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    Core Concepts
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    Core Concepts

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    Article summary

    In this article, we will guide you through some core basic and advanced concepts that are commonly used in Dataddo.

    Basic concepts: data source, data destination, and data flow.

    Advanced concepts: connectors, authorizers, and data backfilling.

    Basic Concepts

    There are three basic components which allow you to successfully create a data pipeline: data source, data destination, and data flow.

    What Is a Data Source?

    A data source is a collection of data from an authorized service that's been connected via a Dataddo connector. Data within the source is automatically refreshed based on the source's configuration.

    Data can be extracted from any third-party services as:

    For more information see our article on data sources.

    What Is a Data Destination?

    A data destination refers to the endpoint to which data from your sources will be delivered. Destinations include:

    Since Dataddo supports direct connection to dashboards, do I even need a data warehouse?

    Dataddo offers an embedded storage called SmartCache, which is designed to provide a simple solution for situations where large data storage volumes or complex data transformations are not required.

    However, SmartCache is not intended to replace a data warehouse. As a rule of thumb, if you need to store more than 100,000 rows per data source, or if you require complex data transformations beyond simple joins (e.g data blending, and data union), we recommend using a data warehouse solution.

    For more information see our article on data destinations.

    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.

    In Dataddo, data extraction and data delivery operations are decoupled. In practice this means that you can define loose associations between data sources and data destinations. For example, you can route single extraction of Salesforce data to multiple data warehouses.

    For more information see our article on data flows or refer to the following articles for a more comprehensive overview of use cases that are possible in Dataddo:

    Advanced Concepts

    In this section, you will find the terms connectors, authorizers, and data backfilling. Although all of these are still very commonly used in Dataddo, you most likely are able to use them without even actively knowing about it.

    Connectors

    The use of connectors is implicit as you encounter it as soon as you start using Dataddo without even actively noticing.

    Connectors allow Dataddo to extract data from your services. When you configure a connector, you define what data you want to extract and as such you create a data source in Dataddo.

    There are three types of connectors available: universal connectors, fixed-schema connectors, and custom-schema connectors. For more information, see our article on types of connectors.

    Authorizers

    Similarly to connectors, you may encounter the use of authorizers without even actively noticing.

    Authorizers represent any authentication and authorization data that are used to connect to a source or destination. You can re-use a single authorizer for multiple sources or destinations. See our article on authorizers for more information.

    Relationship Between Core Dataddo Components

    Now that we know what core Dataddo components are, we can take a look at how they are used.

    • Firstly, a connector instantiates a data source.
    • Simultaneously, an authorizer bears the credentials for access to synchronize data in a data source or write data to a data destination.
    • Finally, a data flow connects everything as it gives you the flexibility to create M:N assocations between data sources and data destinations.

    Core Concepts

    Data Backfilling

    Data backfilling is used to load historical data from your sources to your destinations. This is particularly important for aligning your data pipeline with broader data management strategies.

    For more detailed explanation and a how-to guide, check the following articles:

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