Data teams have a structural challenge: how to integrate many different data sources without using up too much engineering time. None of the general-purpose ETL tools I looked at solve this problem for you because either they require a business user to know how to write code or they require a data engineer to write custom code to build data pipelines with their tool’s proprietary connectors.
There is a huge gap between the ease of use a data analyst expects from a no-code tool and the flexibility a data engineer requires to build custom data pipelines. Most no-code tools sacrifice one feature, like real-time data sync, security compliance, or a large number of available connectors, to deliver on another.
We compare no-code and managed ETL platforms that balance simplicity for business users with enterprise-grade features for complex data pipelines. The four companies we reviewed were scored on five factors: the ability to run no-code or low-code workflows, availability of over 200 pre-built connectors, availability of real-time or near-real-time data sync, availability of enterprise-grade compliance and security certifications, and availability of flexible pricing plans.
How to choose the right ETL tools
Depending on the size and experience level of your data engineering team, and on the complexity of your use cases, the right ETL platform for you will vary.
Choose an ETL tool that works well with the data sources you need to integrate.
- No-code or low-code interface — Determine if business users can construct workflows without using SQL or Python to minimize the need for developers in the case of standard integrations.
- Pre-built connector library — Seek out solutions with 200+ native connectors for your existing tech stack (CRMs, databases, warehouses, SaaS apps, etc) so you don’t have to build APIs yourself.
- Real-time sync capability — Are you looking for sub-second latency? Do scheduled batch loads work for you? Real-time systems cost more, but they allow for more immediate, operational analytics.
- Enterprise compliance certifications — Check for SOC 2, GDPR, HIPAA, or ISO 27001 compliance if you’re working with regulated data; request audit reports, not just sales pitches.
- Pricing model flexibility — Consider pricing models per row, per connector, or a flat rate. Compare the price per row to your expected data volume, and take advantage of free tiers to test out the service before committing to paying.
- Transformation capabilities — Ensure that the tool allows for both ELT (transform in warehouse) and ETL (transform in-flight), depending on where you do analytics transformations.
Top 4 ETL tools
We selected these tools based on their no-code simplicity, 200+ built-in integrations, real-time syncing, and enterprise-level security. We chose these platforms because they offer a competitive price point that fits both startup use cases as well as large-scale data pipelines used by Fortune 500 companies.
Each solution handles different types of integrations. Some provide two-way synchronization, while others handle more complex ETL or ELT processes.
1. Skyvia
Skyvia has been around since 2014. Its 200+ connectors cover SaaS apps, databases, and cloud warehouses, and the same account can be used for fairly different jobs: loading data into a warehouse, keeping two systems in sync, moving data during a migration, or sending modeled warehouse data back into an operational app.
You can build most of that visually. Mapping fields, filtering rows, changing types, and setting up recurring jobs do not require custom code. For transformations that belong in the warehouse, Skyvia can work with native SQL or hosted dbt Core. Schema changes, execution logs, and alerts are handled in the platform as well, which cuts down on the amount of routine pipeline maintenance.
The pricing is based on data volume rather than seats or connector count. Adding another user or integration does not create a separate fee, and there is a free tier that does not require a credit card. Skyvia has more than 2,000 paying customers in 120+ countries, including Hyundai, Panasonic, GE, and Médecins Sans Frontières, and moves more than 10 billion records per month.
| Founded | 2014 |
| Best For | Teams handling several integration jobs without building each one from scratch |
| Pricing | Volume-based, unlimited users, no per-connector fees |
| Connectors | 200+ |
2. Stacksync
Stacksync provides real-time, bidirectional data synchronization between CRMs, ERPs, databases, warehouses, and 1,000+ business apps.
Founded in 2023, Stacksync was created to replace legacy stacks stitched together from multiple tools like MuleSoft, Fivetran, Kafka, and Zapier. Traditional ETL tools only batch data hourly or daily; Stacksync syncs data in both directions with sub-second latency.
All Stacksync platforms run on infrastructure that is SOC 2, HIPAA, GDPR, ISO 27001, and CCPA compliant. The platform can sync data, automate workflows, process event queues, and handle EDI.
Stacksync’s AI Agents allow you to build complex workflows across multiple systems. They are currently actively shipping content with an 11-50 person team.
- Starter: $1,000 per month – Pro: $3,000 per month – Managed Pro: $4,200 per month – Enterprise: Custom pricing;
- 4.7/5 on G2;
- Design your solution for scale with dedicated custom services;
- No free trial — demo-first sales model.
3. Rivery
Rivery provides a fully-managed ELT solution created by data engineers focused on giving business users an intuitive front end without sacrificing the complex functionality required for enterprise-grade pipelines. With more than 200 connectors and no user or connection caps, Rivery also includes built-in Python execution inside data flows, which is uncommon and allows data analysts to easily add their own logic to transformations. Reverse ETL and Change Data Capture capabilities enable real-time, bi-directional data syncing between warehouses and SaaS applications.
It has a free trial, with flexible pricing from $0.90/BDU credit depending on the volume and complexity of your data transformations; no license or seat fees apply. Rivery received a 5.0 rating on Capterra from data engineering teams who appreciate its automatic and end-to-end pipelines that provide clean and controlled data to dashboards, analytics platforms, machine learning, and AI agents.
There are 4 tiers of service: Base, Professional, Pro Plus, and Enterprise. This enables smaller teams to quickly start prototyping data flows and larger organizations to scale to hundreds of data sources. Rivery also supports development and deployment lifecycles with version control for all transformations.
- 200+ connectors with unlimited users and connections;
- Native Python execution for custom transformations;
- Reverse ETL and CDC for bidirectional sync;
- Free trial with transparent usage-based pricing;
- 5.0 Capterra rating from data engineering teams.
4. Zapier
Zapier brings 15 years of integration expertise to AI orchestration. It connects 9,000+ apps via no-code workflows, enabling non-technical business users to automate processes without waiting on developers. Users have a single place to establish guardrails, control model access, and visualize everything, empowering all team members to safely build with AI across models without seeking explicit approval. With MCP (Model Context Protocol) support and AI agent orchestration, Zapier sits between traditional application automation and the agentic AI era.
While it can orchestrate ETL workflows, it is better suited for triggering actions in other applications than building out complex data pipelines. Trusted by NetSuite, Salesforce, HubSpot, and Slack, Zapier is ideal if your priority is wide-ranging app integrations rather than sophisticated data transformations.
Its free tier is perfect for getting started. Professional starts at $19.99/month and Team at $69/month. Enterprise pricing is available upon request.
It meets SOC 2, GDPR, and CCPA compliance standards, ensuring essential enterprise security requirements are met, and includes audit trails and governance capabilities for enhanced oversight.
| Founded | 2011 |
| Best For | Cross-app AI workflows and broad integration coverage |
| Pricing Tiers | Free, Professional, Team, Enterprise |
| Notable Feature | AI agent orchestration with MCP support |
How to shortcut this list
Different teams prioritize different integration scenarios — real-time sync for operational workflows, managed ELT for analytics pipelines, or broad app connectivity for business automation.
- Real-time operational sync: Stacksync;
- Managed analytics pipelines: Skyvia, Rivery;
- Business app automation: Zapier.
Conclusion
The right platform shouldn’t force business users and data teams to choose between ease-of-use and capability. Our four top-ranked platforms all feature no-code UIs, hundreds of integrations, and enterprise-level security. However, they differ in where they perform best.
Your ideal ETL tool depends on what matters most: near-real-time updates, a fully managed solution, or an extensive list of integrations. Align your primary needs with the platform’s strengths.
Begin by using a free trial or demo to see how well their connectors match your data sources. Evaluate the speed of data sync and the flexibility of transformations in your environment. All of our top picks have proof-of-concept options available. Take advantage of these to confirm the software will work for you before you commit to a full year’s subscription.
