Effective data governance requires incorporating preventive measures within data orchestration layers.
Current data governance tools predominantly offer post-action analytics rather than proactive preventive measures.
By integrating role-based access control and monitoring in the orchestration layer, organizations can shift to a more proactive data governance approach.
Data Teams still prefer classic open source tools over workflow orchestration functionality on Data and AI platforms.
The Data Orchestration category might be fading as orchestration becomes embedded in other platforms and pricing becomes a concern.
A robust system of control and management for data and AI pipelines is vital, encompassing aspects like alerting, lineage, metadata, infrastructure, and multi-tenancy support.
Orchestra serves as a comprehensive Data Control Panel, bridging orchestration and observability. It offers a Control Panel for Data Teams that stands out from other tools focused solely on orchestration or observability.
Orchestra integrates Git-control with a user-friendly interface and advanced scheduler functionalities, setting itself apart from open-source tools. It provides more granularity in monitoring and failure insights.
Orchestra focuses on providing a unified platform for data orchestration, observability, and operations, standing out by offering full observability, end-to-end asset-based lineage, powerful UI, hosted infrastructure, fixed pricing, and out-of-the-box integrations.
Data orchestration is often confused with workflow orchestration, but it involves more than just triggering and monitoring tasks; it includes reliably and efficiently moving data into production.
Reliably and efficiently releasing data into production is complex and involves elements like data movement, transformation, environment management, role-based access control, and data observability.
Implementing end-to-end and holistic data orchestration offers transformative benefits such as intelligent metadata gathering, data lineage, environment management, data product enablement, and cross-functional collaboration for scalable data operations.
Understanding the pricing of data orchestration tools is crucial for managing costs efficiently in data pipelines.
Consider the trade-offs between self-hosted open-source options like Airflow, Prefect, Dagster, Mage, and managed services like MWAA, Cloud Composer, Astronomer, Prefect Cloud, and Dagster Cloud.
Orchestra offers fixed pricing based on the number of pipelines and tasks, providing certainty in costs, potential savings, and efficiency gains for data teams.
Data Mesh is a decentralized approach to enterprise data management, focusing on distributed datasets and data ownership within domains.
DBT Mesh is a set of features that allow multiple teams to work on dbt projects with less friction, enabling separate repositories and orchestration capabilities.
Having separate dbt jobs run across projects on a schedule is limited, requiring external workflow orchestration tools for more flexibility.
Understanding the total cost of ownership is crucial when choosing between open-source and managed data architectures.
Leveraging open-source software can offer cost benefits, but it also comes with risks like lack of support and high maintenance requirements.
Using managed data architecture tools like Rivery and Orchestra can minimize total cost of ownership, provide scalability, and offer simplicity in maintaining data operations.
ETLP paradigm integrates Airbyte with dbt and Orchestra for quick end-to-end data pipelines without coding.
Using a fully managed deployment approach with tools like Airbyte, dbt, and Orchestra can save time and effort compared to self-managed solutions.
For a data product with 10GB data, costs for Airbyte, dbt, and Orchestra would be around $2400 monthly, potentially more cost-effective than hosting or developer time.