The Best Data Orchestration Tools for Omnichannel Brands [2023]


Data orchestration is a fundamental piece of the ELT + analytics puzzle that consumer brands are missing. 

Managing the flow of data between various systems and applications is the sine qua non of any omnichannel strategy, as it ensures that data is accurate, consistent, and accessible. In short, a data strategy can’t exist on any scale without data orchestration.

Data orchestration tools do the heavy lifting and enable data teams to build workflows suited to their needs.

In this piece, we’ll review some of the best tools on the market to manage data orchestration: Airflow, Astronomer, Prefect, Dagster, Shipyard, and Daasty.

Apache Airflow

Overview: Apache Airflow is an open-source platform that enables teams to create, schedule, and monitor data workflows and ETL/ELT processes. 

Created by AirBnB in 2014, Airflow is widely considered the original tool for authoring and orchestrating big data workflows. It remains quite popular, and one recent poll showed 41.6% of data teams using Airflow for their data pipelines. 

Customers: All sizes from mid-market to enterprise, including Adobe, Airbnb, Slack, and Walmart.

  • Many blue-chip tech companies have invested in Apache Airflow to support its growth, including Google with its Cloud Composer GCP service.
Source: Showing sample DAG organizational UI. 

Strengths: Airflow is open-source and Python-based, and because it has been around for years, it has the backing of a supportive developer community. If you‘re facing a data engineering issue, it’s likely that someone else has already solved it and shared their solution online.

Notes on usability: Airflow’s UI is considered less intuitive than newer data orchestration tools, and it’s missing competitive features, like versioning and event-based scheduling. Airflow has a steep learning curve for new users, and the technical limitations can make it tricky to manage data workflows effectively.

Integrations: Airflow connects to AWS, Azure, and GCP Cloud and has over 140 integrations, with more added each month.


Overview: Astro is an orchestration service that enables engineers to manage their Airflow instance(s). It was released by Astronomer, which is active in the development of Airflow, and its goal is to provide a “next-generation” experience for data teams using Airflow.

As an end-to-end solution, Astro offers a wide range of capabilities, from supporting legacy ETL/ELT workflows to facilitating seamless interoperability between cloud and on-prem resources.

Customers: Many big names, including Sonos, Conde Nast, and Credit Suisse.

Strengths: Astro enables teams to run data orchestration through a CLI, and/or in a Cloud IDE via’s UI. 

Source: showing dependency builder in Astro's Cloud IDE

Notable features include worker auto-scaling, the ability to run multiple clusters on AWS, GCP, or Azure, and role-based access control (RBAC).

Integrations: As an orchestration tool running on Airflow, Astro adds enhanced integrations with hundreds of third-party data services and infrastructure tools. 


Overview: Prefect is an open-source data orchestration platform designed for ML engineers. It streamlines the process of building, testing, and deploying complex data pipelines, allowing ML engineers to focus on their core tasks, such as developing and training models.

Customers: Companies of all sizes, including smaller and midmarket companies.

Source: Showing workflows in Prefect UI

Strengths: Prefect provides a unified interface for managing data pipelines across different systems and tools. It supports scheduling, error handling, and retry logic. These capabilities make it easier for ML engineers to automate and manage the flow of data between their systems, without having to make major changes to their existing infrastructure. 

Integrations: Prefect integrates seamlessly with a range of popular tools and technologies, including AWS, Google Cloud, and Kubernetes.


Overview: Dagster is an open-source data orchestration platform that enables data engineers to manage complex data pipelines. 

It was developed by the data engineering team at Elementl to overcome the limitations of existing orchestration tools. Dagster's software-defined assets feature transforms DAGs into interconnected assets with dependencies, allowing users to focus on defining the assets and their relationships while Dagster handles the underlying operations and execution of DAGs.

Customers: A few of Dagster’s notable customers include DoorDash, GoPuff, and Drizzly.

Source: Showing log of events in Dagster UI

Strengths: Dagster enables data engineers to manage their pipelines in a modular and scalable way. It plugs into a range of data sources and destinations, including databases, files, and cloud storage services, and has a range of features for pipeline management and error handling. 

What stands out about Dagster is its focus on data lineage and provenance, tracking the data from the source to its final destination to help engineers understand the data’s journey. 

Integrations: Dagster supports many different data sources and sinks, including databases, files, and cloud storage services.


Overview: Shipyard provides a unified platform for defining, executing and monitoring data workflows, making it easier for data engineers to manage the end-to-end data processing lifecycle. 

Customers: A couple of Shipyard’s notable customers include ClickFunnels and Joybird Furniture.​​

Source: Showing dependency builder in Shipyard GUI

Strengths: Shipyard is one of the newest data orchestration tools, and it offers powerful solutions on the market for launching, monitoring, and sharing data workflows.

Shipyard offers a number of features like pre-designed low-code templates, a sleek workflow builder via their GUI, and real-time diagnosing to enable teams to build workflows more efficiently.

Integrations: Shipyard offers dozens of integrations, including cloud storage solutions, ETL/ELT tools, and databases like Snowflake.


Overview: Daasity is an ELT + analytics platform that exclusively supports omnichannel consumer brands, whether they’re doing millions or billions in revenue. 

Daasity provides customizable event-driven data orchestration through the complete data pipeline. Data teams can leverage an out-of-the-box orchestration setup, or they can fully build their own automated event-driven workflows, leveraging their own GitHub repo:

Customers: Daasity powers ELT and analytics for over 1,600 brands, including multi-billion dollar brands with large data teams and custom data needs.

Strengths: Daasity offers purpose-built event-driven data orchestration for consumer brands.

Besides building their own workflows, data teams can customize Daasity’s robust transformation code as often as they need to, and they can do so in a safe development environment via a one-click-build test data warehouse.

Teams can monitor progress of overall daily (and hourly) data refreshes:

And investigate metrics and performance around any part of the ELT, including on individual integrations:

Integrations: Daasity connects with 60+ tools that consumer brands use in their daily operations.

Looking for a Data Orchestration Tool?

Consumer brands often have significant data needs that need to be effectively managed and integrated. Data orchestration helps businesses to handle the increased complexity and scale of their data environments and make better use of their data to drive business growth. 

While there are many data orchestration tools on the market, Daasity is uniquely positioned as a complete data platform to support fast-growing consumer brands with their data orchestration. 

Get in touch with a data specialist from Daasity to discuss your data orchestration needs.

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