Daasity vs. Fivetran: a Comparison [2022]


Every week, we talk to dozens of omnichannel brands looking to implement data and analytics solutions, and many of them mention encountering both Daasity and Fivetran in their research. 

This article will endeavor to provide a fair comparison on the differences between Daasity and Fivetran, ranging from a high-level overview to a point-by-point comparison.

Note: We will update this article as new features from both Daasity and Fivetran roll out.

Platform Overviews


Fivetran is an EL (Extract, Load) provider that offers transformation functionality through both basic SQL transformations as well as transformation packages via dbt Core. It connects to around 200 data sources, which cover a variety of different business use cases, and it loads data into a number of destinations, including Redshift, BigQuery, Azure, Databricks, and Snowflake. 

Fivetran is a common choice for the EL portion of the Modern Data Stack, which generally includes an EL tool and tools/platforms for data transformation, data warehousing, data orchestration, BI, and reverse ETL. The Modern Data Stack may also include other components, such as a CDP (customer data platform).

Many multi-million and multi-billion dollar organizations, such as DocuSign, Lufthansa, and Square, use Fivetran in their data pipelines.


Daasity is a Modular Data Platform, built for omni-channel consumer brands. Brands can use some or all of Daasity’s ELT, pre-built (and customizable) analytics dashboards for BI tools, data orchestration, and reverse ETL, all of which are purpose-built for analysis of eCommerce, Amazon, retail, and wholesale data. 

When leveraged in part, brands can use Daasity as an extractor-only solution, for ELT, or in other combinations. When linked to a preferred BI tool (e.g., Looker or Tableau), Daasity partially or fully replaces the Modern Data Stack, depending on the brand’s preference. 

Daasity connects to around 60 data sources, supports Snowflake, BigQuery, and Redshift as data warehouse options, and can push customer data to several marketing tools/channels, such as Klaviyo and TikTok.

Daasity supports the data and analytics of multi-million and multi-billion dollar consumer brands, including Manscaped, Vuori, Snowe, and American Giant.

Further Comparison and Relevance to Omnichannel Consumer Brands

Omnichannel Data Extraction

Daasity + eCommerce data: Daasity extracts Shopify, Magento, and Salesforce Commerce Cloud data.

  • Daasity will support BigCommerce in early Q1 2023.

Fivetran + eCommerce data: Fivetran extracts Shopify, BigCommerce, and Salesforce Commerce Cloud data. It can extract Magento data via MySQL and MySQL Amazon RDS. 

Daasity + Amazon data: Daasity extracts Amazon Seller Central and Amazon Ads data.

  • Daasity has a robust Amazon Seller Central integration that allows brands to analyze nearly all their Amazon Seller Central data. Daasity extracts dozens of reports for all Amazon regions through the SP-API, including orders reports, settlement reports, traffic reports, and FBA inventory reports.

Fivetran + Amazon data: Fivetran does not connect to Amazon Seller Central or Amazon Ads.

Daasity + retail data: Daasity extracts retail/POS data from tools such as KWI and Newstore. 

Fivetran + retail data: Fivetran does not connect to retail/POS tools.

Data Models

Daasity’s Data Models

  • Normalization schemas: Daasity has data models built to normalize disparate data and facilitate centralization and analytics of omnichannel business data (e.g., Unified Order Schema): eCommerce, Amazon, retail, and wholesale data.
  • Data reporting schema: Daasity uses data marts (stored in a single schema) to build a visualization layer so that any user (including non-Analysts and non-technical users) can build a report in their BI tool.


  • Daasity’s transform code can be modified based on a merchant’s needs.
  • Daasity’s data models allow brands to make tech stack changes without interruption to metrics/data consistency, to “household” customers, and more.
  • For a full discussion of Daasity's data models, you can read our technical documentation.

Fivetran’s Data Models

  • Fivetran has individual “dbt Core-compatible data models for [its] top connectors,” as well as certain normalization schemas, such as for ad reporting. These require both a Snowflake, BigQuery, or Redshift destination as well as a dbt project.
  • Fivetran offers a “Shopify Holistic Reporting” package, which allows brands to combine Shopify data with Klaviyo marketing data. This can be loaded into BigQuery, Snowflake, Redshift, Postgres, and Databricks.

Data Orchestration

Data orchestration in Daasity: Daasity automatically provides complete out-of-the-box data orchestration based on a merchant’s integrations, ensuring that data transformation runs once extraction is complete. No data orchestration tool, such as Airflow, Prefect, or dbt Cloud, is needed.

Merchants can fully customize their data orchestration within the Daasity platform, and they can run multiple workflows. 

Data orchestration in Fivetran: Fivetran requires a separate tool in order to manage data orchestration, such as Airflow, Prefect, or Dagster

Technical Resources Required

Technical resources required for Daasity: Daasity can be managed with zero technical resources or a full data team. The specifics depend on a brand’s preferences and how they intend to use Daasity. Here are a few (out of many) examples:

  • A brand can use Daasity as an extractor-only tool, and it may have a data team to manage its entire ELT pipeline and analytics setup. In this case, Daasity may function as part of the data stack and can be used to extract data from certain key sources, such as Shopify Plus, Amazon, and Netsuite. 
  • A brand can use Daasity in its entirety and refer any customization/dev work to Daasity’s dev resources. 
  • A brand can use Daasity in its entirety and do most or all customization/dev work in-house.

Note: Daasity fully manages and maintains all APIs.

Technical resources required for Fivetran: Fivetran manages and automates EL processes, and its pre-built transformations allow for some reduced dev work in certain situations. However, Fivetran is usually an element of an in-house data solution in which a dedicated data resource or team is required. 

  • Based on our research, experience, and conversations with omnichannel merchants, brands looking to leverage Fivetran as their EL provider can expect 6-18 months of development work to bring a complete data solution to fruition. The brand will require multiple custom-built data connectors, significant dev work on data transformation and normalization (even with help from pre-built dbt Core packages), and time to build the rest of the pipeline.

Final Notes

We encourage omnichannel merchants to thoroughly explore data solutions and incorporate all key stakeholders in the decision. That said, if you’re interested in learning more about how Daasity can be a part of (or all of) your data solution, we’d love to show you more about how we can help.

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