Dataddo vs. Airbyte
Dataddo and Airbyte are both market-proven data integration tools, and which one is right for your use case will depend on a range of factors.
This comparison will help you make an informed decision based on your specific requirements.
No credit card required
|No. of data flows
A data flow is the connection between a source and a destination. For example, a connection between Facebook Ads and Looker Studio would count as one flow.
|Dashboards as destinations
|Data warehouses as destinations
|Apps as destinations (reverse ETL)
|Open-source (basic features)
Closed-source (premium features)
|Time to add new connectors
|Must be coded by user
|Platform does transformations?
Dataddo automatically cleanses and harmonizes data before sending it to a destination. This makes it machine-readable, immediately analyzable in dashboards, and easier for engineers to manipulate in data warehouses.
|In-transit & at-rest
|In-transit, depending on connector
|Data warehouse required?
Though Dataddo can connect to any data warehouse, it also allows users to store data in its embedded SmartCache.
|Community-based (free) & direct (for an additional fee)
Airbyte’s pricing is based on credits, which are used to “pay” for rows or GBs synced. This could be beneficial for small businesses or startups moving lower volumes of data, but keep in mind it can lead to variable costs due to unpredictable data volumes.
Dataddo’s pricing is flow-based. What is a data flow? The connection between a source and a destination, e.g., Facebook Ads to BigQuery, would count as one flow. This means your bill will be the same every month (unless you decide to scale up or down, which can be done easily).
Both vendors offer a limited free plan.
Airbyte offers a wide portfolio of connectors, but if the one you need isn’t on their list, you'll need to build it yourself. Airbyte does offer no- and low-code quick connector builder tools for DIY situations, but these still require technical expertise to use.
Dataddo also offers a wide portfolio of connectors. If the one you need isn’t on our list and you’re a paid client, we'll create it for you in about two weeks, free of charge.
Airbyte supports ELT operations and database replication. It does not support reverse ETL operations, or direct end-to-end integration of online services with dashboards.
Dataddo also supports ELT and database replication, but, in addition, it supports ETL, reverse ETL, and direct end-to-end integration of online sources with dashboarding apps. The latter enables non-technical users to create their own pipelines for ultra quick insights.
Data Transformation/Customization Capabilities
Airbyte does not have any native transformation functionality, but it integrates closely with dbt—a data transformation platform for data engineers.
Dataddo offers a blend of pre-built and custom transformation capabilities. It provides an easy-to-use, no-code interface where users can define custom transformations, significantly reducing reliance on SQL. This makes the platform more inclusive for teams with varying technical proficiencies. Dataddo users can still use dbt as a layer on top of their warehouse, so there is no “forced” system of transformations. Also, because Dataddo pre-cleans any data it extracts, it saves on warehouse fees.
If you really want to fully customize your business logic, both Airbyte and Dataddo offer “headless” products.
Ease of Use
Airbyte is designed for use by data engineers and data scientists. Its functionality therefore requires a certain level of technical proficiency, and using the tool involves a learning curve, especially for users unfamiliar with data integration and SQL. Technically oriented teams or those with the resources to invest in learning the platform will be pleased with the depth of capabilities and granularity of control it offers.
Dataddo places a strong emphasis on user experience, aiming to combine power and simplicity. It offers an intuitive, no-code user interface designed to minimize the learning curve for non-technical users and save work for engineers, making it accessible to a wide range of users. It offers visual aids for data transformations and straightforward methods for setting up and managing data flows. This is good for teams with a mix of technical skills or for businesses aiming to democratize data usage across different departments.
Inbuilt Data Quality Mechanisms
Airbyte’s approach to data quality is centered around connector testing and reporting. Airbyte subjects all connectors to a set of standard integration tests, and keeps detailed logs, which users can refer to in case of a problem. Any further testing or data quality mechanisms must be implemented by the user. In general, Airbyte relies on user initiative to monitor alerts and take corrective actions.
Dataddo takes a multi-layered approach to data quality. Among other mechanisms, it features a data quality firewall, which lets users set rule-based integration triggers that prevent suspicious datasets from flowing downstream. It also offers inbuilt transformation capability to ensure data integrity, as well as a notification system and troubleshooting toolbox to help users quickly resolve issues. The connector and flow logs give users a complete overview of all activity within the tool. Dataddo’s engineers also proactively monitor and maintain pipelines independently of user initiative, minimizing the need for user intervention.
Data Warehouse: Necessary or Not?
Airbyte specializes in the extraction of data to warehouses, so you have to have a data warehouse in order to use it.
Dataddo specializes in the same, but, in addition, it allows you to skip the warehouse and extract data straight to dashboarding apps, which is useful for business teams that need quick insights. In case your business doesn’t have a warehouse, you can use Dataddo’s embedded SmartCache to collect data from periods past.
Both Airbyte and Dataddo are certified and/or compliant with SOC 2 Type 2, ISO 27001, and other global and regional standards for data privacy and security.
Pay as You Grow
What is a data flow?
A data flow is the connection between a data source or sources and a destination. For example, if you send data from Facebook Ads (source) to Looker Studio (destination), this will count as one flow.
Supported integration types
How Do You Want to Move Your Data?
Data from Apps » Dashboarding AppsSend data from cloud services straight to dashboarding apps or Google Sheets.
Data from Apps » Data WarehousesMove data from cloud services to storages to establish one source of truth for all decisions.
Data from Warehouses » AppsSend data back into apps to give business teams insights in the systems they use most.
Database ReplicationConnect storages to replicate, migrate, and distribute data throughout your organization.