Top 10 dbt Alternatives and Competitors for Modern Data Teams

Why consider an alternative to dbt?

dbt (data build tool) has become a household name in modern data engineering. With its open-source roots and paid dbt Cloud offering, many teams adopt it as their first step into SQL-based data transformation. But as data programs grow, teams often begin searching for dbt alternatives.

Why consider alternatives to dbt?

• Steep learning curve and technical overhead – dbt requires deep YAML expertise, strict process discipline, and a strong engineering culture to avoid chaos.
• Governance challenges – consistency, lineage, and auditing are difficult without senior engineers enforcing standards.
• Scaling limitations – “model explosion” and compute inefficiency can quickly turn a lightweight start into tech debt.
• Collaboration hurdles – dbt often rewards the more experienced engineer, leaving mixed-skill teams struggling with onboarding, productivity, and alignment.
• Pricing uncertainty with dbt Cloud – While dbt Core is free, many teams quickly find they need dbt Cloud for features like scheduling, collaboration, and governance. However, dbt Cloud pricing has shifted multiple times in recent years, with evolving tiers and usage-based mechanisms that make long-term costs unpredictable. For growing data teams, this can create budgeting challenges and unexpected cost escalations.

If you’ve found yourself searching for alternatives to dbt, chances are you’re feeling some of these pains. The good news: there’s a growing ecosystem of tools that can deliver faster and more predictable outcomes for your data team.

Below, we profile 10 viable alternatives to dbt—from enterprise-grade platforms to emerging tools—so you can make an informed choice.

1. Coalesce Build fast, with guardrails that scale

Coalesce is rapidly emerging as the top dbt alternative, built specifically for data transformation at scale. Unlike dbt’s blank code editor, Coalesce provides a visual and modular development environment that supports both code-first SQL users and GUI-driven workflows.

Book a Demo

Key Features

• Column-level lineage and impact analysis for full visibility into changes
• Declarative extensibility to codify architectural standards
• Visual, metadata-driven development with reusable templates
• Git integration, SQL validation testing, and enterprise-grade version control
• Multi-platform support across Snowflake, Databricks, Google BigQuery, Amazon Redshift, and Microsoft Fabric
• Built-in scheduler to schedule data pipeline refreshes directly in the Coalesce app

Pros

• Fast onboarding (teams productive in minutes, not weeks)
• Predictable project quality with built-in governance
• Empowers entire team—both senior engineers and less technical SQL users

Cons

• Fewer native application integrations available than some other tools available in the ETL ecosystem
• Because Coalesce is a relatively new player in the ecosystem, its community isn’t as mature as an open source ecosystem like dbt. Expect that to change with its rapid adoption and expanding platform support
• Most of its advanced capabilities are currently available to teams invested in Snowflake and modern cloud platforms (though expanding rapidly). As Coalesce support for Databricks, Fabric, Redshift, and BigQuery becomes more mature, expect deeper features and custom capabilities to emerge in the form of platform-specific nodes, marketplace packages, and more

Best For
Coalesce is built for data teams who want to develop at scale quickly, but with the right guardrails in place. Where dbt often rewards speed at the expense of governance—the classic “move fast and break things” mindset—Coalesce flips that equation by enabling teams to move just as fast, but with consistency, predictability, and enterprise-grade safeguards.

Its visual framework, column-level lineage, and standardized templates mean that new engineers can onboard in minutes, while experienced developers can still leverage their SQL expertise to customize and extend workflows. Instead of reinventing the wheel with every new model or relying on tribal knowledge, teams can deliver transformations faster, with fewer errors, and with confidence that every project aligns to organizational standards. In short: Coalesce is a dbt alternative that helps you move fast and build things the right way, at scale.

2. Matillion Low-code ELT with broad connectors, but watch costs at scale

Matillion is a cloud-native ETL and data transformation platform that integrates with major cloud data warehouses like Snowflake, Redshift, BigQuery, and Databricks. It features a user-friendly, visual, low-code interface for building data pipelines, making data integration more accessible for tasks like analytics, business intelligence, and AI/ML.

Key Features

• Extensive library of pre-built connectors for ingestion and transformation.
• Low-code, drag-and-drop interface for designing pipelines.
• Built-in orchestration and scheduling of workflows.
• Cloud-native scaling with usage-based pricing.

Pros

• Easy to use for analysts and engineers alike.
• Mature ecosystem of connectors reduces manual coding.
• Flexible deployment options across multiple cloud platforms.

Cons

• Can become costly at scale with high-volume workloads.
• Less fine-grained governance and metadata visibility compared to Coalesce.
• Versioning support is less smooth than code-centric tools, leading to difficulties managing changes or collaborating across branches.
• Administrative APIs or metadata retrieval APIs limited, which hampers automation or deeper integration with existing tooling.

Best For
Organizations that need a dbt alternative with a balance of ETL and transformation in one tool, particularly those that value a low-code interface for cross-functional teams.

3. Informatica Enterprise powerhouse with legacy weight and acquisition uncertainty

Informatica has long been a leader in enterprise data integration, offering one of the most comprehensive and feature-rich data management platforms in the market. Its portfolio spans ETL/ELT, data governance, master data management, and advanced data quality services, with deployment options across on-premises, hybrid, and cloud environments.

Key Features

• Wide-ranging support for ETL, ELT, and real-time data integration.
• Data governance, master data management, and data quality services.
• AI-driven recommendations for pipeline design and optimization.
• Deployment options across on-premises, hybrid, and cloud.

Pros

• Rich enterprise governance and compliance capabilities.
• Trusted vendor with long track record in large-scale data programs.
• Suitable for mission-critical workloads with high security requirements.

Cons

• Licensing costs are high compared to cloud-native competitors.
• Complexity and overhead may slow down smaller, agile teams.
• Often seen as a legacy, heavy-weight solution compared to cloud-native competitors. Its recent acquisition by Salesforce raises questions about long-term direction, roadmap alignment, and whether customers will face tighter bundling or shifts in pricing and support models.

Best For
Global enterprises that desire a dbt alternative with complex data estates requiring strict governance, compliance, and hybrid deployment flexibility.

Choosing the Right Data Transformation Tool Not sure which platform best fits your data strategy? Explore our guide to choosing the right data transformation tool. Read The Guide

4. Talend (Qlik) Data quality and governance strength, bridging legacy and cloud

Talend, now part of Qlik, is known for its integration, data quality, and governance features, providing both open-source roots and robust enterprise offerings. The platform spans data ingestion, transformation, and stewardship, with a strong emphasis on ensuring trusted and validated data flows. Talend’s hybrid deployment options allow organizations to operate across on-premises and cloud environments, making it a versatile choice for companies in transition.

Key Features

• Data quality and data stewardship modules.
• Hybrid deployment (on-prem and cloud).
• API integration and application-to-application connectivity.
• Seamless integration with Qlik analytics ecosystem.

Pros

• Strong governance and data validation capabilities.
• Flexible deployment models for hybrid environments.
• Active community around its open source tools.

Cons

• UI and workflows feel more legacy compared to modern cloud-native platforms.
• Transformation features are less optimized for cloud data warehouses.
• While it benefits from an active open-source community and a broad set of data management capabilities, its legacy workflows and recent integration into the Qlik ecosystem raise questions about future investment priorities and pace of innovation compared to cloud-native competitors.

Best For
Companies seeking an alternative to dbt that balance legacy data platforms with a gradual cloud migration who need strong governance and validation baked in.

5. Azure Data Factory Microsoft-native orchestration for cloud and hybrid pipelines

Azure Data Factory (ADF) is Microsoft’s fully managed ETL and data orchestration service, deeply integrated with the Azure ecosystem. It enables teams to design, schedule, and monitor complex data pipelines through a visual, low-code interface while still supporting code-first customization for advanced scenarios. With native integrations into services like Azure Synapse Analytics, Databricks, and Power BI, ADF acts as a central hub for moving, transforming, and orchestrating data across cloud and hybrid environments.

Key Features

• Visual authoring for pipeline creation with drag-and-drop activities.
• Deep integrations with Azure services (Synapse, Databricks, Power BI).
• Parameterized pipelines and reusable components for enterprise workflows.
• Supports hybrid ingestion and real-time streaming.

Pros

• Strong fit for Azure-first organizations.
• Powerful orchestration and scheduling at enterprise scale.
• Flexible pipeline customization with both low-code and code-first options.

Cons

• Steeper learning curve than some low-code competitors.
• Less intuitive for non-Microsoft environments.
• Troubleshooting can be cumbersome, with less real-time visibility compared to some modern observability-first tools.
• While ADF is powerful, larger workflows can become unwieldy and require heavy configuration.
• Pay-as-you-go pricing tied to pipeline activity can be difficult to forecast, especially as workloads scale.

Best For
Enterprises already committed to Microsoft Azure looking for a dbt alternative that includes a unified orchestration and transformation solution.

6. Alteryx Self-service analytics for business users, less cloud-native for scale

Alteryx is a popular data preparation and analytics platform designed with analysts and business users in mind. Its drag-and-drop workflow builder makes it easy to connect to spreadsheets, databases, and cloud applications, and then clean, blend, and enrich data without writing codents.

Key Features

• Intuitive drag-and-drop interface for workflow design.
• Wide variety of connectors for BI, spreadsheets, and data sources.
• Built-in data profiling, cleansing, and enrichment.
• Advanced analytics and machine learning modules.

Pros

• Accessible for non-technical users with little SQL knowledge.
• Rich feature set for data wrangling and lightweight analytics.
• Strong community with a large user base.

Cons

• High licensing costs, especially at enterprise scale.
• Limited scalability for complex cloud-native transformation workloads.
• Originally built for desktop workflows, Alteryx struggles with heavy, distributed data transformation in modern cloud warehouses.
• Designed primarily for individual analysts, Alteryx makes version control, team-based development, and governance harder compared to modern platforms.

Best For
Business analysts and data prep teams who want to self-serve analytics without relying heavily on engineers.

7. SQLMesh (Fivetran) A structured, predictable take on SQL transformations

SQLMesh, recently acquired by Fivetran, positions itself as a more scalable and predictable alternative to dbt for SQL-based transformations. It takes a declarative approach to building and managing SQL models, with built-in dependency tracking, testing, and environment management to reduce the “model sprawl” challenges common in dbt projects.

Key Features

• Declarative approach to transformations, similar to dbt but with stricter consistency.
• Built-in testing and validation for SQL models.
• Emphasis on minimizing “model explosion.”
• Native integration with Fivetran’s ingestion pipelines.

Pros

• Reduces some of dbt’s project sprawl issues.
• Tighter integration with ingestion and orchestration workflows.
• Open-source foundation for extensibility.

Cons

• Ecosystem and community still maturing compared to dbt.
• Adoption is still early-stage, so fewer large-scale reference architectures are available.
• Acquisition brings benefits, but also ties SQLMesh’s evolution to Fivetran’s priorities, which may limit independence.

Best For
Data teams already invested in Fivetran’s ingestion platform who want a transformation layer that feels lighter-weight than dbt but still enforces more structure and predictability. It’s a good dbt alternative for mid-sized data teams that have outgrown dbt’s sprawl but don’t yet need the governance and visual framework of an enterprise-grade solution like Coalesce.

8. Hevo Data No-code ELT for real-time ingestion and quick pipelines

Hevo is a fully-managed ELT (Extract, Load, Transform) platform designed to simplify data integration and accelerate time-to-insight. Positioned as a no-code/low-code solution, Hevo enables teams to connect to over 150 data sources—including SaaS applications, databases, and cloud services—and load data directly into cloud data warehouses like Snowflake, BigQuery, and Redshift.

With real-time streaming capabilities, Hevo ensures data freshness while requiring little engineering effort to manage pipelines. While its transformation layer is not as robust or governance-driven as dedicated platforms like Coalesce, it provides lightweight transformation features suitable for many operational analytics use cases.

Key Features

• 150+ pre-built connectors for SaaS apps, databases, and APIs.
• Real-time streaming ingestion to keep warehouse data up to date.
• No-code/low-code pipeline design with scheduling and monitoring built-in.
• Basic in-warehouse transformation capabilities for simple modeling.
• Fully-managed platform with minimal setup or maintenance required.

Pros

• Easy to use — enables analysts and non-engineers to set up pipelines quickly.
• Strong for ingestion and real-time ELT workloads.
• Fully managed with little operational overhead.
• Transparent pricing with predictable subscription tiers.

Cons

• Transformation features are relatively lightweight compared to dbt or Coalesce.
• Limited governance and standardization controls for large teams.
• May not scale well for highly complex data modeling needs.
• Stronger in ingestion than in transformation, so may require pairing with another tool.

Best For
Small to mid-sized teams seeking a dbt alternative and want to stand up reliable pipelines quickly without heavy engineering effort. It’s especially appealing for companies prioritizing real-time data ingestion and operational analytics and for those looking for a simple, managed alternative to stitching together multiple open-source ELT tools. However, teams with complex modeling, governance, or enterprise-scale needs will likely need to complement Hevo with a dedicated transformation platform like Coalesce.

Buy vs. Build: How to Approach Data Transformation Curious whether you should develop your own transformation framework or invest in a dedicated platform? Read our guide on buy vs. building a data transformation tool. Read The Guide

9. Datacoves Guardrails and governance to make dbt more manageable

Datacoves is a governance and productivity platform built specifically to enhance dbt projects, adding operational guardrails and collaboration tools. Instead of replacing dbt, Datacoves sits on top of it, providing a structured environment for development, deployment, and monitoring. It helps teams streamline CI/CD pipelines, enforce standards, and reduce some of the complexity and inconsistency that can emerge in large-scale dbt projects.

Key Features

• Pre-built CI/CD pipelines for dbt projects.
• Governance frameworks to reduce tech debt.
• Project templates and standardization for onboarding.
• Enhanced collaboration features for distributed teams.

Pros

• Improves dbt’s governance and maintainability.
• Makes scaling dbt projects more feasible.
• Useful for teams already committed to dbt.

Cons

• Datacoves cannot function without dbt, so teams still inherit dbt’s core limitations (YAML-heavy configs, lack of column-level lineage, etc.).
• Requires existing dbt expertise to be effective.
• While it improves dbt, it introduces additional licensing costs without fully addressing root pain points.
• As a niche enhancer to dbt, community support and resources are more limited than dbt’s own ecosystem.

Best For
Organizations heavily invested in dbt that want stronger governance and productivity without abandoning dbt entirely. It’s a solid fit for teams at mid-to-large companies where dbt projects have grown unwieldy and need structure, but where leadership isn’t yet ready to switch platforms. For teams already comfortable with dbt but seeking safer scaling and improved collaboration, Datacoves can provide valuable guardrails.

10. Other Emerging Tools Composable building blocks for modular data stack

The data ecosystem evolves quickly, and several tools complement or partially compete with dbt rather than directly replacing it. These solutions often focus on specific parts of the data pipeline — such as ingestion, orchestration, or lightweight transformations — and are frequently used alongside dbt or other transformation platforms in a modular, best-of-breed stack.

Airbyte

• Open-source ELT tool with a rapidly growing connector library, making it popular for teams that need to move data from many disparate sources.
• Strong appeal for engineering teams who want the flexibility to build and customize their own connectors.
• While it handles ingestion and some transformations, it doesn’t provide the governance or modeling depth needed to replace dbt.

Prefect

• Workflow orchestration platform that manages scheduling, execution, and monitoring of data pipelines.
• Designed to pair well with transformation engines like dbt, Coalesce, or SQLMesh, ensuring pipelines run reliably at scale.
• Strong observability and automation capabilities but does not provide modeling or transformation logic itself.

Pros of this modular approach

• Enables best-of-breed architectures, letting teams choose the strongest tool for each layer of the pipeline.
• Provides flexibility and independence from being locked into one vendor’s ecosystem.
• Strong open-source communities (especially around Airbyte and Prefect) can accelerate innovation.

Cons of this modular approach

• Requires more integration work and operational overhead to stitch tools together.
• Governance, lineage, and consistency can become fragmented across multiple platforms.
• Costs and complexity may scale quickly when maintaining separate tools for ingestion, transformation, and orchestration.

Best For
These tools are best for teams looking to build modular, composable stacks where transformation, orchestration, and ingestion are handled by different best-of-breed solutions. They appeal to engineering-driven organizations that value flexibility and control, and to companies that want to avoid overreliance on a single vendor. However, teams seeking unified governance and visibility may prefer an all-in-one transformation platform like Coalesce that reduces the need for stitching together multiple tools.

Choosing the Right dbt Alternative

Searching for dbt alternatives means you’ve likely outgrown its limitations—or want to avoid them altogether. The best alternative depends on your team’s skills, governance needs, and cloud platform of choice.

• If you’re an enterprise seeking speed and flexibility with governance, Coalesce is the clear leader.
• If you’re in the Microsoft ecosystem, Azure Data Factory may be a fit.
• For business analysts, Alteryx provides an approachable option.
• And for those who want to stick with dbt but improve governance, Datacoves is worth a look.

At the end of the day, your executives don’t care about the tool’s logo—they care about results. The right choice is the one that enables your team to deliver faster, safer, and at scale.