--- title: "Core Concepts" slug: "core-concepts" description: "Connect sources & destinations reliably and securely with Dataddo for effortless data integration. Learn the basics & get help setting up your first pipeline." tags: ["Resource", "Quickstart"] updated: 2026-07-12T16:30:26Z published: 2026-07-12T16:30:26Z canonical: "docs.dataddo.com/core-concepts" --- > ## Documentation Index > Fetch the complete documentation index at: https://docs.dataddo.com/llms.txt > Use this file to discover all available pages before exploring further. # Core Concepts In this article, we will guide you through some core basic and advanced concepts that are commonly used in Dataddo. **Basic concepts**: [data source](/docs/core-concepts#what-is-a-data-source), [data destination](/docs/core-concepts#what-is-a-data-destination), and [data flow](/docs/core-concepts#what-is-a-data-flow). **Advanced concepts**: [connectors](/docs/core-concepts#connectors), [authorizers](/docs/core-concepts#authorizers), and [data backfilling](/docs/core-concepts#data-backfilling). ## Basic Concepts There are three **basic** components which allow you to successfully create a data pipeline: [data source](/docs/core-concepts#what-is-a-data-source), [data destination](/docs/core-concepts#what-is-a-data-destination), and [data flow](/docs/core-concepts#what-is-a-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: - SaaS apps (e.g. [Salesforce](/docs/salesforce), [NetSuite](/docs/netsuite), [HubSpot](/docs/hubspot), [Stripe](/docs/stripe), [Klavio](/docs/klaviyo), [Facebook](/docs/facebook-ads), [Google Analytics 4](/docs/google-analytics-4)) - Databases (e.g. [MySQL](/docs/mysql-destination), [Postgres](/docs/postgres), [SQL Server](/docs/universal-sql-server)) - Cloud data warehouses (e.g. [BigQuery](/docs/google-bigquery), [Amazon Redshift](/docs/redshift), [Snowflake](/docs/snowflake)) - File storages (e.g. [Amazon S3](/docs/s3), SFTP). For more information see our [article on ***data sources***](/docs/overview-sources). ### What Is a Data Destination? A ***data destination*** refers to the endpoint to which data from your ***sources*** will be delivered. ***Destinations*** include: - Dashboarding applications (e.g. [Databox](/docs/databox), [Tableau](/docs/tableau), [PowerBI](/docs/power-bi), [Looker Studio](/docs/looker-studio)) - Databases (e.g. [MySQL](/docs/mysql-destination), [Postgres](/docs/postgres), [SQL Server](/docs/universal-sql-server)) - Cloud data warehouses (e.g. [BigQuery](/docs/google-bigquery), [Amazon Redshift](/docs/redshift), [Snowflake](/docs/snowflake)) - File storages (e.g. [Amazon S3](/docs/s3), SFTP) - Applications (e.g. [HubSpot](/docs/hubspot-destination), Salesforce, NetSuite, Zoho CRM) **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](/docs/data-blending), and [data union](/docs/data-union)), we recommend using a data warehouse solution. For more information see our [article on data destinations](/docs/overview-destinations). ### What Is a Data Flow? A ***data flow*** in Dataddo represents the connection between a ***data source*** (or multiple sources) and a ***destination***. For example, transferring data from Facebook Ads (source) to Looker Studio (destination). **Key considerations**: - **One Dataset, One Flow**: For fixed-schema connectors like HubSpot Analytics, each dataset must have its own flow. This means that if you need to extract multiple datasets, such as contacts and deals, you’ll need a separate flow for each. - **[Multi-Account Extraction](/docs/multi-account-extraction)**: If multiple accounts share the same schema (i.e., identical attributes and metrics), their data can be combined into **a single flow using [data union](/docs/data-union)**. For example, data from multiple Google Analytics accounts can be consolidated and extracted together. Dataddo’s unique architecture **decouples data extraction from data delivery**, allowing flexible and scalable configurations. This means a single Salesforce [data extraction](/docs/core-concepts#what-is-a-data-source) can be routed to multiple [destinations](/docs/core-concepts#what-is-a-data-destination), such as different data warehouses or BI tools, enabling efficient use of resources. For more information see our [article on data flows](/docs/overview-flows) or refer to the following articles for a more comprehensive overview of use cases that are possible in Dataddo: - [Simple Data Integration to Dashboards](/docs/simple-data-integration-to-dashboards) - [Batch Ingestion to Data Warehouses](/docs/ingestion-to-data-warehouses) - [Batch Ingestion to Data Lakes](/docs/batch-ingestion-to-data-lakes) - [Database Replication](/docs/database-replication) ## Advanced Concepts In this section, you will find the terms [connectors](/docs/core-concepts#connectors), [authorizers](/docs/core-concepts#authorizers), and [data backfilling](/docs/core-concepts#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](/docs/connectors). ### Authorizers Similarly to **[connectors](/docs/core-concepts#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](/docs/authorized-services) 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](https://cdn.document360.io/084ed225-3f99-4644-a2da-39ca0cd5ef45/Images/Documentation/Core%20Concepts.png) ### 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: - [Data Backfilling to Data Storages](/docs/data-backfilling-to-storages) - [Data Backfilling to Dashboarding Apps](/docs/data-backfilling-to-dashboarding-apps)