July 17, 2023

Where Does Fivetran Fit into The Modern Data Stack?

By Sunny Yan

Over the past few decades, the corporate data landscape has changed significantly. The shift from on-premise databases and spreadsheets to the modern era of cloud data warehouses and AI/ LLMs has transformed what businesses can do with data.  

With the rise of cloud data warehouses, processing large volumes of data has become faster and cheaper than ever. In order to fully leverage this vast quantity of collected data, companies need a robust and scalable data infrastructure to manage it. This is where Fivetran and the Modern Data Stack come in.

What is Fivetran?

Fivetran is a fully-automated, zero-maintenance data pipeline tool that automates the ETL process from data sources to your cloud warehouse. It eliminates the need for time-consuming data engineering tasks to maintain the pipeline and allows businesses to spend more time analyzing their data instead of maintaining it.

What is the Modern Data Stack?

The modern data stack refers to the collection of tools and platforms that have been developed to provide scalable and economical solutions to work with large volumes of data. The modern data stack is important because its suite of tools is designed to solve all of the core data challenges companies face. 

In this blog, we will delve deeper into the benefits of using Fivetran, discuss its role in the modern data stack, and shed light on other tools in the modern data stack that pair well with it.

Where Does Fivetran Fit Into the Modern Data Stack?

Modern Data Stack

To understand Fivetran’s role in the Modern Data Stack, it is important to understand what the overall goal of the Modern Data Stack is. The Modern Data Stack is designed to solve most businesses’ data challenges, including cloud data warehouse infrastructure, data replication, transformation, activation, and governance.

Core Data Challenge


Modern Data Stack Tool Recommendation

Cloud Data Warehouse

  • provides data processing and storage capabilities within a cloud-based infrastructure. 

  • Designed to cheaply and efficiently process large quantities of data. 

Snowflake Data Cloud


  • Transferring data from a source system to a cloud data warehouse. (A.K.A- Data Ingestion)



  • Once various data sources are available in the data warehouse, data transformation is needed to create usable datasets.

  • Data modeling, data cleanup, etc.



  • Now that usable datasets have been created, the next step is to create BI Reports and Data Visualizations to analyze the data.

  • We can also create advanced data science models with this data using AI/ Machine Learning.

Data Visualization: Sigma, Streamlit

Data Science:


Fivetran’s Role in the Modern Data Stack

A graphic depicting the Modern Data Stack. There's 4 technology components, Sigma, Snowflake, dbt, and Fivetran

Fivetran plays an important role in the modern data stack. The data ingestion tool is responsible for transporting data from the source system to the cloud data warehouse.

Fivetran facilitates this data movement process with its automated pipelines. With over 160 data connectors, Fivetran supports extracting data from all of the popular data source systems, including Salesforce, Shopify, Google Analytics, and many more.

How Fivetran Differs from Traditional ETL Tools

The traditional method of ingesting data from the source system to the data warehouse can be complex. Data teams generally need to integrate a wide variety of source systems into their data warehouses, with each source having its own specific data extraction method.

This complexity often requires many hours of work from a large data engineering team to build and manually manage data pipelines.

Benefits of Using Fivetran

Automated Data Integration

Fivetran automates the data integration process, and all users have to do is set up their sources and destination, and Fivetran automates the rest, including automated data syncs, built-in data encryption, masking, and automated schema changes.

This reduces the overall effort needed to manage data movement from disparate sources to your organization’s data warehouse. This can save your organization significant time and money compared to manual data integration methods that require a lot of effort to maintain.

Faster time-to-insight and increased data reliability

With fewer moving parts for your team to manage, change management becomes simpler. It allows your teams to tighten their delivery cycles, fix bugs faster, and add additional features more often. This makes the business more agile, improving its ability to accurately monitor and respond to changes in the market using data. 

Additionally, Fivetran supports compliance with data privacy regulations, such as GDPR, CCPA, and HIPPA, with built-in features like data encryption and masking to ensure that sensitive data is protected throughout its lifecycle.

Centralize Many Different Data Sources Into a Single Cloud-Based Target (i.e. Snowflake)

Fivetran supports the ingestion of over 150 data sources, including popular databases, applications, and cloud platforms such as Salesforce, Google Analytics, SQL Server, Snowflake Data Cloud, and more.

It uses high-performance connectors and cloud-based infrastructure to ensure that data is loaded quickly and reliably into your data warehouse, making it easy for organizations to integrate data from multiple sources.

How phData Customers are Leveraging Fivetran

Major Regional Bank Case Study

Business Challenge

A leading regional bank in the Southeast needed to modernize its data infrastructure. They were limited by slow legacy systems and processes and didn’t have a centralized data platform. Because of this, it was hard for them to leverage their data and make data-driven decisions.

Modern Data Stack Solution

Using a combination of Fivetran, DBT, and Snowflake Data Cloud, the phData team was able to create automated data pipelines for data ingestion using Fivetran, transform the data with DBT, and load the data into a Snowflake Cloud Data Warehouse. Click on the link above to learn more!

Major Trucking Company Case Study

Business Challenge

A major U.S. trucking company wanted to leverage its real-time operation data to gain insights to improve decision-making in the organization.

However, the data was stored in legacy on-premise databases, which were challenging to access and use for analysis. To better leverage their data, they decided they needed to migrate to the cloud and the Modern Data Stack.

Modern Data Stack Solution

The company used Fivetran HVR (High-Volume Replicator) for their migration because it provided a flexible and scalable solution to their needs.

After assisting in the setup and configuration of HVR, the phData team collaborated with the client’s database administrator to accelerate the migration of the tables to the Snowflake Data Cloud. Click on the link above to learn more!

Key Takeaways

This is just one of many examples where phData has helped businesses migrate from a legacy data infrastructure to a modern data stack. During our project experiences, we have found that this data stack of Fivetran, DBT, and Snowflake works effectively across a diverse range of business use cases and verticals.


In the evolving landscape of data infrastructure, it has become clear that businesses need to maintain a robust data infrastructure to remain competitive. Many companies that have migrated from legacy data systems to leveraging the modern data stack have had a lot of success with improving their data capabilities. The benefits are significant from increased data reliability, faster time-to-insights, and easier-to-manage data pipelines. 

Fivetran is a critical member of the Modern Data Stack. It enables the other tools in the Stack by quickly and reliably moving your data from source to target destination. With Fivetran, businesses can feel confident that they are always analyzing the most up-to-date data and that the data is accurate.

If you want to learn more about how phData can help your organization migrate from a legacy data infrastructure to Fivetran and the Modern Data Stack, contact us.

Yes, Fivetran is an automated, cloud-based ELT tool that simplifies the process of moving data from the source system to the data warehouse target.

Fivetran differs from legacy ETL (Extract Transform Load) tools which transform the data before loading it because of fear of overloading an on-premise database. With easily scalable cloud data warehouses, this is no longer an issue. 

Doing the transformation after the data load preserves the granularity of the data and makes it possible to perform data transformations within the data warehouse environment using SQL, simplifying the process.

Fivetran significantly simplifies the process of maintaining data pipelines compared to legacy solutions.

With Legacy solutions, each data source system requires a different data extraction method, and a team of data engineers needs to maintain these data pipelines in case anything breaks due to data schema changes or other reasons.

Fivetran automates the creation of data pipelines. All users need to do is set up their source and destination within the tool, and Fivetran takes care of the rest. With automated data syncs and schema changes, data teams can have peace of mind that their data is accurate and easily accessible, allowing them to spend more time analyzing the data for insights instead of just maintaining it.

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