Airflow is a general-purpose, open-source workflow tool that is used as a data orchestration tool to define and coordinate analytics data pipelines. Airflow has some similar objectives as Datameer. Your target is often a cloud data warehouse such as a Snowflake, and it allows you to apply software engineering best practices to process the data. The similarities end there.
For data transformation, Datameer offers an easier, hybrid no-code/SQL-code user experience usable by all your personas – data engineer, analytics engineer, data analyst, and data scientist. The catalog-like data documentation, collaboration, easy data enrichment, deep data profiling, and Google-like search and discovery make Datameer a superior choice for your data transformation needs.
Apache Airflow is an open-source workflow management platform. It allows teams to define, manage, execute, and monitor workflows programmatically. Workflows contain any number of tasks, each of which will connect with various back-end services/systems and execute the task within these services/systems, with Airflow coordinating the end-to-end workflow.
Airflow is very lightweight – it pushes tasks down into underlying services/systems – and uses message queues to gain parallelism and reliability. Airflow is designed to be scalable, dynamic, and extensible. Workflows and tasks can be templated (via Jinja) to facilitate reuse and dynamic execution.
Airflow is also highly general purpose. It connects to almost 100 different services which have a wide variety of applications. Workflow tasks can retrieve data, process data, insert data, and more within these various back-end services.
Besides being available in the open-source package, other companies have embedded and used Airflow as part of their more specialized solutions include:
Users define workflows in Airflow using Python and orchestrate them via Directed Acyclic Graphs (DAGs) of tasks. A workflow will contain any number of these tasks, and tasks may have interdependencies. Connections to outside services/systems that tasks will interact with are defined and managed independently.
DAGs representing the overall workflow is declared and defined via Python. A DAG contains any number of tasks, which are also defined in Python. Each task can interact with a back-end service via the service’s API, also via Python.
Tasks contain relationships and dependencies, which help define both the order of the workflow and parallelism. Message queues manage communication between tasks to ensure the seamless flow of data, status, and other execution information. Message queues also allow failed workflows to be restarted at the point of failure, with the full state restored. Airflow also contains “sensors “that wait for an event before starting or continuing the execution of a workflow or task.
Workflows are run via the Airflow Executor. Jobs can be scheduled or triggered by external events (see sensors above). Pools can be created and tasks assigned to them to define how parallel a job will run. Jobs can be run on compute clusters.
Jinja can be used to template both DAGs and individual tasks. This allows entire DAGs to be parameterized, thus executed in different ways via the parameters, and tasks to be parameterized and reused in multiple DAGs.
Airflow is a general-purpose workflow platform that can be used for any type of job. A common use case for Airflow is to orchestrate data pipelines that include data transformation. For the purpose of this comparison, we will explore using Datameer for orchestrating data transformation workflows versus Airflow.
Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics. With Datameer, you can:
Datameer provides a number of key benefits for your modern data stack and cloud analytics, including:
At the surface level, it might seem obvious that there is no comparison between the Datameer and Airflow. The two offerings focus on different problems and have very different approaches to solving their respective problems.
Airflow is a highly programmatic approach to workflows, including data orchestration workflows. Users require heavy Python experience and strong knowledge of the underlying service APIs which the tasks use.
Yet, some organizations use Airflow to orchestrate data pipelines which will include data transformation, or specifically use Airflow for data transformation. The data transformation tasks will either (a) transform the data directly in Python, or (b) load data into database schemas, transform the data using SQL statements in Python, then reload the data into new schemas – both highly programmatic.
If data architects are coordinating complex dataflows across multiple systems that have transactional properties, Airflow is a good tool and platform. In this use case, data architects will require the precision and control that Airflow offers. Data architects will also have strong Python skills and an understanding of the underlying APIs they use.
For data pipelines and data transformation, Airflow’s complexity and sophistication make it overkill for the use case. Modern ELT data pipelines can easily be defined and managed using a combination of no-code EL tools such as Fivetran and no-code/low-code data transformation tools such as Datameer – without writing ANY code. This allows your broader community of non-programmers in your analytics community to get involved in the analytics engineering process and expand the speed and adoption of your analytics.
|Purpose-built tool and platform for data transformation and modeling||General-purpose workflow and orchestration tool|
|No-code, low-code, and SQL-code interfaces for data modeling and transformation. No Python or Jinja needed||Python and Jinja interfaces requiring strong programming knowledge|
|Abstracts the user from the underlying services and system (Snowflake)||Requires strong understanding of how to use underlying data services and interfaces|
|Schemas and models are automatically carried forward between steps, requiring no coding||Data elements need to be redefined within each task if carried between tasks|
Datameer is explicitly designed and optimized for in-Snowflake data transformations and hits a home run for this use case. For data transformation, Datameer offers many advantages:
Airflow is very good for coordinating complex dataflows across multiple systems that have transactional properties. It gives data architects precision and control and lets them use their strong Python skills and API knowledge. But, for data pipelines and data transformation, Airflow’s complexity and sophistication make it overkill.
Datameer’s explicit focus on in-Snowflake data transformation makes it much more applicable for ELT data pipelines. It offers a much more inclusive and easier user experience that supports multiple personas, collaboration among team members, a much deeper set of searchable, catalog-like data documentation, and transforms directly in Snowflake, using its powerful engine and keeping data and models secure.
Are you interested in seeing Datameer in action? Contact our team to request a personalized product demonstration .
|Data transformation||General-purpose data workflow|
|In cloud data warehouse||Uses the engines of underlying services used within tasks|
|Three distinct UIs for code (SQL), low-code (spreadsheet-like), and no-code (graphical)||Programmatic Interactive Development Environment (IDE)|
|UI/UX that supports all your personas: data engineer, analytics engineer, data analyst, and data scientist||Only supports strong programming personas|
|Easy, no-code data enrichment via a wizard-driven formula builder in the spreadsheet UI||Programmatic via Python|
|Shared workspaces, model reuse, mix-and-match of model types, and shared catalog-like data documentation facilitate collaboration||None|
|Maintains a deep, visual data profile that easily allows users to identify invalid, missing, or outlying fields and values, as well as the overall shape of the data||None|
|A rich set of catalog-like auto-generated and user-created data documentation, including system-level metadata and properties, wiki-style descriptions, custom properties and attributes, tags, and comments||None|
|Google-like faceted search across all information captured on the data, including system-level metadata and properties, descriptions, custom properties and attributes, tags, and comments||None|