Top dbt Alternatives and Competitors –  Ranked by G2

dbt (Data Build Tool) established a new standard for data transformation, equipping teams with software engineering practices like version control, testing, and modularity for analytics. This empowered countless teams to reliably shape data within their warehouses using dbt Core, its open-source CLI, or dbt Cloud, its managed service offering features like an IDE and scheduling. For years, dbt has empowered teams to transform data within their warehouses reliably.

Yet, as enterprise demands evolve and the data landscape matures, even a powerful tool like dbt might not be the perfect fit for every scenario or meet every data engineer’s specific transformation needs. 

That’s why we’ve explored the data market to bring you a curated list of compelling dbt alternatives, each offering unique strengths and approaches to help you conquer your data transformation challenges. We’ll dive into their key features, weigh their pros and cons, and help you choose the one that best aligns with your use case.

Here is a quick summary of all the tools that we have discussed below:

Tool	Key Features	Pros	Cons
Hevo Data	No-code ELT + Integrated dbt Core execution via Hevo Transformer; Pre-load transformations (e.g., JSON parsing, sanitization).	Fully managed ELT + dbt platform; Eliminates the need for dbt Cloud; Real-time collaboration tools for dbt.	No custom connectors; Limited on-premise support.
Azure Data Factory	Mapping Data Flows translated to Apache Spark; Executes diverse compute jobs (Databricks, Functions, SSIS).	Deep Azure ecosystem integration; Handles both code-based and visual workflows.	High learning curve for non-Azure users; Costs can escalate with scale.
Informatica	CLAIRE AI for intelligent transformation suggestions; Broadest legacy system connectivity.	Enterprise-grade data governance & metadata lineage; On-premise + cloud support.	High licensing cost; Steep learning curve.
Alteryx	Visual APA platform with Intelligence Suite (text analytics, computer vision); Assisted modeling for ML.	Ideal for non-coders; Transparent workflows; Built-in reporting tools.	Limited for large-scale centralized modeling; Core engine bottlenecks possible.
Datacoves	Managed Airflow + dbt Core in VS Code workspace; Blueprints for CI/CD, testing, DataOps best practices.	Highly customizable; No black-box processes; Open-source stack reduces vendor lock-in.	Not ideal for GUI-focused users; VPC setup required for enhanced security.
Talend	Visual design generates Java/Spark code; Built-in machine learning for data enrichment and de-duplication.	Embedded data quality checks; Flexible hybrid/cloud/on-prem deployment.	Requires tuning for Spark jobs; Licensing for the full suite can be expensive.
Fivetran	Managed dbt Core tied to ingestion syncs; Offers pre-built dbt packages for fast analytics.	Seamless ingestion + transformation in one platform; Simplified CI/CD via Git.	Only supports dbt for transformations; Monthly Active Rows pricing can unexpectedly increase.
Matillion	Pushdown ELT with visual job builder; Native SQL and Python scripting directly in warehouse.	Warehouse-native ELT; Git-integrated job management; Built-in orchestration.	Not ideal for streaming/event-based transformations; Advanced features require technical know-how.

What is dbt?

dbt (data build tool) is an open-source tool that transforms raw data into actionable, analysis-ready insights. It leverages the power of SQL to perform transformations directly inside your data warehouse. 

When dbt was launched, dbt’s code-centric, engineering-minded approach made a seismic shift for data transformation by cleverly weaving essential software engineering principles like version control, testing, modularity, and documentation into the data transformation. 

The result? Teams could finally build data models collaboratively that are not only neatly modular and reusable but also incredibly reliable and well-explained.

Why Are People Looking for dbt Alternatives?

Even with its significant impact on data transformation, dbt’s engineering-centric, code-first approach might not be the perfect fit for every team or every use case. Some organizations seek more flexibility or different capabilities.

Upon investigating, we found some commonly occurring reasons why data professionals might explore options beyond dbt:

Limited Visual Interfaces

Not every team possesses the same level of technical expertise to comfortably navigate a completely code-dependent tool. Many are looking for no-code or low-code solutions for their transformations, particularly tools that offer a visual representation of the data flow, such as drag-and-drop features.

“When a dbt model fails, pinpointing the exact cause can be a real time sink. Sifting through logs and complex Jinja-rendered SQL isn’t always straightforward, and it definitely slows down our development and iteration speed. More intuitive debugging would be a huge plus.”

– David K., Senior Analytics Engineer

Need for Real-Time Streaming Capabilities

dbt excels at batch transformations, which is ideal for many analytical workloads. However, if your use case demands transforming data in real-time or processing streaming data as it arrives (e.g., for immediate fraud detection or operational dashboards), dbt’s batch-oriented architecture isn’t designed for this.

“We’re increasingly moving towards real-time analytics for our core operations, but dbt’s batch nature just doesn’t fit this model. We end up with complex, often brittle workarounds to simulate near real-time. We’re actively looking for a tool that can handle streaming transformations natively and efficiently.”

– John B., Lead Data Engineer

Steep Learning Curve

Mastering dbt often involves getting comfortable with a suite of developer tools and concepts, including Git for version control, YAML for configurations, and Jinja for templating SQL. These skills, while powerful, are not universally common among all data professionals, and the learning curve can be a significant hurdle, especially for analysts or those newer to data engineering.

“Getting our newer analysts up to speed on dbt, especially with Git, YAML, and the intricacies of Jinja, takes a considerable amount of time and dedicated training. The initial learning curve is pretty steep for those who don’t come from a strong coding or software engineering background. We need something more accessible for broader team adoption.”

– Emily P., Data Team Lead

Debugging Challenges

While dbt provides logs and testing capabilities, troubleshooting failed transformations or unexpected outcomes often means diving deep into generated SQL, interpreting error messages, and navigating through potentially complex model dependencies. This can slow down development cycles and make maintenance more challenging, especially for intricate projects.

“When a dbt model fails, pinpointing the exact cause can be a real time sink. Sifting through logs and complex Jinja-rendered SQL isn’t always straightforward, and it definitely slows down our development and iteration speed. More intuitive debugging would be a huge plus.”

– David K., Senior Analytics Engineer

Top dbt Alternatives to Consider in 2025

Hevo Data

Hevo Data offers a comprehensive, no-code ELT platform designed to streamline your entire data pipeline from source to analysis-ready insights. For years, Hevo has empowered users to effortlessly stream data from over 150 sources, providing robust in-flight transformations. 

These include automatic data sanitization, JSON parsing, and date conversions, alongside user-defined transformations using intuitive drag-and-drop blocks or powerful Python scripts, all happening before data even hits your destination.

But Hevo doesn’t stop there. Recognizing the power and popularity of dbt Core, Hevo has ingeniously introduced Hevo Transformer.  You can set up, build, run, and test your dbt projects straight from Hevo’s platform. 

It’s like getting the power of dbt Core without the headache of managing servers or paying those hefty dbt Cloud fees. Hevo Transformer takes care of the infrastructure and automation, letting your team collaborate effectively and transform raw data into actionable insights with confidence.

If you are looking for a no-code implementation while ensuring top-notch data consistency and security, Hevo Data makes a strong case as your go-to tool. 

If you love dbt Core for those deep in-warehouse transformations but wish someone else would manage the grunt work? Hevo Transformer is what you are looking for. 

And with Hevo Transformer currently free during its beta phase, there’s never been a better time to experience this powerful, integrated approach to data transformation.

Key Features

• Simplify dbt Workflow Automation: Save time using powerful automation tools within the Transformer IDE to build, test, and run dbt models seamlessly in one intuitive platform, simplifying your workflow and enhancing productivity.
• Version Control and Team Collaboration: Hevo transformer helps your team collaborate effectively using built-in Git integration, ensuring teamwork, precise change tracking, and complete alignment across the data transformation process.
• Collaboration Tools: Work together on transformation workflows in real-time, share dbt models with team members, and track changes effectively to ensure consistency, improve efficiency, and reduce errors through real-time coordination.
• Seamless Integration with dbt Core: Create, test, and run dbt models directly on Hevo’s platform, leveraging dbt Core’s full capabilities like version control and modular SQL, while potentially avoiding the licensing fees associated with dbt Cloud.

Pros

• Fully Managed Service: Hevo handles all backend infrastructure, including servers, software updates, and scaling, offering a zero-maintenance platform with high availability and reliability, allowing users to focus entirely on building and optimizing data models.
• Integrate with any Data Warehouse in no time: Effortlessly connect to platforms like Snowflake with zero hassle, as Hevo automatically fetches the schema to facilitate building data transformations.
• Scalable: Hevo transformer is designed to scale effortlessly with your business, from small teams to large enterprises, handling large volumes of data and complex transformation workflows without hidden costs or compromising performance.

Cons

• Limited on-premise support: Primarily built for cloud-based integrations.
• No custom connectors

Why Choose Hevo Data (with Hevo Transformer) over dbt?

Hevo Data offers a compelling advantage for teams seeking an end-to-end, no-code ELT platform where dbt transformations are blended rather than a separate, albeit powerful, component. If your primary need is to minimize engineering overhead across the entire data pipeline (ingestion, pre-load transformations, and in-warehouse dbt transformations) and you prefer a unified, fully managed service that handles infrastructure for both ELT and dbt Core execution without separate dbt Cloud fees, Hevo’s integrated Transformer provides a more holistic and potentially cost-effective solution.

Pricing

Hevo provides transparent pricing that ensures no billing surprises even as you scale. It provides four pricing plans, which are:

• Free: For moving minimal amounts of data from SaaS tools. Provides up to 1 M free events/month.
• Starter: $239/Month – Moving limited data from SaaS tools and databases.
• Professional: $679/Month – For considerable data needs and higher control over data ingestion.
• Business Critical: You can customize it according to your ETL requirements. This is for advanced data requirements like real-time data ingestion.

Azure Data Factory

Azure Data Factory (ADF) is Microsoft’s cloud-native solution for orchestrating and automating data movement and transformation. It acts as a central conductor, leveraging the might of various Azure compute services like Databricks, Synapse Analytics, or even Azure 

Functions to execute the heavy lifting. This allows for a flexible, serverless approach where you design the “what” and “when,” while ADF manages the “how” of data processing.

Its deep integration and visual-first approach to pipeline authoring sets ADF apart, especially within the Azure ecosystem. While it can execute custom code, its Mapping Data Flows offer a graphical interface for designing complex transformations without writing extensive Spark or SQL. Under the hood, ADF translates these visual designs into Apache Spark code that runs on Azure-managed Spark clusters. You don’t need to manage the Spark clusters yourself.

ADF is particularly compelling for organizations already invested in the Azure stack, as it provides a cohesive experience with other services like Azure Blob Storage, Azure SQL Database, and Synapse Analytics. Data engineers and ETL developers who prefer a visual development environment or need to orchestrate diverse compute engines for their transformation tasks will find ADF a natural fit. 

Key features

• Visual Design & Mapping Data Flows: ADF offers a drag-and-drop interface with its Mapping Data Flows. This allows you to design complex ETL/ELT logic visually, making intricate transformations look as clear as a flowchart.
• Code-Based Transformation Options: While visual tools are great, sometimes you need the full power of code. ADF embraces this by integrating with robust Azure compute services. You can execute your existing Python, Scala, SQL, or R notebooks and jobs on Azure Databricks clusters, leverage Azure Synapse Analytics, run SQL Stored Procedures, or even invoke Azure Functions for custom logic, giving you flexibility and control.
• Version Control & DevOps Integration: ADF pipelines (which are JSON definitions) integrate natively with Git for comprehensive version control. This, coupled with strong Azure DevOps support for CI/CD, means you can implement automated testing, staged deployments, and collaborative development practices for your transformation workflows.
• Serverless Operations & Intelligent Scheduling: ADF provides serverless compute that automatically scales up or down based on the workload, optimizing performance and cost. Complementing this, its smart scheduling capabilities allow you to trigger pipelines based on fixed schedules, specific events (like a new file arriving), or even complex tumbling window conditions.
• SSIS Package Lift-and-Shift: For organizations with existing investments in SQL Server Integration Services (SSIS), ADF offers a smooth migration path. You can easily rehost your SSIS packages to run within ADF, allowing you to modernize your ETL processes and manage them within your cloud-native Azure data estate.

Pros

• Strong integration with other Azure services.
• Scalable and flexible.
• Hybrid connectivity cloud and on-premises worlds without building a custom solution.

Cons

• It may require familiarity with the Azure ecosystem.
• It can become costly with large-scale operations.

Why Choose Azure Data Factory (ADF) over dbt?

Azure Data Factory excels over a standalone dbt setup when your transformation needs extend beyond SQL-based logic within a single data warehouse and require diverse compute services within the Azure ecosystem. If you need to integrate Python/Spark jobs on Databricks, run SSIS packages, leverage Azure Functions for custom processing, or visually design complex data flows that are then translated into Spark, ADF provides a much broader data movement capability that dbt, by itself, doesn’t offer, making it ideal for complex, multi-faceted Azure-centric data processing.

Pricing

ADF follows a pay-as-you-go pricing model based on the number of data pipelines and integration runtime usage.

Informatica

Informatica has been an old player in enterprise data integration, offering solutions for complex data movement and transformation. Informatica operates with its own powerful ETL engine, emphasizing a metadata-driven development approach. 

Informatica’s traditional on-premises platform is known as PowerCenter, and its modern cloud-native counterpart is Informatica Intelligent Cloud Services (IICS). Informatica is powered by Claire AI, an Intelligent Data Management Cloud engine that simplifies data access, automates labor-intensive tasks, and ensures AI-readiness.

What distinguishes Informatica is its extensive library of pre-built transformation functions, coupled with a high-performance engine. 

Informatics is well-suited for data engineers who are looking for a feature-rich platform with a proven record for handling high-volume transformations and are working on complex hybrid data architecture. Following its recent acquisition by Salesforce, Informatica is likely to have much more to unveil in the near future.

Key Features

• CLAIRE AI Engine Integration: Informatica’s AI engine, CLAIRE, is embedded across IICS. It provides intelligent recommendations for data discovery, schema mapping, transformation logic, data quality rule generation, and operational optimizations, aiming to accelerate development and improve data management outcomes.
• Extensive Library of Transformation functions: A comprehensive suite of pre-built transformations like Joiner, Lookup (cached or uncached), Aggregator, Expression, Filter, Router, and Sorter allows data engineers to perform complex transformations easily without custom coding for most scenarios.
• Visual Mapping & Development Environment: Informatica provides a graphical interface (PowerCenter Designer or IICS Mapping Designer) where developers drag-and-drop sources, targets, and transformation objects to build data pipelines, offering a clear visual representation of data flow and logic.
• Broad Connectivity: Informatica boasts one of the industry’s most extensive sets of connectors for a vast array of data sources, including databases (relational, NoSQL), applications (Salesforce, SAP), cloud platforms, mainframes, flat files, XML, JSON, and more.
• Reusable Components & Parameterization: Developers can create reusable transformation logic (e.g., mapplets, user-defined functions) and parameterize mappings and workflows, promoting modular design, reducing development effort, and simplifying maintenance.

Pros

• Highly optimized processing engine for effortless scalability.
• Both on-premises and cloud support.
• Proven track record in complex, high-volume, mission-critical environments.

Cons

• Can have a higher total cost of ownership, particularly due to licensing and infrastructure for on-premises deployments.
• The learning curve can be steeper for developers new to the platform compared to some newer, more specialized tools.
• While IICS is a modern cloud offering, the platform’s extensive feature set and enterprise focus can sometimes feel less agile for very small or narrowly focused projects.

Why Choose Informatica over dbt?

Informatica is a stronger choice than dbt for large enterprises with deeply entrenched, complex hybrid data architectures and stringent data governance, lineage, and metadata management requirements that span beyond the data warehouse. If your organization requires robust, enterprise-grade ETL/ELT capabilities proven over decades, extensive connectivity to legacy systems (like mainframes), and a metadata-driven approach with advanced data quality and masking features embedded directly within the transformation flows, Informatica offers a more comprehensive, albeit more traditional, enterprise data integration suite.

Pricing

Informatica’s pricing is typically enterprise-focused. For IICS, it often follows a subscription model based on factors like Informatica Processing Units (IPUs), data volume, or service tiers. PowerCenter traditionally involves perpetual licensing based on server cores or processing power. 

Alteryx

Alteryx stands out in the analytics landscape as a powerful Analytics Process Automation (APA) platform, with Alteryx Designer at its core. Alteryx Designer tackles complex data transformation and preparation tasks through its highly visual and workflow-centric platform. 

What distinguishes Alteryx is its potent combination of user-friendliness and a deep arsenal of data processing tools. It allows data cleansing, reshaping, blending, and enrichment workflows without demanding deep programming expertise. While development happens on a desktop client, the transformation workflows can be scheduled and operationalized using Alteryx Server.

Alteryx Designer is particularly effective for teams needing to ingest data from diverse sources rapidly. It excels where data quality is variable and complex business rules must be applied to prepare data for downstream analytics, reporting, or loading into structured data stores.

Key Features

• Transforming Unstructured Data with Intelligence Suite: The Alteryx Intelligence Suite extends transformation capabilities to unstructured data. This includes tools for text mining (extracting concepts, performing sentiment analysis to understand emotion hidden in text) and computer vision, enabling insights from data types beyond traditional structured formats.
• Versatile Output & Reporting Options: Transformed data isn’t just stored; it’s often visualized or reported. Alteryx includes user-friendly reporting tools to directly create high-quality, data-driven reports (in formats like PDF, HTML, XLSX) incorporating text, charts, maps, and images derived from your transformed datasets.
• Formula & Expression Engine: A powerful tool for creating derived columns, applying complex conditional logic, and performing calculations on data.
• Reusable Transformation Logic (Macros): Allows users to create reusable macros, which are complex sequences of transformation steps. These macros can then be reused across multiple workflows, promoting consistency and saving development time.
• Preparing Data for Advanced Analytics & ML: While a complete analytics platform, a key transformation use case is preparing data for predictive modeling. Alteryx facilitates this by allowing users to access, clean, transform, and model data, even leveraging Assisted Modelling features to build ML pipelines all within a single, unified environment.

Pros

• Accelerated development of complex transformation workflows due to its visual nature and drag-and-drop feature.
• Empowers even non-technical users to perform sophisticated data preparation.
• Can transform and process geospatial data.
• Provides good transparency into the transformation logic through the visual workflow.

Cons

• While scalable with in-database tools, the core engine processing can be a bottleneck for extremely large, distributed data volumes if not designed carefully.
• Alteryx is less focused on building and managing large-scale, multi-layered data models within a central repository.

Why Choose Alteryx over dbt?

Alteryx presents a significant advantage over dbt for teams where business analysts or citizen data scientists, who may not have deep SQL or coding expertise, need to perform complex data preparation, blending, and advanced analytics (including spatial and predictive). Its visual, drag-and-drop workflow environment empowers users to build sophisticated transformation and analytical pipelines without writing code, and its capabilities extend to transforming unstructured data and generating direct reports, where dbt’s SQL-centric, in-warehouse approach is less focused.

Pricing

Alteryx primarily uses a subscription-based licensing model for Alteryx Designer (per user). Alteryx Server, for scheduling and sharing workflows, is licensed separately and adds to the overall cost. Pricing details are typically obtained through direct sales engagement.

Datacoves

Datacoves is a DataOps platform designed to enhance and streamline the entire ELT (Extract, Load, Transform) process, with a strong emphasis on managing dbt Core transformations. It provides a fully integrated environment that bundles dbt Core with managed Airflow, accessible through a pre-configured VS Code workspace.

What distinguishes Datacoves is its integrated approach to the modern data stack, particularly for teams leveraging dbt for transformations but seeking more powerful orchestration and a developer-centric environment. It aims to solve the challenge of needing multiple, disparate tools by providing a unified platform where dbt transformations are developed within a familiar VS Code IDE and orchestrated seamlessly using a managed Apache Airflow instance.

Datacoves is particularly compelling for data teams that use dbt Core for transformations but require a more holistic, customizable, and scalable platform than what a standalone transformation tool might offer. 

Key Features

• Integrated dbt Core & VS Code Environment: Provides a pre-configured, browser-based VS Code environment, offering technical users a familiar, highly customizable, and flexible workspace for developing dbt Core transformation projects. This allows for using any VS Code extensions and custom Python libraries.
• Managed Airflow for Full ELT Orchestration: A cornerstone of Datacoves is its managed Apache Airflow solution. This enables teams to orchestrate not just dbt transformations but the entire ELT pipeline, including ingestion, pre-transformation steps, post-transformation actions like reverse ETL, and complex inter-dependencies.
• Enterprise-Grade Scalability & Complexity Handling: Built on Kubernetes, the managed Airflow solution is designed to handle large and complex workflows efficiently. Features like Datacove’s Mesh further enhance scalability by allowing large data projects to be split into smaller, more manageable sub-projects.
• DataOps: The platform is built to support modern DataOps practices without “black-box” processes. It provides out-of-the-box blueprints to accelerate process maturity, including best practices for CI/CD, dbt project architecture, blue-green deployments, and data security.
• Open Source Foundation & Reduced Vendor Lock-In: Datacoves leverages open-source tools like dbt Core and Airflow at its core, and integrates best-in-class open-source libraries. This minimizes vendor lock-in and allows organizations to adapt and extend their stack as new open-source technologies emerge.

Pros

• End-to-end orchestration with managed Airflow, overcoming limitations of standalone transformation tools.
• Promotes DataOps best practices.
• Reduces vendor lock-in by building on open-source foundations.

Cons

• The platform’s power comes from its integrated nature; teams looking for a very simple, purely transformation-focused GUI tool might find it more comprehensive than needed.
• The VPC deployment for enhanced security might involve more setup than a pure SaaS solution.

Why Choose Datacoves over dbt?

Datacoves offers a distinct advantage for dbt Core users who want to elevate their DataOps maturity with an integrated development environment built around open-source standards. If your team seeks the power of dbt Core combined with managed Airflow for robust ELT orchestration, a pre-configured VS Code IDE for development, and out-of-the-box DataOps blueprints (CI/CD, testing, advanced deployment strategies) without the vendor lock-in or potential black-box nature of some managed services, Datacoves provides a more customizable and developer-centric platform.

Pricing

To get detailed information on Datacove’s pricing, kindly contact their sales team.

Talend

Talend is a data integration and management platform, with its Talend Data Fabric product suite offering an end-to-end solution. It aims to unify data integration, quality, and governance within a single, low-code environment. Talend’s transformation capabilities are to provide a visual design paradigm for building data pipelines that can handle both batch and real-time data streams, generating executable code under the hood.

What makes Talend unique is its commitment to open-source roots, with Talend Open Studio being a well-known entry point, combined with enterprise-grade features in its commercial offerings. Its ability to generate code from visual designs allows for customization and extensibility.

Talend is suitable for organizations seeking an end-to-end data management solution that spans various deployment models, cloud, multi-cloud, hybrid, or on-premises. Data engineers and ETL developers who appreciate a visual development environment but also need the option to delve into generated code or leverage its extensive component library will find Talend versatile.

Key Features

• Visual Transformation Studio (Talend Studio): Talend Studio provides a graphical, drag-and-drop interface where developers design data integration “jobs” by connecting a vast library of pre-built components. These visual designs are then translated into optimized Java or Spark code for execution, abstracting much of the boilerplate coding.
• Integrated Data Quality Management: A key strength is the tight integration of data quality tools directly within transformation workflows. Components for data profiling, cleansing (standardization, validation, de-duplication using various algorithms), and enrichment can be embedded in data pipelines, ensuring data integrity as it’s processed.
• Machine Learning-Assisted Transformations: The platform incorporates machine learning capabilities to aid in data transformation tasks such as data standardization, intelligent de-duplication, automated data mapping suggestions, and data enrichment, aiming to improve accuracy and reduce manual effort.
• Real-Time and Batch Processing Unified: Talend can handle both batch-oriented transformations for large historical datasets and real-time data integration for streaming data sources.
• Code Generation and Extensibility: While primarily visual, Talend generates underlying Java or Spark code that can be inspected, customized, or extended by developers needing to implement highly specific logic or integrate custom routines.

Pros

• Offers a wide array of component libraries for various data manipulation, processing, and orchestration tasks.
• Strong, built-in data quality features that can be directly embedded within transformation processes.
• Flexible deployment options and good extensibility through generated code and custom components.

Cons

• Steeper learning curve for users solely focused on specific, narrow transformation tasks.
• Performance for very large-scale Spark jobs can depend heavily on proper job design and configuration of the Talend job.
• Licensing costs for the whole enterprise suite can be significant.

Why Choose Talend over dbt?

Talend is a more suitable alternative to dbt when organizations require a unified platform that combines visual ETL/ELT design with strong, integrated data quality management and the ability to generate and customize underlying code (Java/Spark) for complex transformations. If your needs go beyond SQL-based transformations and include extensive data profiling, cleansing, enrichment within the same visual workflow, and the flexibility to deploy on-premise, in the cloud, or hybrid scenarios, Talend’s comprehensive Data Fabric offers a broader data integration and governance scope.

Pricing

Talend offers various product editions, from the free, open-source Talend Open Studio to enterprise-grade commercial offerings within the Talend Data Fabric. Pricing for commercial versions is typically subscription-based and tailored to the organization’s specific needs, data volume, and the modules required. Direct engagement with Talend sales is usually necessary for detailed quotes.

Fivetran

Fivetran is a fully managed data integration platform for automating data extraction and loading from sources to data warehouses.

Fivetran’s approach to transformation is centered around seamless integration with dbt Core and providing a fully managed environment for these transformations. This means users can leverage their existing dbt projects or build new ones, with Fivetran handling the orchestration, scheduling, and operational overhead associated with running dbt jobs.

This integrated transformation capability is particularly compelling for teams already using Fivetran for data ingestion and who want to streamline their data stack by managing transformations within the same ecosystem. It appeals to data engineers and analysts who are comfortable with dbt’s SQL-first approach but seek a managed service to reduce the operational burden of self-hosting dbt Core or managing separate orchestration tools for their transformation logic.

Key Features

• Integrated Scheduling & Orchestration: Transformations can be scheduled to run automatically when your Fivetran data sync has completed, guaranteeing transformations will apply against the most recent data. Integrating scheduling and orchestration helps remove the management of dependencies between the data loading and transformation steps.
• Version Control via Git Integration: Fivetran integrates directly with Git repositories, allowing for robust version control, collaboration, and CI/CD practices for your dbt transformation code, just as you would with a self-managed dbt setup.
• Pre-built dbt Packages (Fivetran Data Models): Fivetran offers a suite of pre-built dbt packages, often referred to as Fivetran Data Models, for many popular sources. These provide ready-to-use dbt models that transform raw Fivetran-replicated data into analytics-ready tables, significantly accelerating time-to-insight.

Pros

• Combines automated data ingestion with managed dbt transformations in one platform.
• Eliminates the need to self-host or manage infrastructure for dbt Core execution.
• Fivetran’s core strength in data ingestion (500+ connectors) ensures data is readily available for transformation.

Cons

• Fivetran’s Monthly Active Rows (MAR) pricing can be affected by transformation patterns, potentially increasing costs if transformations significantly expand the number of active rows.
• The transformation capabilities are primarily focused on dbt; if your team uses other transformation tools or paradigms, Fivetran’s integrated solution might be less suitable.
• While managed, it may offer less fine-grained control over the dbt execution environment compared to a fully self-managed dbt Core setup.

Why choose Fivetran over dbt?

Fivetran’s integrated dbt transformation feature becomes a compelling alternative when the primary goal is to achieve maximum automation and minimal operational overhead for the entire ELT pipeline, from source ingestion to transformed data. If your team already relies on Fivetran for its extensive connector library and automated data loading, and you want to simply add dbt transformations without managing a separate dbt Cloud subscription or self-hosting dbt Core, Fivetran offers a streamlined, “all-in-one” experience where transformations are tightly coupled with data syncs.

Pricing Model

Fivetran uses a consumption-based pricing model primarily determined by Monthly Active Rows (MAR) – the number of distinct primary keys moved from source to destination each month. Transformations run within Fivetran could potentially influence MAR. 

Matillion

Matillion is a cloud-native ELT platform built to empower data teams to efficiently load and transform data directly within modern cloud data warehouses such as Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Databricks. It leverages the massive processing power and scalability of these target platforms by generating and executing native SQL for all transformation tasks. This “pushdown” methodology makes sure that data is processed where it resides, minimizing data movement and maximizing performance.

What distinguishes Matillion is its focus on the ELT combined with a user-friendly, browser-based visual interface. It enables data engineers and analysts to design complex transformation workflows by connecting pre-built components, which Matillion then translates into optimized code specific to the chosen cloud data warehouse.

Matillion is particularly compelling for organizations that have adopted a modern cloud data warehouse and are looking to streamline their data preparation and transformation pipelines.

Key Features

• Job Designer & SQL Generation: Matillion offers a drag-and-drop interface for building transformation jobs. Users connect components representing various data operations, and Matillion automatically generates the underlying SQL optimized for the target cloud data warehouse.
• Pushdown ELT Architecture: All transformation logic is executed directly within the cloud data warehouse. This leverages the warehouse’s native processing power, scalability, and performance features, avoiding the need to move data to a separate transformation engine.
• Rich Library of Transformation Components: Provides a comprehensive set of pre-built components for common data transformation tasks, including Join (various types), Filter, Aggregate, Calculate (for complex expressions), Window Functions, Pivot/Unpivot, Union, and data cleansing operations.
• SQL and Python Scripting for Extensibility: For custom logic or operations not covered by standard components, Matillion allows users to embed custom SQL scripts directly into workflows via its “SQL Script” component. It also offers a “Python Script” component for more complex tasks, data validation, or integration with external services.
• Built-in Orchestration and Scheduling: Matillion includes features for orchestrating complex sequences of data loading and transformation jobs, managing dependencies, and scheduling executions based on time or events.
• Environment Management: Facilitates managing different environments (e.g., development, staging, production) with distinct configurations and variables.

Pros

• Leverages the high performance and scalability of target cloud data warehouses for transformation tasks.
• Reduces data movement by transforming data in place within the data warehouse.
• Integrates with Git for version control and collaboration.

Con

• Matillion is great for batch-loading transformation, but not for ultra-low latency, where event-by-event streaming is directly into it.
• Mastering complex transformations, advanced Query DSL within Matillion, or intricate pipeline orchestration can require a lot of technical acumen.

Why Choose Matillion over dbt?

Matillion offers a key advantage over dbt for teams who prefer a visual, low-code approach to building and managing ELT transformations directly within their cloud data warehouse, without necessarily diving deep into SQL or Jinja. If your organization values a graphical interface for designing complex data flows that are then translated into optimized, native SQL for your specific warehouse (Snowflake, Redshift, BigQuery, etc.), and wants built-in scheduling and orchestration tailored for this visual ELT paradigm, Matillion provides a more intuitive and potentially faster development experience for less code-centric users.

Pricing

Matillion organizes its pricing around four tiers. The first tier is for individuals, and they also typically operate on a pay-as-you-go model. For businesses that need higher abilities, the pricing for the following tiers starts at roughly $1,000 a month. For large enterprises, Matillion offers customized solutions so that pricing can be negotiated with their team.

Factors to Consider When Choosing a dbt Alternative?

1. How Does Your Team Like to Work? – Code or No-code

Does your team prefer the old-school way of writing code, or do they want a slick, drag-and-drop interface where everything’s visual? A bit of both?

How will they use the tool day-to-day? Is there a friendly GUI? A robust API for all your automators? Or a command line interface for those who love to script?

Look out for the learning curve. A tool that clicks with your team’s current skills means less groaning and more doing.

2. What Kind of Data transformations Are You Doing? – Complex and Real-time capabilities

Are we talking straightforward cleanups, or are your transformations complex?

How much data are you dealing with? Make sure your chosen tool can keep up without breaking a sweat.

Got data that needs transforming right now as it streams in? Look out for tools catering to real-time transformations on the fly.

3. Keeping it Running Smoothly – Maintenance and Automation

The goal is to save effort, right? Not to create a new time-sucking monster.

How much fiddling does it take to get things set up and running? You want smart automation that gets you going quickly.

Think about the daily grind. How much babysitting will this tool need? The less, the better!

How well does it handle hiccups? Good error messages, easy recovery, and alerts are what you are looking for here.

4. Integration and Flexibility with your other tools

Does it easily connect to where your data lives now (databases, warehouses) and where it needs to go?

Got complex data pipelines with lots of moving parts? See if the tool can manage that whole show itself?

Where Will It Live? Cloud? On your own servers? Make sure it fits your company’s infrastructure and security rules.

5. Teamwork Makes the Dream Work – Version control and collaboration

Can multiple people work on stuff at the same time without chaos? Like Git-style version control and ways to review each other’s work.

Big Company Needs? If you’re in a larger org, you’ll care about:

• Who Gets to Do What (RBAC): Controlling permissions is a must.
• Keeping an Eye on Things (Audit Logs): Essential for security and compliance.
• Making Sure It’s Governed: Does it help with data discovery, cataloging, and keeping data policies in check?

6. Does the Price Tag Make Sense?

Look More Than Just the Sticker Price. Don’t forget the hidden costs, setup time, team training, ongoing TLC, and any extra bits you need to buy.

Does this tool solve a real problem and improve your data life enough to justify the cost and effort?

Why Hevo?

If you’re looking for a dbt alternative that tightly bundles powerful transformations into a seamless ELT pipeline, Hevo Data stands out. Imagine handling both data ingestion and transformation within one intuitive platform. 

Hevo offers impressive flexibility: you can write custom Python scripts for complex, nuanced logic directly within your data flows. If you prefer a more visual approach or need to empower less code-savvy team members, Hevo’s drag-and-drop transformation blocks let you quickly build out data shaping and cleaning steps. And for those comfortable with SQL, you can still define SQL-based models in your warehouse. 

What’s particularly compelling about Hevo is its ability to perform in-flight transformations before data even lands in your destination, which is perfect for quick cleanups, data type casting, or PII masking on the fly. With this combination of Python, a visual interface, and SQL migrated and scheduled across Hevo’s complete end-to-end platform, you can prepare data for analysis quickly and without switching between multiple tools. If you value speed, accessibility, and an integrated approach to your data stack, Hevo is a strong candidate for your data pipeline needs.

FAQ on DBT Alternative