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Not long ago, the data analytics world relied on monolithic infrastructures—tightly coupled systems that were difficult to scale, maintain, and adapt to changing needs. These legacy setups often resulted in operational bottlenecks, delayed insights, and high maintenance costs. To overcome these challenges, the industry shifted toward what was deemed the Modern Data Stack (MDS)—a suite of focused tools optimized for specific stages of the data engineering lifecycle.

This modular approach was revolutionary, allowing organizations to select best-in-class tools like Airflow for Orchestration or a managed version of Airflow from Astronomer or Amazon without the need to build custom solutions. While the MDS improved scalability, reduced complexity, and enhanced flexibility, it also reshaped the build vs. buy decision for analytics platforms. Today, instead of deciding whether to create a component from scratch, data teams face a new question: Should they build the infrastructure to host open-source tools like Apache Airflow and dbt Core, or purchase their managed counterparts? This article focuses on these two components because pipeline orchestration and data transformation lie at the heart of any organization’s data platform.

What does it mean to build vs buy?

Build

When we say build in terms of open-source solutions, we mean building infrastructure to self-host and manage mature open-source tools like Airflow and dbt. These two tools are popular because they have been vetted by thousands of companies! In addition to hosting and managing, engineers must also ensure interoperability of these tools within their stack, handle security, scalability, and reliability. Needless to say, building is a huge undertaking that should not be taken lightly.

Buy

dbt and Airflow both started out as open-source tools, which were freely available to use due to their permissive licensing terms. Over time, cloud-based managed offerings of these tools were launched to simplify the setup and development process. These managed solutions build upon the open-source foundation, incorporating proprietary features like enhanced user interfaces, automation, security integration, and scalability. The goal is to make the tools more convenient and reduce the burden of maintaining infrastructure while lowering overall development costs. In other words, paid versions arose out of the pain points of self-managing the open-source tools.

This begs the important question: Should you self-manage or pay for your open-source analytics tools?

Comparing build vs. buy: Key tradeoffs  

As with most things, both options come with trade-offs, and the “right” decision depends on your organization’s needs, resources, and priorities. By understanding the pros and cons of each approach, you can choose the option that aligns with your goals, budget, and long-term vision.

Building In-House  

Pros:  

• Customization: The biggest advantage of building in-house is the flexibility to customize the tool to fit your exact use case. You maintain full control, allowing you to align configurations with your organization’s unique needs. However, with great power comes great responsibility—your team must have a deep understanding of the tools, their options, and best practices.  

• Control: Owning the entire stack gives your team the ability to integrate deeply with existing systems and workflows, ensuring seamless operation within your ecosystem.  

• Cost Perception: Without licensing fees, building in-house may initially appear more cost-effective, particularly for smaller-scale deployments.  

Cons:  

• High Upfront Investment: Setting up infrastructure requires a considerable time commitment from developers. Tasks like configuring environments, integrating tools like Git or S3 for Airflow DAG syncing, and debugging can consume weeks of developer hours.  

• Operational Complexity: Ongoing maintenance—such as managing dependencies, handling upgrades, and ensuring reliability—can be overwhelming, especially as the system grows in complexity.  

• Skill Gaps: Many teams underestimate the level of expertise needed to manage Kubernetes clusters, Python virtual environments, and secure credential storage systems like AWS Secrets Manager.  

• Experimentation: Your organization is using the first iteration the team is producing which can lead to unintended consequences, edge cases, and security issues.  

Example:

A team building Airflow in-house may spend weeks configuring a Kubernetes-backed deployment, managing Python dependencies, and setting up DAG synchronizing files via S3 or Git. While the outcome can be tailored to their needs, the time and expertise required represent a significant investment.  

Building with open-source is not free. Cons Continued

Before moving on to the buy tradeoffs, it is important to set the record straight. You may have noticed that we did not include “the tool is free to use” as one of our pros for building with open-source. You might have guessed by reading the title of this section, but many people incorrectly believe that building their MDS using open-source tools like dbt is free. When in reality there are many factors that contribute to the true dbt pricing and the same is true for Airflow.

How can that be?  Well, setting up everything you need and managing infrastructure for Airflow and dbt isn’t necessarily plug and play.  There is day-to-day work from managing Python virtual environments, keeping dependencies in check, and tackling scaling challenges which require ongoing expertise and attention. Hiring a team to handle this will be critical particularly as you scale. High salaries and benefits are needed to avoid costly mistakes; this can easily cost anywhere from $5,000 to $26,000+/month depending on the size of your team.

In addition to the cost of salaries, let’s look at other possible hidden costs that come with using open-source tools.  

Time and expertise  

The time it takes to configure, customize, and maintain a complex open-source solution is often underestimated. It’s not until your team is deep in the weeds—resolving issues, figuring out integrations, and troubleshooting configurations—that the actual costs start to surface. With each passing day your ROI is threatened. You want to start gathering insights from your data as soon as possible. Datacoves helped Johnson and Johnson set up their data stack in weeks and when issues arise, a you will need expertise to accelerate the time to resolution.

And then there’s the learning curve. Not all engineers on your team will be seniors, and turnover is inevitable. New hires will need time to get up to speed before they can contribute effectively. This is the human side of technology: while the tools themselves might move fast, people don’t. That ramp-up period, filled with training and trial-and-error, represents a hidden cost.  

Security and compliance  

Security and compliance add another layer of complexity. With open-source tools, your team is responsible for implementing best practices—like securely managing sensitive credentials with a solution like AWS Secrets Manager. Unlike managed solutions, these features don’t come prepackaged and need to be integrated with the system.  

Compliance is no different. Ensuring your solution meets enterprise governance requirements takes time, research, and careful implementation. It’s a process of iteration and refinement, and every hour spent here is another hidden cost as well as risking security if not done correctly.  

Scaling complexities  

Scaling open-source tools is where things often get complicated. Beyond everything already mentioned, your team will need to ensure the solution can handle growth. For many organizations, this means deploying on Kubernetes. But with Kubernetes comes steep learning curves and operational challenges. Making sure you always have a knowledgeable engineer available to handle unexpected issues and downtimes can become a challenge. Extended downtime due to this is a hidden cost since business users are impacted as they become reliant on your insights.    

Buying a managed solution  

A managed solution for Airflow and dbt can solve many of the problems that come with building your own solution from open-source tools such as: hassle-free maintenance, improved UI/UX experience, and integrated functionality. Let’s take a look at the pros.

Pros:  

• Faster Time to Value: With a managed solution, your team can get up and running quickly without spending weeks—or months—on setup and configuration.  

• Reduced Operational Overhead: Managed providers handle infrastructure, maintenance, and upgrades, freeing your team to focus on business objectives rather than operational minutiae.  

• Predictable Costs: Managed solutions typically come with transparent pricing models, which can make budgeting simpler compared to the variable costs of in-house built tooling.  

• Reliability: Your team is using version 1000+ of a managed solution vs the 1^st version of your self-managed solution. This provides reliability and peace of mind that edge cases have been handled, and security is under wraps.  

Cons:  

• Potentially Less Flexibility: This is the biggest con and reason why many teams choose to build. Managed solutions may not allow for the same level of customization as building in-house, which could limit certain niche use cases. Care must be taken to choose a provider that is built for enterprise level flexibility.

• Dependency on a Vendor: Relying on a vendor for your analytics stack introduces some level of risk, such as service disruptions or limited migration paths if you decide to switch providers. Some managed solution providers simply offer the service, but leave it up to your team to “make it work” and troubleshoot. A good provider will have a vested interest in your success, because they can’t afford for you to fail.  

Example:  

Using a solution like MWAA, teams can leverage managed Airflow eliminating the need for infrastructure worries however additional configuration and development will be needed for teams to leverage it with dbt and to troubleshoot infrastructure issues suck as containers running out of memory.

Datacoves for Airflow and dbt: The buy that feels like a build

For data teams, the allure of a custom-built solution often lies in its promise of complete control and customization. However, building this requires significant time, expertise, and ongoing maintenance. Datacoves bridges the gap between custom-built flexibility and the simplicity of managed services, offering the best of both worlds.

With Datacoves, teams can leverage managed Airflow and pre-configured dbt environments to eliminate the operational burden of infrastructure setup and maintenance. This allows data teams to focus on what truly matters—delivering insights and driving business decisions—without being bogged down by tool management.

Unlike other managed solutions for dbt or Airflow, which often compromise on flexibility for the sake of simplicity, Datacoves retains the adaptability that custom builds are known for. By combining this flexibility with the ease and efficiency of managed services, Datacoves empowers teams to accelerate their analytics workflows while ensuring scalability and control.

Datacoves doesn’t just run the open-source solutions, but through real-world implementations, the platform has been molded to handle enterprise complexity while simplifying project onboarding. With Datacoves, teams don’t have to compromize on features like Datacoves-Mesh (aka dbt-mesh), column level lineage, GenAI, Semantic Layer, etc. Best of all, the company’s goal is to make you successful and remove hosting complexity without introducing vendor lock-in. What Datacove does, you can do yourself if given enough time, experience, and money. Finally, for security concious organizations, Datacoves is the only solution on the market that can be deployed in your private cloud with white-glove enterprise support.

Datacoves isn’t just a platform—it’s a partnership designed to help your data team unlock their potential. With infrastructure taken care of, your team can focus on what they do best: generating actionable insights and maximizing your ROI.

Conclusion

The build vs. buy debate has long been a challenge for data teams, with building offering flexibility at the cost of complexity, and buying sacrificing flexibility for simplicity. As discussed earlier in the article, solutions like dbt and Airflow are powerful, but managing them in-house requires significant time, resources, and expertise. On the other hand, managed offerings like dbt Cloud and MWAA simplify operations but often limit customization and control.

Datacoves bridges this gap, providing a managed platform that delivers the flexibility and control of a custom build without the operational headaches. By eliminating the need to manage infrastructure, scaling, and security. Datacoves enables data teams to focus on what matters most: delivering actionable insights and driving business outcomes.

As highlighted in Fundamentals of Data Engineering, data teams should prioritize extracting value from data rather than managing the tools that support them. Datacoves embodies this principle, making the argument to build obsolete. Why spend weeks—or even months—building when you can have the customization and adaptability of a build with the ease of a buy? Datacoves is not just a solution; it’s a rethinking of how modern data teams operate, helping you achieve your goals faster, with fewer trade-offs.

dbt (data build tool) is a powerful data transformation tool that allows data analysts and engineers to transform data in their warehouse more effectively. It enables users to write modular SQL queries, which it then runs on top of the data warehouse; this helps to streamline the analytics engineering workflow by leveraging the power of SQL. In addition to this, dbt incorporates principles of software engineering, like modularity, documentation and version control.

dbt Core vs dbt Cloud

Before we jump into the list of dbt alternatives it is important to distinguish dbt Core from dbt Cloud. The primary difference between dbt Core and dbt Cloud lies in their execution environments and additional features. dbt Core is an open-source package that users can run on their local systems or orchestrate with their own scheduling systems. It is great for developers comfortable with command-line tools and custom setup environments. On the other hand, dbt Cloud provides a hosted service with dbt core as its base. It offers a web-based interface that includes automated job scheduling, an integrated IDE, and collaboration features. It offers a simplified platform for those less familiar with command-line operations and those with less complex platform requirements.

You may be searching for alternatives to dbt due to preference for simplified platform management, flexibility to handle your organization’s complexity, or other specific enterprise needs. Rest assured because this article explores ten notable alternatives that cater to a variety of data transformation requirements.

Below is a quick list of the dbt alternatives we will be covering in this article:

We have organized these dbt alternatives into 3 groups: dbt Cloud alternatives, code based dbt alternatives , and GUI based dbt alternatives.

dbt Cloud Alternatives

dbt Cloud is a tool that dbt Labs provides, there are a few things to consider:

• Flexibility, may be hindered by the inability to extend the dbt Cloud IDE with Python libraries or VS Code extensions
• Handlining enterprise complexity of an end-to-end ELT pipeline will require a full-fledged orchestration tool
• Costs can be higher than some of the alternatives below, especially at enterprise scale
• Data security and compliance may require VPC deployment which is not available in dbt Cloud
• Some features of dbt Cloud are not open source increasing vendor lock-in

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Although dbt Cloud can help teams get going quickly with dbt, it is important to have a clear understanding of the long-term vision for your data platform and get a clear understanding of the total cost of ownership. You may be reading this article because you are still interested in implementing dbt but want to know what your options are other than dbt Clould.

Datacoves

Datacoves is tailored specifically as a seamless alternative to dbt Cloud. The platform integrates directly with existing cloud data warehouses, provides a user-friendly interface that simplifies the management and orchestration of data transformation workflows with Airflow, and provides a preconfigured VS Code IDE experience. It also offers robust scheduling and automation with managed Airflow, enabling data transformations with dbt to be executed based on specific business requirements.

Benefits:

Flexibility and Customization: Datacoves allows customization such as enabling  VSCode extensions or adding any Python library. This flexibility is needed when adapting to dynamic business environments and evolving data strategies, without vendor lock-in.

Handling Enterprise Complexity: Datacoves is equipped with managed Airflow, providing a full-fledged orchestration tool necessary for managing complex end-to-end ELT pipelines. This ensures robust data transformation workflows tailored to specific business requirements. Additionally, Datacoves does not just support the T (transformations) in the ELT pipeline, the platform spans across the pipeline by helping the user tie all the pieces together. From initial data load to post-transformation operations such as pushing data to marketing automation platforms.

Cost Efficiency: Datacoves optimizes data processing and reduces operational costs associated with data management as well as the need for multiple SaaS contracts. Its pricing model is designed to scale efficiently.

Data Security and Compliance: Datacoves is the only commercial managed dbt data platform that supports VPC deployment in addition to SaaS, offering enhanced data security and compliance options. This ensures that sensitive data is handled within a secure environment, adhering to enterprise security standards. A VPC deployment is advantageous for some enterprises because it helps reduce the red tape while still maintaining optimal security.

Open Source and Reduced Vendor Lock-In: Datacoves bundles a range of open-source tools, minimizing the risk of vendor lock-in associated with proprietary features. This approach ensures that organizations have the flexibility to switch tools without being tied to a single vendor.

Do-it-Yourself dbt Core

It is worth mentioning that that because dbt Core is open source a DIY approach is always an option. However, opting for a DIY solution requires careful consideration of several factors. Key among these is assessing team resources, as successful implementation and ongoing maintenance of dbt Core necessitate a certain level of technical expertise. Additionally, time to production is an important factor; setting up a DIY dbt Core environment and adapting it to your organization’s processes can be time-consuming.  

Finally, maintainability is essential- ensuring that the dbt setup continues to meet organizational needs over time requires regular updates and adjustments. While a DIY approach with dbt Core can offer customization and control, it demands significant commitment and resources, which may not be feasible for all organizations.

Benefits:

This is a very flexible approach because it will be made in-house and with all the organization’s needs in mind but requires additional time to implement and increases the total cost of long-term ownership.

dbt alternatives - Code based ETL tools

For organizations seeking a code-based data transformation alternative to dbt, there are two contenders they may want to consider.

SQLMesh

SQLMesh is an open-source framework that allows for SQL or python-based data transformations. Their workflow provides column level visibility to the impact of changes to downstream models. This helps developers remediate breaking changes. SQLMesh creates virtual data environments that also eliminate the need to calculate data changes more than once. Finally, teams can preview data changes before they are applied to production.

Benefits:

SQLMesh allows developers to create accurate and efficient pipelines with SQL. This tool integrates well with tools you are using today such as Snowflake, and Airflow. SQLMesh also optimizes cost savings by reusing tables and minimizing computation.

Dataform

Dataform enables data teams to manage all data operations in BigQuery. These operations include creating table definitions, configuring dependencies, adding column descriptions, and configuring data quality assertions. It also provides version control and integrates with GitLab or GitHub.

Benefits:

Dataform is a great option for those using BigQuery because it fosters collaboration among data teams with strong version control and development practices directly integrated into the workflow. Since it keeps you in BigQuery, it also reduces context switching and centralizes data models in the warehouse, improving efficiency.  

AWS Glue

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. It automates the provisioning of ETL code. It is worth noting that Amazon Glue offers GUI elements (like Glue Studio).

Benefits:

AWS Glue provides flexible support for various pipelines such as ETL, ELT, batch and more, all without a vendor lock-in. It also scales on demand, offering a pay-as-you-go billing. Lastly, this all-in-one platform has tools to support all data users from the most technical engineers to the non-technical business users.

dbt alternatives - Graphical ETL tools

While experience has taught us that there is no substitute for a code-based data transformation solution. Some organizations may opt for a graphical user interface (GUI) tool. These tools are designed with visual interfaces that allow users to drag and drop components to build data integration and transformation workflows. Ideal for users who may be intimidated by a code editor like VS Code, graphical ETL tools may simplify data processes in the short term.  

Matillion

Matillion is a cloud-based data integration platform that allows organizations to build and manage data pipelines and create no-code data transformations at scale. The platform is designed to be user-friendly, offering a graphical interface where users can build data transformation workflows visually.

Benefits:

Matillion simplifies the ETL process with a drag-and-drop interface, making it accessible for users without deep coding knowledge. It also supports major cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake, enhancing scalability and performance.

Informatica

Informatica offers extensive data integration capabilities including ETL, hundreds of no code connectors cloud connectors, data masking, data quality, and data replication. It also uses a metadata-driven approach for data integration. In addition, it was built with performance, reliability, and security in mind to protect your valuable data.

Benefits:

Informatica enhances enterprise scalability and supports complex data management operations across various data types and sources. Informatica offers several low-code and no-code features across its various products, particularly in its cloud services and integration tools. These features are designed to make it easier for users who may not have deep technical expertise to perform complex data management tasks.

Alteryx

Alteryx allows you to automate your analytics at scale. It combines data blending, advanced analytics, and data visualization in one platform. It offers tools for predictive analytics and spatial data analysis.  

Benefits:

Alteryx enables users to perform complex data analytics with AI. It also improves efficiency by allowing data preparation, analysis, and reporting to be done within a single tool. It can be deployed on-prem or in the cloud and is scalable to meet enterprise needs.

Azure Data Factory

Azure Data Factory is a fully managed, serverless data integration service that integrates with various Azure services for data storage and data analytics. It provides a visual interface for data integration workflows which allows you to prepare data, construct ETL and ELT processes, and orchestrate and monitor pipelines code-free.

Benefits:

Azure Data Factory can be beneficial for users utilizing various Azure services because it allows seamless integration with other Microsoft products, which is ideal for businesses already invested in the Microsoft ecosystem. It also supports a pay-as-you-go model.

Talend

Talend offers an end-to-end modern data management platform with real-time or batch data integration as well as a rich suite of tools for data quality, governance, and metadata management. Talend Data Fabric combines data integration, data quality, and data governance into a single, low-code platform.

Benefits:

Talend can enhance data quality and reliability with built-in tools for data cleansing and validation. Talend is a cloud-independent solution and supports cloud, multi-cloud, hybrid, or on-premises environments.

SSIS (SQL Server Integration Services)

SQL Server Integration Services are a part of Microsoft SQL Server, providing a platform for building enterprise-level data integration and data transformations solutions. With this tool you can extract and transform data from a wide variety of sources such as XML data files, flat files, and relational data sources, and then load the data into one or more destinations. It Includes graphical tools and wizards for building and debugging packages.

Benefits:

SQL Server Integration Services are ideal for organizations heavily invested in SQL Server environments. They offer extensive support and integration capabilities with other Microsoft services and products.

Conclusion

While we believe that code is the best option to express the complex logic needed for data pipelines, the dbt alternatives we covered above offer a range of features and benefits that cater to different organizational needs. Tools like Matillion, Informatica, and Alteryx provide graphical interfaces for managing ETL processes, while SQLMesh, and Dataform offer code-based approaches to SQL and Python based data transformation.  

For those specifically looking for a dbt Cloud alternative, Datacoves stands out as a tailored, flexible solution designed to integrate seamlessly with modern data workflows, ensuring efficiency and scalability.