# Dataddo - Complete Reference > Enterprise data movement control plane for cloud, on-prem, and hybrid environments. The AI data backbone for bidirectional data flows - moving data into AI systems and routing AI-generated outputs back to operational tools, with the governance, SLA, and security an enterprise expects. --- ## Company Overview Dataddo operates data movement as managed infrastructure. A single Control Plane orchestrates Data Planes that run where the data lives - in the customer's cloud, on-premises, or both - so regulated and hybrid environments can use a managed service without data leaving the perimeter. Founded in 2019, headquartered in Prague, Czech Republic. Used by enterprises in regulated industries (banking, insurance, healthcare, public sector) as well as digital-native companies at scale. Key differentiators: - Control plane / data plane architecture - management runs in the cloud; data movement executes inside the customer's environment when required - Hybrid and on-prem deployment supported natively - sensitive workloads never traverse public infrastructure - 400+ actively maintained connectors; Dataddo owns the connector contract end-to-end (API changes, schema drift, and maintenance are Dataddo's responsibility, not the customer's) - All transport patterns in one platform: ETL, ELT, CDC, streaming, Reverse ETL, batch file delivery, zero-copy Apache Arrow - Four interfaces, one governance layer: UI, REST API, CLI, and MCP server - No row-tax pricing - row count is not the primary pricing metric - SOC 2 Type II certified, ISO 27001 certified, GDPR aligned, PCI DSS compliant ## Architecture: Single Control Plane, Multiple Data Planes - Manage the entire data integration operation from a single Control Plane while running Data Planes wherever the data lives - cloud, on-premises, or both. - One Control Plane can orchestrate multiple Data Plane deployments simultaneously: centralized governance without sacrificing architectural flexibility. - Sensitive data can be processed exclusively within a private, on-premises Data Plane, while less sensitive workloads run in the cloud - all under a single management interface. - Data Planes deploy on all major hyperscalers (AWS, Azure, GCP), regional and local cloud providers, and on-premises container platforms including Kubernetes, Red Hat OpenShift, and VMware Tanzu. - Connection patterns supported across cloud and hybrid: cloud-to-cloud, on-prem-to-cloud, cloud-to-on-prem, and on-prem-to-on-prem. ## All Data Transport Types One platform, every movement pattern - no separate tool just because the delivery pattern changed: - ETL & ELT - classic extract-transform-load, or load-first with in-warehouse transformation - Change Data Capture (CDC) - real-time, low-latency replication that tracks row-level changes as they happen - Data Streaming - continuous, event-driven pipelines for time-sensitive and AI-ready workloads - Reverse ETL - push curated warehouse data back into business tools, CRMs, and operational systems - Batch File Delivery - structured delivery of datasets to S3, SFTP, or any storage target - Zero-Copy with Apache Arrow - high-performance, in-memory data sharing without serialization overhead, built for AI and analytics pipelines ## Four Interfaces, One Governance Layer Dataddo is fully operable through four interfaces, so each kind of operator works the way they work best - and every action available in the UI is available headlessly via API or CLI: - UI - web interface for business teams to build and monitor pipelines without filing tickets to engineering - REST API - programmatic control and automation; embed Dataddo into internal developer portals or existing orchestration tooling - CLI - for engineers in the terminal: scripting, CI/CD, GitOps, and mass deployment of thousands of pipelines - MCP server - native protocol for AI agents (Claude, ChatGPT, Cursor, LangChain, and any MCP-compatible framework) Pipeline configuration can be version-controlled as YAML, templated, and deployed as code - suitable for internal developer portals, multi-tenant managed-service offerings, and config-as-code workflows. ## We Own the Data Contract, Not Just the Connector Building a pipeline is day one; keeping it running is every day after. Source APIs change, schemas evolve, vendors deprecate endpoints. With Dataddo that is Dataddo's problem, not the customer's. - Connector ownership end to end - Dataddo engineers design, build, and continuously maintain every connector; when an upstream source changes its API, auth, or data model, Dataddo updates the connector and restores the pipeline. - Custom connector development - if a needed source or destination isn't covered, Dataddo builds and maintains it under the same ownership model. - Proactive pipeline monitoring - continuous health monitoring detects anomalies, delivery delays, and data-quality deviations before they surface downstream. - Schema drift detection - structural changes are detected automatically and handled by configurable rules (propagate, alert, or quarantine). - Full observability - run histories, payload inspection, error drill-down, and end-to-end lineage. ## AI Data Backbone Dataddo is the governed data movement and write-back layer between enterprise data sources and AI infrastructure. It does not train or host models. - Always-fresh context for RAG and LLM grounding - keep vector databases, knowledge bases, and AI context stores continuously updated via CDC and streaming pipelines. - Reliable feature pipelines for ML - consistent population of feature stores with built-in monitoring against silent failures and data drift. - Close the loop with Reverse ETL - push AI-generated scores, predictions, and insights back into CRMs, ERPs, and operational systems where they drive decisions. - AI governance - built-in PII detection, data masking, and lineage so AI systems meet the same compliance standards as the rest of the data infrastructure. - Stack-agnostic by design - adopt new model providers, vector databases, or AI platforms, or run multiple models in parallel, without rebuilding pipelines. - MCP server for native AI agent access (Claude, ChatGPT, Cursor, LangChain). ## Products ### Data Anywhere Connect any source to any destination. ETL, ELT, Reverse ETL, CDC, and database replication in one platform, via data flows that run on a schedule or event trigger. ### Dashboards Connect data directly to dashboarding tools like Looker Studio, Tableau, and Power BI - with or without a data warehouse in between. ### Headless Data Integration Build data products on the unified Dataddo REST API and CLI - all integrations behind one programmatic interface, fully operable as config-as-code. ## Use Cases ### Powering Analytics Automate data delivery to BI tools and dashboards. Eliminates manual CSV exports and fragile spreadsheet pipelines. ### ETL & ELT Extract data from 400+ sources, load to a data warehouse (Snowflake, BigQuery, Redshift, etc.), and optionally transform. Supports both ETL and ELT patterns. ### Reverse ETL Sync processed data from a data warehouse back to operational tools (CRM, marketing platforms, etc.). ### Database Replication Real-time and scheduled replication between databases. Supports PostgreSQL, MySQL, SQL Server, Oracle, DB2, Informix, and cloud databases. ### Headless Data Products API-first data integration for building custom data products. Full REST API and CLI with the same connector library as the UI. ### Custom Workloads Complex, bespoke data automation scenarios. Custom connectors developed and maintained under Dataddo's connector-ownership model. ## FAQ: Powering Analytics ### What other integration use cases does Dataddo serve? Dataddo is an any-to-any data integration platform that supports ETL/ELT, reverse ETL, database replication, event-based integrations, and end-to-end integration of online sources with dashboarding apps. It also offers access to a full REST API, so any of Dataddo's data integration functionality can be deployed in a headless scenario. For more details on specific use cases, see our other use case pages: ETL & ELT Reverse ETL Database Replication Connectors for Data Products ### What are the benefits of integrating data with Dataddo? - No more manual CSV uploads. Automate all your data connections. - No more switching between platforms to see data. By syncing data from all your platforms to an analytics tool, you can monitor important cross-platform metrics from a single place. - Analytics-ready data. Dataddo automatically unifies the format of data from your various apps, so that it's ready to analyze by the time it gets to your dashboard. - Long-term scalability. Dataddo is a comprehensive, any-to-any data integration tool designed to meet the needs of any professional or organization that works with data—from solo marketers to global enterprises. This means you can start by using it to send data from online services to analytics tools and then, when your organization is ready, use it to centralize data in a warehouse, replicate data between databases, or send data from warehouses into business apps like CRMs and marketing automation platforms. A central screen for managing all data connections, multiple users per account, and multi-tenant deployment makes it easy for various departments and teams to adopt Dataddo for their own use cases. - Predictable pricing. Always know what you're paying and never get a bad surprise at the end of the month. Since Dataddo's pricing is based on number of data connections (i.e., flows) costs do not escalate with data volume, extraction frequency, or number of data sources. - Proactive pipeline monitoring and maintenance. Our engineers proactively monitor connections and manage all API changes behind the curtain. This means you don't have to worry about your connections breaking in the middle of the night. - Inbuilt data quality tools. Dataddo automatically unifies the formats of data it sends to dashboarding tools, so your data will always be ready to analyze. We also enable rule-based monitoring and data quality checks, to help you prevent inaccuracies and errors in any data you transfer with Dataddo. - Connects any A to any B. Never worry that you might be "stuck" with a tool that can't connect to one of your services. In addition to offering a massive connector portfolio of apps and databases, we build new connectors for clients all the time. ### How does Dataddo compare to Supermetrics? See our Supermetrics comparison page for the full breakdown. ### Where can I learn more about Dataddo's SOC 2 Type II certificate? You can find our full SOC 2 Type II certificate on our data security page . ### How is Dataddo priced for app-to-dashboard use cases? Dataddo's pricing is based on number of flows. 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 Google BigQuery (destination), this will count as one flow. This pricing model allows for clear budgeting, as costs do not escalate with data volume, extraction frequency, or number of data sources. ## FAQ: Etl Elt ### What other integration use cases does Dataddo serve? In addition to ETL and ELT, Dataddo supports reverse ETL, database replication, event-based integrations, and end-to-end integration of online sources with dashboarding apps. It also offers access to a full REST API, so any of Dataddo's data integration functionality can be deployed in a headless scenario. See our other use case pages: Operational analytics (reverse ETL) Database replication Powering analytics (SaaS apps to BI tools) Headless Data Integration for data products ### How does Dataddo solve data quality problems for ETL and ELT workloads? Dataddo offers a number of mechanisms for tackling data quality problems. These include, but are not limited to: The Data Quality Firewall (rule-based, configurable per column) Detailed monitoring and notifications Format harmonization (minimizes downstream processing costs and ensures that data from disparate sources is analytics-ready) Data blending/union The ability to exclude personal identifiable information (PII) from extractions See our documentation for more information about how Dataddo approaches data quality. ### Is Dataddo deployable within the Google Cloud Platform/AWS/Microsoft Azure ecosystems? Absolutely. Dataddo subscriptions can be managed through accounts with any of the three major cloud providers. See our: GCP marketplace page AWS marketplace page Azure marketplace page ### Where can I learn more about Dataddo's SOC 2 Type II certificate? See Dataddo's SOC 2 Type II certificate . ### How is Dataddo priced for ETL/ELT use cases? Dataddo's pricing is based on number of flows. 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 Google BigQuery (destination), this will count as one flow. This pricing model allows for clear budgeting, as costs do not escalate with data volume, extraction frequency, or number of data sources. ## FAQ: Reverse Etl ### What other integration use cases does Dataddo serve? In addition to reverse ETL, Dataddo supports ETL and ELT, database replication, event-based integrations, and end-to-end integration of online sources with dashboarding apps. It also offers access to a full REST API, so any of Dataddo's data integration functionality can be deployed in a headless scenario. For more details on specific use cases, see our other use case pages: ETL & ELT Database Replication Powering Analytics (SaaS apps to dashboarding tools) Headless data integration for data products ### How does Dataddo solve data quality problems for reverse ETL workloads? Dataddo offers a number of embedded mechanisms for tackling data quality problems. These include, but are not limited to: The Data Quality Firewall (rule-based, configurable per column) Flexible write modes Easy data mapping Detailed monitoring and notifications Detailed flow logs Syncs every 5 mins See our documentation for more information about how Dataddo approaches data quality. ### What are the benefits of reverse ETL? Generally speaking, reverse ETL gives business teams like sales and marketing insights from information that only organizations know about themselves. To illustrate, let's look at CRMs. CRMs collect a lot of customer data. Payment amounts, support tickets, acquisition information—the data's there. But it's not all there. Companies have their own specific way of calculating certain metrics, in particular metrics based on first-party data, and it's difficult for CRMs to display these without heavy—and costly—customizations. Imagine your company sells software as a service, and that customers with three or more user accounts tend to have a lower risk of churn. There is no way your CRM could know this straight out of the box. But, if you have the right customer data in your data warehouse, your engineers can run computations there, then send the risk scores back into your CRM via a reverse ETL tool, making them visible to your sales and support teams alongside all other customer data. With little to no effort, these teams will then be able to identify who is at risk of churn. This is why reverse ETL is often referred to as the "last mile" of the modern data stack—it enables organizations to display any information in any business app. ### Is Dataddo deployable within the Google Cloud Platform/AWS/Microsoft Azure ecosystems? Absolutely. Dataddo subscriptions can be managed through accounts with any of the three major cloud providers. See our: GCP marketplace page AWS marketplace page Azure marketplace page ### Where can I learn more about Dataddo's SOC 2 Type II certificate? Consult the Dataddo SOC 2 Type II Certificate . ### How is Dataddo priced for reverse ETL use cases? Dataddo's pricing is based on number of flows. 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 Google BigQuery (destination), this will count as one flow. This pricing model allows for clear budgeting, as costs do not escalate with data volume, extraction frequency, or number of data sources. ## FAQ: Database Replication ### What other integration use cases does Dataddo serve? In addition to database replication, Dataddo supports ETL and ELT, reverse ETL, event-based integrations, and end-to-end integration of online sources with dashboarding apps. It also offers access to a full REST API, so any of Dataddo's data integration functionality can be deployed in a headless scenario. For more details on specific use cases, see our other use case pages: ETL & ELT Reverse ETL (Operational Analytics) Powering Analytics (SaaS apps to dashboarding tools) Headless data integration for data products ### How does Dataddo solve data quality problems for database replication workloads? Dataddo offers a number of embedded mechanisms for tackling data quality problems. These include, but are not limited to: The Data Quality Firewall (rule-based, configurable per column) Truncate Insert write mode Detailed monitoring and notifications Detailed flow logs Automatic data type conversion (minimizes downstream processing costs and ensures that data from disparate sources or unstructured data is analytics-ready) See our documentation for more information about how Dataddo approaches data quality. ### What are the benefits of database replication? Generally speaking, database replication helps keep data accessible across locations and platforms for the whole organization. More specifically, it is used for: Analytics. Replicating data from a production database to a data warehouse, i.e. a safe sandbox for analytics teams. Data migration. Simplifies the data migration processes during system upgrades or transitions to new database platforms. Disaster recovery. Redundancy and backup solutions ensure data availability and reliability in case of system failures or disasters. Data consistency. Keeps data consistent across databases and other systems, preventing discrepancies and ensuring uniformity. Performance optimization. Distributes data load across multiple databases, improving read performance and reducing latency. Improving scalability. Distributing data across multiple servers or cloud environments enables systems to scale better. Automated real-time data sync & updates. Keeps data automatically updated and synchronized in real-time and between systems, reducing manual efforts and errors. ### Is Dataddo deployable within the Google Cloud Platform/AWS/Microsoft Azure ecosystems? Absolutely. Dataddo subscriptions can be managed through accounts with any of the three major cloud providers. See our: GCP marketplace page AWS marketplace page Azure marketplace page ### Where can I learn more about Dataddo's SOC 2 Type II certificate? Consult the Dataddo SOC 2 Type II Certificate . ### How is Dataddo priced for database replication use cases? Dataddo's pricing is based on number of flows. 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 Google BigQuery (destination), this will count as one flow. This pricing model allows for clear budgeting, as costs do not escalate with data volume, extraction frequency, or number of data sources. ## FAQ: Headless Data Products ### Who should consider using Dataddo's Headless Data Integration? Any organization building a data product whose main focus is to generate insights from data; for example, CDPs or data analytics platforms—these need data from various sources to work properly, but their main functionality is analytics. By connecting to the unified Dataddo API, such organizations can put all of Dataddo's integration functionality under the hood of their product, and focus instead on developing the product's insight-generation functionality. Doing this will: Shorten time to market Improve market adaptability Save money and engineering resources ### What data integration use cases does Dataddo serve? Dataddo supports ETL, ELT, reverse ETL, database replication, event-based integrations, and end-to-end integration of online sources with dashboarding apps. It also offers access to a full REST API, so any of Dataddo's data integration functionality can be deployed in a headless scenario. For more details on specific use cases, see our other use case pages: ETL & ELT Reverse ETL (Operational Analytics) Database Replication Powering Analytics (SaaS apps to dashboarding tools) ### How does Dataddo solve data quality problems? Dataddo offers a number of mechanisms for tackling data quality problems. These include, but are not limited to: The Data Quality Firewall (rule-based, configurable per column) Detailed monitoring and notifications Format harmonization (minimizes downstream processing costs and ensures that data from disparate sources is analytics-ready) Data blending/union The ability to exclude personal identifiable information (PII) from extractions See our documentation for more information about how Dataddo approaches data quality. ### Is Dataddo deployable within the Google Cloud Platform/AWS/Microsoft Azure ecosystems? Absolutely. Dataddo subscriptions can be managed through accounts with any of the three major cloud providers. See our: GCP marketplace page AWS marketplace page Azure marketplace page ### Where can I learn more about Dataddo's SOC 2 Type II certificate? Consult the Dataddo SOC 2 Type II Certificate . ### How is Dataddo priced for Headless use cases? Dataddo's pricing for Headless Data Integration is calculated individually for every client, and is based on integration units. These units take into account rows and actions per month, per connector. The number of units a client needs is determined in advance, which allows for clear budgeting, as costs will not fluctuate unpredictably from month to month. ## FAQ: Headless Custom Workloads ### Who should consider using Dataddo's Headless Data Integration? Any organization that wants to go beyond our user interface to achieve more control over data integrations — for example, to automate repetitive jobs like historical data loads, or to implement client-specific configurations. Doing this via the Dataddo API allows you to: Automate any complex or repetitive integration workload Implement custom authentication and authorization flows Subscribe and manage Dataddo via AWS, Azure, or GCP marketplaces ### What data integration use cases does Dataddo serve? Dataddo supports ETL, ELT, reverse ETL, database replication, event-based integrations, and end-to-end integration of online sources with dashboarding apps. It also offers access to a full REST API, so any of Dataddo's data integration functionality can be deployed in a headless scenario. For more details on specific use cases, see our other use case pages: ETL & ELT Reverse ETL (Operational Analytics) Database Replication Powering Analytics (SaaS apps to dashboarding tools) ### How does Dataddo solve data quality problems? Dataddo offers a number of mechanisms for tackling data quality problems. These include, but are not limited to: The Data Quality Firewall (rule-based, configurable per column) Detailed monitoring and notifications Format harmonization (minimizes downstream processing costs and ensures that data from disparate sources is analytics-ready) Data blending/union The ability to exclude personal identifiable information (PII) from extractions See our documentation for more information about how Dataddo approaches data quality. ### Is Dataddo deployable within the Google Cloud Platform/AWS/Microsoft Azure ecosystems? Absolutely. Dataddo subscriptions can be managed through accounts with any of the three major cloud providers. See our: GCP marketplace page AWS marketplace page Azure marketplace page ### Where can I learn more about Dataddo's SOC 2 Type II certificate? Consult the Dataddo SOC 2 Type II Certificate . ### How is Dataddo priced for Headless use cases? Dataddo's pricing for Headless Data Integration is calculated individually for every client, and is based on integration units. These units take into account rows and actions per month, per connector. The number of units a client needs is determined in advance, which allows for clear budgeting, as costs will not fluctuate unpredictably from month to month. ## Pricing Plans are based on the number of active data flows (the connection between a data source or sources and a destination), not metered by rows. Free trial available without a credit card. Plans: - Free - $0/month, entry tier for evaluation - Data to Dashboards - direct source-to-dashboard delivery - Data Anywhere - any source to any destination - Enterprise - custom flows, rows, sync frequency, and deployment (cloud, hybrid, or on-prem) All paid plans include the full connector and destination catalog. See https://www.dataddo.com/pricing for current plan details. ## Supported Connectors (sample) Sources: Salesforce, HubSpot, Pipedrive, Google Analytics 4, Facebook Ads, Instagram Ads, LinkedIn Ads, Google Ads, Microsoft Ads, Shopify, WooCommerce, Stripe, Recurly, Zendesk, Freshdesk, Jira, GitHub, Snowflake, BigQuery, PostgreSQL, MySQL, SQL Server, Oracle, DB2, Informix, MongoDB, and 400+ more. Destinations: Snowflake, Google BigQuery, Amazon Redshift, Databricks, Microsoft Fabric, PostgreSQL, MySQL, Google Sheets, Looker Studio, Amazon S3, Azure Blob Storage, vector databases, and more. First-class support for open table formats: Apache Iceberg, Delta Lake, Apache Hudi. Full list: https://www.dataddo.com/connectors ## Security & Compliance - SOC 2 Type II certified - ISO 27001 certified - GDPR aligned - PCI DSS compliant - Network isolation - sensitive data can be processed in a fully isolated Data Plane in the customer's private cloud or on-premises environment, never traversing public infrastructure - End-to-end encryption in transit and at rest; bring your own keys via AWS KMS, Azure Key Vault, or HSM - PII detection, masking, and tokenization at ingestion - SSO (SAML 2.0, OIDC), role-based access control (RBAC), immutable audit logging - Recognized by Gartner: Gartner Cool Vendor; Honorable Mention in the 2024 Gartner Magic Quadrant for Data Integration Tools - Rated on G2 (4.8), Capterra (4.5), GetApp (4.5), Gartner Peer Insights ## Case Studies New enterprise case studies (L'Oréal / Fortune 100 FMCG anonymized, VIG, Growth Digital, Publicis) are in progress and will be added when published. The two below are on the approved enterprise list: - Wärtsilä (industrial manufacturing): legacy industrial data modernization - extraction from legacy industrial systems into modern analytical infrastructure without big-bang replatforming - Livesport (real-time sports data): high-volume, low-latency data movement for real-time sports analytics and product features Full list: https://www.dataddo.com/case-studies