Fivetran vs dbt: Everything you need to know about this duo

The right choice of tools makes all the difference in ETL or ELT processes in today’s fast and ever-changing data analytics environments. The ever-increasing volume and rising complexity of data are demanding powerful, adaptive solutions for businesses. Fivetran or dbt(Data Build Tool), each have special merits to master data pipelines from seamless extraction and loading to advanced transformations.

In the blog Fivetran vs dbt, features and benefits of these popular tools will be discussed so you can understand which one best fits your data needs—be it smoothening workflows or improving data quality, we shall give insights that will help in making a decision.

Overview of Fivetran

G2 Rating: 4.2(378)

Gartner Rating: 4.5(268)

What is Fivetran?

Fivetran is a fully managed data integration platform for automating data extraction and loading from sources to data warehouses. Its ease of use and robust automation make it one of the most popular tools data professionals use today.

Key Features and Benefits

• Automated data integration: Fivetran handles everything from data extraction to loading with minimum setup.
• Comprehensive Connectors: It supports over 500+ data sources, from databases to cloud applications.
• Real-Time Sync: Keep updating data so that analysis can be conducted at any time.

Pricing Model

Fivetran uses a consumption-based pricing model determined by Monthly Active Rows (MAR), offering a 14-day free trial for new users.

Overview of dbt

G2 Rating: 4.8(154)

Gartner Rating: 4.6(11)

What is dbt (Data Build Tool)?

Data Build Tool (dbt) is an open-source tool designed for transforming raw data within a data warehouse. Unlike traditional ETL tools that handle data extraction, loading, and transformation, dbt focuses on the “Transform” phase of the ELT process. It empowers analysts to perform data transformations after the data has been loaded into the warehouse, making it a powerful ELT solution for managing and optimizing data workflows.

Key Features and Benefits

• SQL-Based Transformations: dbt enables transformations using simple SQL queries easily accessible to any data analyst.
• Version Control Integration: Works perfectly with Git to version control data transformations.
• Modular Design: This is a way of designing programs to reuse code, improving efficiency and maintainability.

Pricing Model

dbt offers free community and paid enterprise editions with additional features and support. 

Critical Differences Between Fivetran and dbt

Criteria	Fivetran	dbt(Data Build Tool)	Hevo
Primary Function	Automated data extraction, loading, and transformation	Transformations and data modeling	Automated data ingestion, transformation, and loading
Key Features	Automated ELT Pipelines, Managed connectors	SQL-based transformations, Model version control	No-code data pipeline, Pre-built connectors, Real-time data syncing
Target Audience	Data engineers, analysts	Data analysts, engineers, data scientists	Data engineers, analysts, business users
Use Cases	Automating data pipelines	Data transformation and analytics	Automating data migration, real-time analytics
Technical Architecture	Cloud-native managed SaaS	CLI tool works with data warehouses	Cloud-based SaaS, no-code platform
Integration Capabilities	Over 500+ pre-built connectors	Works with existing data warehouses and BI tools	150+ pre-built connectors, supports cloud and on-premise sources
Scalability	Scales with cloud resources	Depends on data warehouse scalability	Highly scalable, real-time processing
Pricing	Consumption-based (monthly active rows)	Open-source, with paid Cloud and Enterprise tiers	Consumption-based pricing, 14-day free trial
Performance Model	High performance with low latency	Performance varies based on data warehouse capabilities	Real-time data ingestion and transformation, low-latency pipelines

When to use Fivetran?

• Automated Data Integration: Best for organizations needing fully automated data extraction and loading with the least manual intervention.
• Quick Setup and Low Maintenance: Suitable for companies desiring quick deployment without dealing with infrastructure complexities.
• Diverse data sources: Fivetran provides a wide range of connectors and can be used by companies with diverse data sources.
• Multi-Source Data Integration: Appropriate for companies that must integrate data from numerous and heterogeneous data sources into a central data repository.
• Data Security and Compliance: Fivetran is best when the data security and compliance requirements are stringent. The application provides enterprise-grade security features and certifications.

Advantages and Disadvantages of Fivetran

Advantages	Disadvanatges
Ease of use: User-friendly interface and automation reduce the requirement of technical expertise.	Limited Transformation Capabilities: It is primarily a tool for extraction and loading only. Complex transformations require it to be integrated with other tools like dbt.
Comprehensive Connectors: Over 500+ built in connectors, including major databases and SaaS applications.	Cost: This on-use model can be expensive for an organization with extensive data.

When to use dbt?

• Data Transformation and Modeling: Best for businesses focusing on data transformation and modeling within data warehouses.
• Data Governance and Documentation: Designed for companies that require robust data governance, version control, and data documentation capabilities.
• Custom Transformations: Ideal for those groups that need flexibility while writing custom SQL transformations.
• Data Quality Management: Ideal for teams whose function is to ensure high data quality standards through testing and documentation in the transformation processes.
• Team Collaboration: A good fit for organizations where strong collaboration among data teams happens, as dbt fits neatly into version control systems like Git for teamwork and code reviews.

Advantages and Disadvantages of dbt

Advanatages	Disadvantages
Customizability: It is highly customizable through SQL and with dbt’s templating system.	Requires Technical Expertise: Requires good knowledge of SQL and data warehousing.
Open Source: Free to use, strong community, making it accessible for all sizes of organizations.	No Data Extraction: This is purely a transformation-focused component. Data extraction must be handled separately.

Fivetran and dbt Together

How can Fivetran and dbt complement each other?

• End-to-End Data Pipeline: Fivetran is responsible for extracting and loading data, and dbt handles transformation and modeling. Together, they combine to create a fully-rounded solution for building data pipelines.
• Efficiency and Automation: Fivetran automates Extract and Load processes, and dbt does the transformation and documentation.

Real-World Example and Case Study

Problem Number 1:

• Complex Data Management: SpotOn managed customer transaction data from over 2,000 lines of code and several MySQL databases, which created complex scaling and maintenance issues.
• Ineffective Reporting: Manual QA processes and the lack of version control also meant that the whole process took too long, sometimes 15 hours into the week, and reporting was slow.
• Scalability Issues: The reason behind this is that the engineering team had to write custom code for every new use case because it was not modular in data modeling, so this was a cumbersome process for scalability.
• Central Repository: It was hard for the system to generate the reporting for internal metrics because there was no central repository.

Solution:

• Fivetran and dbt integration: SpotOn integrated Fivetran with dbt (including Fivetran Transformations for dbt Core) to automate and streamline their ELT (Extract, Load, Transform) process.
• Automated Reporting: With Fivetran’s integration to dbt Core, models were run automatically following a load by the Fivetran connector, eliminating custom scripting or additional tools.
• Modular Data Models: dbt Core modularized existing complex transformation code, making it more readable, manageable, and scalable using Jinja templates.

Impact:

• Faster Reporting: With SpotOn, client-facing reporting became quicker, reducing the development time by a number of weeks.
• Efficient Data Management: Manage and automate a company’s entire ELT process from a single platform, improving its scalability and reliability.
• Internal Efficiency: The data engineering team empowered their analytics peers to support high volumes of data from multiple sources by leveraging dbt Cloud’s collaborative UI, scheduling, and alerting.
• Cost and Time Savings: It cut down development time by 5x and facilitated the reporting process internally and with clients with Spot On. All this was done without the loss of data integrity or scaling headcount.

Problem Number 2:

• Inefficient data processing: Red Ventures (RV) sought an efficient method to process client data; the existing process was consuming too much time and resources.
• High Engineering Workload: Setting up integrations and managing data transformations was highly resource-intensive by engineers, which constrained their attention to higher-value tasks.
• Troubleshooting Challenges: Integration and transformation problems require tremendous engineering time to troubleshoot.

Solution:

• Databricks, Fivetran, and dbt Integration: RV leveraged Databricks to scale data engineering pipelines, Fivetran for data ingestion, and dbt for data transformation. Each of these tools helped to support a machine learning pipeline, which could optimize client spending on advertisement.
• Data Democratization: The user-friendly aspects of Fivetran and dbt allowed for a broader set of employees to get involved in data tasks.

Impact:

• Cost Efficiency: Clients experienced up to 30 percent increased cost efficiency in marketing channels due to improved data utilization and AI-driven insights.
• Time Savings: Red Ventures saved 100 hours per typical data integration and reduced data processing time by 80 percent, enabling data engineers to focus on more strategic work.
• Reduced Troubleshooting: Engineering troubleshooting time was cut from 50 percent to less than 20 percent due to consistent data sets provided by Fivetran and clear transformation views offered by dbt.
• Enhanced Client Results: The democratization of data processes and improved efficiency led to better results for clients, helping them more effectively target customers.

Benefits of using both tools in a Data Stack

• Seamless integration—both tools are engineered to work harmoniously, ensuring fluid transitions from data extraction to transformation.
• Scalability: Both tools can grow with increasing data needs, making them well-suited for companies of any size.

Why do Hevo and dbt make a good pair?

Hevo is a powerful data integration platform that simplifies the process of moving data from various sources into your data warehouse. It provides seamless connectivity, robust features, and a user-friendly interface to streamline data ingestion.

While Fivetran and dbt offer robust features, some challenges can arise when using them together:

• Limited Customization with Fivetran:
  • Problem: Fivetran is excellent for automated data ingestion but may not offer extensive customization options for handling unique data formats or complex use cases.
  • Hevo Solution: While Fivetran supports a broad range of data sources, Hevo offers greater flexibility and customization in data ingestion and transformation. This adaptability enables users to tailor the data ingestion process to better fit their specific needs and workflows.
• Pricing Concerns:
  • Problem: Fivetran’s pricing can be high, particularly for extensive data operations or scaling use cases, potentially leading to significant costs.
  • Hevo Solution: Hevo provides competitive pricing that is designed to be cost-effective while offering robust features. Its transparent and scalable pricing model allows businesses to manage their data operations efficiently without incurring excessive costs.

Conclusion

Although Fivetran and dbt are great solutions for data ingestion and transformation, challenges related to limited customizability, and possible high costs are unmistakable. These thus create a challenge to flexibility and efficiency in Data Operations.

Learn about the key differences and use cases of Fivetran and ADF to help you optimize your data integration and transformation workflows.

Hevo addresses these concerns with integration with dbt, more customization options, and a friendly user interface. Hevo is cost-effective—with competitive, very open pricing—and offers an easy way to manage data that will grow with you as you scale. For more flexibility and cost-effectiveness, explore ways Hevo can complement your current data stack and optimize your data workflows.

FAQ Fivetran vs DBT