58 Integrations with Orchestra
View a list of Orchestra integrations and software that integrates with Orchestra below. Compare the best Orchestra integrations as well as features, ratings, user reviews, and pricing of software that integrates with Orchestra. Here are the current Orchestra integrations in 2026:
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1
Prefect
Prefect
Prefect is a workflow orchestration and automation platform designed for the modern context-driven era. It enables teams to turn Python functions into production-ready workflows with minimal effort. Prefect provides open-source foundations alongside managed platforms for enterprise-scale automation. The platform supports building and orchestrating data pipelines, workflows, and AI applications with full observability. Prefect Cloud offers managed orchestration with autoscaling, enterprise authentication, and built-in governance. Prefect Horizon extends automation to AI infrastructure by enabling deployment of MCP servers for AI agents. Trusted by leading organizations, Prefect helps teams scale automation without operational complexity. -
2
Coalesce
Coalesce.io
Building and managing a fully documented data project takes a lot of time and manual coding. Not anymore. When we say we can help you transform data more efficiently, we can prove it. Column-aware architecture enables reusable data patterns and change management at scale. Bring visibility to change management and impact analysis for safer and more predictable data ops. Coalesce provides curated packages with best-practice templates to automatically generate native-SQL for Snowflake™. Have a unique need? No worries, templates are fully customizable. Navigating your data pipeline is easy in Coalesce. Each screen and button is designed to provide access to everything you need. Your data team has more control over every project, from comparing code side-by-side to instantly seeing project and audit history. Table-level and column-level lineage is automatically provided and always up-to-date. -
3
Meltano
Meltano
Meltano provides the ultimate flexibility in deployment options. Own your data stack, end to end. Ever growing connector library of 300+ connectors have been running in production for years. Run workflows in isolated environments, execute end-to-end tests, and version control everything. Open source gives you the power to build your ideal data stack. Define your entire project as code and collaborate confidently with your team. The Meltano CLI enables you to rapidly create your project, making it easy to start replicating data. Meltano is designed to be the best way to run dbt to manage your transformations. Your entire data stack is defined in your project, making it simple to deploy it to production. Validate your changes in development before moving to CI, and in staging before moving to production. -
4
Estuary Flow
Estuary
Estuary Flow is a new kind of DataOps platform that empowers engineering teams to build real-time, data-intensive applications at scale with minimal friction. This platform unifies a team’s databases, pub/sub systems, and SaaS around their data, without requiring new investments in infrastructure or development.Starting Price: $200/month -
5
Precog
Precog
Precog is a cutting-edge data integration and transformation platform designed to empower businesses to effortlessly access, prepare, and analyze data from any source. With its no-code interface and powerful automation, Precog simplifies the process of connecting to diverse data sources, transforming raw data into actionable insights without requiring technical expertise. It supports seamless integration with popular analytics tools, enabling users to make data-driven decisions faster. By eliminating complexity and offering unparalleled flexibility, Precog helps organizations unlock the full potential of their data, streamlining workflows and driving innovation across teams and industries. -
6
Google Cloud Dataflow
Google
Unified stream and batch data processing that's serverless, fast, and cost-effective. Fully managed data processing service. Automated provisioning and management of processing resources. Horizontal autoscaling of worker resources to maximize resource utilization. OSS community-driven innovation with Apache Beam SDK. Reliable and consistent exactly-once processing. Streaming data analytics with speed. Dataflow enables fast, simplified streaming data pipeline development with lower data latency. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Dataflow automates provisioning and management of processing resources to minimize latency and maximize utilization. -
7
CData Sync
CData Software
CData Sync is a universal data pipeline that delivers automated continuous replication between hundreds of SaaS applications & cloud data sources and any major database or data warehouse, on-premise or in the cloud. Replicate data from hundreds of cloud data sources to popular database destinations, such as SQL Server, Redshift, S3, Snowflake, BigQuery, and more. Configuring replication is easy: login, select the data tables to replicate, and select a replication interval. Done. CData Sync extracts data iteratively, causing minimal impact on operational systems by only querying and updating data that has been added or changed since the last update. CData Sync offers the utmost flexibility across full and partial replication scenarios and ensures that critical data is stored safely in your database of choice. Download a 30-day free trial of the Sync application or request more information at www.cdata.com/sync -
8
Apache Airflow
The Apache Software Foundation
Airflow is a platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity. Airflow pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically. Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment. Airflow pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine. No more command-line or XML black-magic! Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks. This allows you to maintain full flexibility when building your workflows.