. Everything scales automatically.
The tradeoffs: Purpose-built for AWS services. Outside AWS, you write custom Lambda functions.
When to choose it: You’re all-in on AWS. Your workflows coordinate AWS services.
When to skip it: You have multi-cloud requirements. Your workflows orchestrate many non-AWS systems.
10. Databricks Workflows
Best for: Teams using Databricks for lakehouse architecture
Pricing: Included with Databricks
Databricks Workflows provides orchestration inside the Databricks platform. Designed to orchestrate notebooks and Delta Live Tables.
What makes it different: Native integration means data and orchestration live together. Workflows understand Databricks compute.
The consideration: Best for Databricks-centric pipelines. Teams with complex workflows across many systems still use Airflow alongside it.
When to choose it: Databricks is your primary platform. Your workflows mainly orchestrate notebooks.
When to skip it: You orchestrate across many non-Databricks systems. Your architecture spans multiple platforms.
11. Astronomer (Managed Airflow)
Best for: Teams who want Airflow without operational burden
Pricing: Starts around $2,500 per month
Astronomer is managed Airflow. You get Airflow’s power without running infrastructure.
What makes it different: Value is operational excellence. They solved the hard problems of running Airflow at scale.
What customers say: “We spent 6 months trying to run Airflow ourselves before switching. Now we focus on building DAGs.”
The economics: At $2,500+ per month, Astronomer costs more than DIY Airflow. Fully-loaded cost often favors Astronomer when you include engineer time.
When to choose it: You need Airflow but lack resources to run it. You want to focus on pipelines, not infrastructure.
When to skip it: You have infrastructure expertise. Budget constraints make managed services prohibitive.
How to Choose the Right Tool
Start with these questions:
Who will build workflows? Data engineers writing Python should use Airflow, Prefect, or Dagster. Analysts comfortable with visual tools should use Alteryx or Tableau Prep.
Business users across teams should use Mammoth. It’s designed for people who understand data but don’t code.
What’s your primary use case? Basic data prep for BI should use Tableau Prep or Mammoth. Complex analytical workflows need Alteryx or Dagster.
Large-scale data movement requires Azure Data Factory or Airflow. Event-driven architectures work best with AWS Step Functions or Prefect.
What’s your scale? Under 100K rows, most tools work fine, so prioritize ease of use. Millions of rows require performance tools like Airflow or Databricks.
Billions of rows need enterprise platforms. Mammoth handles 1B+ rows, as do Azure Data Factory and Databricks.
What’s your budget? Limited budget should choose open source or Mammoth at $19 per user per month. Mid-market can afford managed services or business platforms.
Enterprise budgets support cloud platforms or managed Airflow. Remember to factor in training and maintenance costs.
How quickly do you need results? Need wins this quarter, choose tools with fast onboarding like Mammoth or Tableau Prep. Building long-term platform, invest in Airflow or Dagster with proper training.
For more guidance, read our complete guide on how to automate your data workflows.
Common Mistakes
Choosing based on what’s “modern” rather than fit. Every vendor claims to be more modern. What matters is whether your team can use it.
We’ve seen organizations implement Airflow because “it’s what data engineers use,” only to discover analysts can’t modify workflows. Choose based on who will use the tool.
Underestimating total cost of ownership. Open source Airflow is free, but running it requires infrastructure and engineer time. One team calculated DIY Airflow cost $150K annually.
For a detailed breakdown, see our analysis of data automation tools and their real costs.
Not considering maintenance burden. Tools offering maximum flexibility often require maximum maintenance. Ask yourself: what happens when the person who built these leaves?
Focusing on features instead of use cases. Just because a tool can orchestrate ML pipelines doesn’t mean you need that. Start with your actual requirements.
Making Your Decision
Map your top use cases to specific tools. Then run a two-week pilot project.
A pilot reveals more than three months of vendor demos. For most organizations, start simple and add complexity only when needed.
If you’re a business user who needs to automate workflows without Python, start a free trial with Mammoth and solve your first use case this week. No credit card required.
If you’re a data engineer building complex pipelines, try Prefect or Dagster for a modern take. Or stick with Airflow if you value ecosystem over developer experience.
If you’re enterprise with complex requirements, evaluate Azure Data Factory or Databricks depending on your cloud. Or consider Astronomer if you want Airflow without operational burden.
The best tool is the one your team will actually use successfully. Not the one with the longest feature list.
