Orchestra Turnkey solution for AI-powered workflows and data quality monitoring

What is Orchestra?

Orchestra offers a comprehensive platform designed to manage AI and data workflows effectively on platforms such as Snowflake, Databricks, and BigQuery, as well as major cloud providers like AWS, Azure, and GCP. It employs a declarative framework accessible to both engineers and business users, simplifying the creation and maintenance of data pipelines. The system emphasizes proactive monitoring, ensuring users are promptly alerted to pipeline failures and data quality issues, thereby building trust with stakeholders.

The platform facilitates scaling AI and data capabilities beyond specialized teams by providing intuitive workflow management tools with necessary guardrails and monitoring, reducing bottlenecks and overhead. It supports building workflows through either code or a user-friendly interface and integrates seamlessly with a wide range of data tools. This approach aims to replace complex manual processes and debugging with reliable, manageable AI and data workflows, improving efficiency and allowing teams to focus on higher-value tasks.

Features

AI-Powered Workflows: Build and manage AI-driven data processes.
Declarative Data Orchestration: Define and control data pipelines using a straightforward framework.
Data Observability & Quality Monitoring: Proactively track pipeline health and data integrity.
Asset-based Data Lineage: Visualize data flows and dependencies across your stack.
Extensive Integrations: Connects with over 100 data tools like Fivetran, dbt core™, Snowflake, BigQuery.
Code or UI Workflow Building: Offers flexibility for both technical and less technical users.
Serverless Architecture: Provides managed infrastructure for scalability and predictable costs.
Centralized Metadata Collection: Gathers and enriches metadata for comprehensive insights.
Role-Based Access Control (RBAC): Manages user permissions for secure collaboration.
Custom Alerting: Set up notifications for pipeline failures and dbt model/test issues.

Use Cases

Managing complex data pipelines across cloud platforms and SaaS tools.
Ensuring data quality and reliability for AI and analytics applications.
Scaling data operations efficiently beyond the central data team.
Monitoring data infrastructure costs and optimizing performance.
Debugging data pipeline failures quickly using lineage and logs.
Orchestrating and monitoring dbt Core™ workflows.
Building end-to-end Data and AI platforms on Snowflake, Databricks, or BigQuery.

FAQs

What kind of pricing model does Orchestra use?

Orchestra offers fixed, predictable pricing based on its serverless architecture, differing from compute-driven models. Plans include Lean (Free), Scale-up (Paid monthly), and Enterprise (Custom Fixed Pricing).
Can Orchestra integrate with my existing data stack?

Yes, Orchestra supports integrations with over 100 data tools, including Fivetran, dbt core™, Snowflake, BigQuery, Databricks, Airbyte, Tableau, Power BI, and various cloud services like AWS Lambda and Azure Data Factory.
Is Orchestra suitable for teams with limited coding expertise?

Yes, Orchestra is designed with an intuitive UI and low-code options for building pipelines, making it accessible even for non-technical users or teams with limited Python/DevOps expertise.
How does Orchestra enhance data pipeline visibility and debugging?

Orchestra provides end-to-end visibility through features like asset-based data lineage and centralized metadata collection, enabling rapid identification and troubleshooting of pipeline failures.
Does Orchestra offer support for dbt Core™?

Yes, Orchestra provides specific support for orchestrating and monitoring dbt Core™ workflows, including alerting on dbt models/tests and allocating compute minutes for dbt runs within its plans.