Rivery

Build complete data workflows from extracting data to loading and transforming it, all within one platform for analytics or AI use cases.

Offers flexibility for users of all technical levels, allowing both visual, drag-and-drop pipeline creation and advanced coding in SQL or Python.

Utilizes generative AI to accelerate and simplify the process of constructing data pipelines.

Easily pulls data from any application or database (200+ native connectors) and loads it into your data lake or cloud data warehouse, supporting managed API and Change Data Capture (CDC) replication.

Converts raw data into valuable business models using SQL or Python, with options for automated workflows and pre-built data model kits.

Manages the entire data flow with powerful control mechanisms like conditional logic, containers, loops, and advanced scheduling to handle complex dependencies.

Pushes processed and enriched data directly back into operational systems like CRMs, Slack, or other service APIs to drive real-time actions.

Provides tools to scale and manage data operations efficiently, offering clear visibility into pipeline activity, consumption, and seamless deployment across environments with version control.

Instantaneously connects to a vast array of applications, databases, and data warehouses, with fully managed and regularly updated connectors.

Allows creation of custom API connectors to pull data from virtually any source, and Rivery's R&D department can build specific connectors for a fee.

Offers reliable and fast replication of data from databases to your data warehouse, ensuring data is always up-to-date.

Natively supports Python for transformations, enabling advanced scripting and data manipulation directly within your data flow without extra infrastructure.

Automates the entire data integration process, including complex data transformations, to streamline operations.

Allows running multi-step SQL transformations directly within your cloud data warehouse.

Provides advanced orchestration capabilities to handle complex and dynamic data flow requirements.

Enables separate development, staging, and production environments for organized and safe deployment of pipelines.

Offers precise control over deployments between environments, including built-in dependencies and environment variables.

Manages all pipeline logic and transformations, allowing easy rollback to previous versions.

Provides a unified dashboard for an overview of data flow and detailed event logs for monitoring and troubleshooting.

Proactively tracks the health and performance of data pipelines and sends real-time notifications for anomalies or issues.

Integrates SQL-based data checks to ensure the accuracy and reliability of your data.

Connects with popular tools like dbt, Airflow, and Tableau for a more comprehensive data stack.

Empowers data analysts and engineers to build and manage data pipelines autonomously, reducing reliance on central IT teams.

Provides production-level workflow templates with predefined data models, pipelines, transformations, and orchestration logic for rapid deployment.

Enables remote execution, editing, deployment, and management of data pipelines for integration with existing CI/CD processes.

Automatically creates target schemas and manages incremental data loading, simplifying data warehousing.