Consumer brands looking to implement in-house data + analytics solutions often encounter Daasity, Fivetran, and Stitch in their research for data extractors.

We’ve talked about Daasity vs. Fivetran in a previous article, and in this article, we’ll provide a fair comparison between Daasity and Stitch. 

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

Platform Overviews

Stitch 

Stitch is an EL tool that offers both “Stitch-certified” extractors as well as “community-supported” extractors, both using the Singer framework. Singer is an open-source EL developed by Stitch that has two kinds of scripts: Singer taps (i.e., extractors) and Singer targets (i.e., loaders). 

Stitch does not offer any transformation functionality. It must be connected with a transformation tool, such as dbt or Stitch’s parent company, Talend. 

Stitch connects to about 80 data sources that are Stitch-certified (out of about 140 total data sources total) and 10 data destinations, including Redshift, Databricks, BigQuery, and Snowflake. It is another common choice for the EL portion of the Modern Data Stack.

Many well-known companies, such as Peloton, InVision, and Indiegogo use Stitch in their data pipelines.

Daasity

Daasity is a Modular Data Platform. Omnichannel consumer 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, 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 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.‍

‍Stitch + eCommerce data: Stitch extracts data from Shopify and Magento via Stitch-certified EL and BigCommerce via community-supported EL. Stitch does not connect to Salesforce Commerce Cloud.

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.

Stitch + Amazon data: Stitch 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. 

Stitch + retail data: Stitch 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: eCommerce, Amazon, retail, and wholesale data. See Unified Order Schema for more. 
• 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.

Notes: 

• Daasity’s transform code can be fully customized 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.

Stitch's Data Models: 
As an EL tool, Stitch has no data models/transform functionality. Unlike Fivetran, which offers “dbt Core-compatible data models for [its] top connectors (e.g., for Shopify),” Stitch does not currently have a formal dbt partnership. 

Brands must build data models from scratch via dbt, via Talend, or another tool. 

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.

Daasity merchants can leverage our Custom Workflows feature. This allows them to create their own workflows by choosing the integrations, transformation scripts, and schedule(s) they want to run. Merchants can test their workflows from the UI and see the current status of any workflow.

Data orchestration in Stitch: Out of the box, Stitch has error handling with notifications, it logs and monitors extractions and loads, and brands can choose their replication frequency.

Stitch offers more robust data orchestration functionality in its advanced and enterprise tiers: “smart” cache refreshes, multiple destinations, advanced scheduling (i.e., “granular start times”), notification extensibility (e.g., push error notifications to Slack), API key management, and post-load webhooks.

Technical Resources Required

Technical experience 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. 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 customization/dev work to Daasity’s dev resources (from Daasity or our partners). 
• A brand can use Daasity in its entirety and do most or all customization/dev work in-house.

Note: Daasity manages and maintains all APIs; merchants do not spend development resources on API work.

Technical experience required for Stitch: Stitch manages and automates EL processes. Modifications may be necessary for Singer taps, which will require a data engineer, likely a Python engineer. 

Connecting Stitch to data sources and a destination(s) is a simple process. Raw data can be piped into a data warehouse within a week. However, if an omnichannel brand is looking to build a full in-house data stack (one that includes Stitch) with a data team, the entire process will likely require 6-18 months of development work.

• The brand will likely require multiple custom-built extractors (e.g., for Amazon Seller Central).
• The brand will need to put significant work into data transformation and normalization. This will take up the bulk of the development time. 

Final Thoughts

We encourage all merchants to thoroughly explore options before opting for one data solution over another. 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.