What is Astronomer and what are its top alternatives?
Astronomer is a platform designed to help companies build, run, and scale data pipelines. It provides features such as workflow management, scheduling, monitoring, and error handling. However, some limitations of Astronomer include its pricing structure based on data volume and the need for technical expertise to fully leverage its capabilities.
Apache Airflow: Apache Airflow is an open-source platform for orchestrating complex computational workflows and data processing pipelines. It offers a rich set of features including a user-friendly UI, scheduling, monitoring, and extensibility through plugins. Pros include its active community support and flexibility in customizing workflows, while cons may include a steeper learning curve for beginners.
Prefect: Prefect is a workflow management system that focuses on building, monitoring, and managing data pipelines. It offers features like automatic retries, parallelism, and scheduling. Pros include its user-friendly interface and strong support for advanced scheduling, while cons may include a smaller community compared to other tools.
Luigi: Luigi is a Python module that helps you build complex pipelines of batch jobs. It provides tools for handling dependencies, scheduling tasks, and monitoring workflows. Pros include its simplicity and integration with Python code, while cons may include a lack of a graphical user interface.
Dagster: Dagster is a data orchestrator that integrates with Python and enables building complex data pipelines with a focus on data quality and monitoring. It offers features like dependency management, data types, and declarative pipeline definitions. Pros include its emphasis on data quality and testing, while cons may include a more structured approach that may require additional setup time.
Kubeflow Pipelines: Kubeflow Pipelines is a platform for building and deploying machine learning workflows on Kubernetes. It provides features like versioning, reusable components, and collaboration tools. Pros include its seamless integration with Kubernetes for scalable deployments, while cons may include a more specialized focus on machine learning workflows.
Pinball: Pinball is a job scheduling and workflow management system built at Pinterest. It offers features like distributed execution, dependency management, and fault tolerance. Pros include its scalability and fault tolerance, while cons may include a lack of comprehensive documentation compared to other tools.
Apache Nifi: Apache Nifi is a data integration and distribution system that enables the automation of data flows between various systems. It provides a visual interface for designing data flows, data provenance, and scalability. Pros include its visual data flow programming paradigm, while cons may include a more limited focus on complex workflow orchestration compared to other tools.
Conductor: Conductor is an orchestration engine developed by Netflix to manage workflow execution across microservices. It offers features like parallel execution, load balancing, and monitoring. Pros include its focus on microservices orchestration, while cons may include a learning curve associated with its specific use case.
Digdag: Digdag is a simple tool for building and orchestrating complex workflows. It provides features like dependencies, retries, and alerting. Pros include its simplicity and ease of use, while cons may include a less extensive feature set compared to other tools.
Apache Kafka Streams: Apache Kafka Streams is a client library for building real-time streaming applications on top of Apache Kafka. It offers features like stateful processing, windowing, and fault tolerance. Pros include its seamless integration with Kafka for stream processing, while cons may include a more specialized focus on stream processing rather than general-purpose workflow orchestration.
Top Alternatives to Astronomer
- Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...
- Segment
Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch. ...
- Google Tag Manager
Tag Manager gives you the ability to add and update your own tags for conversion tracking, site analytics, remarketing, and more. There are nearly endless ways to track user behavior across your sites and apps, and the intuitive design lets you change tags whenever you want. ...
- Rudderstack
RudderStack allows you to easily build pipelines connecting your whole customer data stack, then make them smarter by pulling analysis from your data warehouse to trigger enrichment and activation in customer tools. ...
- Dagster
It is an orchestrator that's designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports. ...
- Avo
A code-generated, type-safe tracking library to accurately implement analytics events that are defined and maintained in a single-source-of-truth web app. Built to optimize the experience of maintaining and version controlling complicated event schemas. ...
- Alation
The leader in collaborative data cataloging, it empowers analysts & information stewards to search, query & collaborate for fast and accurate insights. ...
- Freshpaint
Codelessly connect your site to your stack. Automate tedious work so engineering can focus on product. It integrates your marketing and analytics tools with one click. ...
Astronomer alternatives & related posts
Airflow
- Features51
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
related Airflow posts
I am working on a project that grabs a set of input data from AWS S3, pre-processes and divvies it up, spins up 10K batch containers to process the divvied data in parallel on AWS Batch, post-aggregates the data, and pushes it to S3.
I already have software patterns from other projects for Airflow + Batch but have not dealt with the scaling factors of 10k parallel tasks. Airflow is nice since I can look at which tasks failed and retry a task after debugging. But dealing with that many tasks on one Airflow EC2 instance seems like a barrier. Another option would be to have one task that kicks off the 10k containers and monitors it from there.
I have no experience with AWS Step Functions but have heard it's AWS's Airflow. There looks to be plenty of patterns online for Step Functions + Batch. Do Step Functions seem like a good path to check out for my use case? Do you get the same insights on failing jobs / ability to retry tasks as you do with Airflow?
I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:
- Trigger Matillion ETL loads
- Trigger Attunity Replication tasks that have downstream ETL loads
- Trigger Golden gate Replication Tasks
- Shell scripts, wrappers, file watchers
- Event-driven schedules
I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise
Segment
- Easy to scale and maintain 3rd party services86
- One API49
- Simple39
- Multiple integrations25
- Cleanest API19
- Easy10
- Free9
- Mixpanel Integration8
- Segment SQL7
- Flexible6
- Google Analytics Integration4
- Salesforce Integration2
- SQL Access2
- Clean Integration with Application2
- Own all your tracking data1
- Quick setup1
- Clearbit integration1
- Beautiful UI1
- Integrates with Apptimize1
- Escort1
- Woopra Integration1
- Not clear which events/options are integration-specific2
- Limitations with integration-specific configurations1
- Client-side events are separated from server-side1
related Segment posts
Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.
We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.
Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).
Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.
Google Tag Manager
related Google Tag Manager posts
Hi,
This is a question for best practice regarding Segment and Google Tag Manager. I would love to use Segment and GTM together when we need to implement a lot of additional tools, such as Amplitude, Appsfyler, or any other engagement tool since we can send event data without additional SDK implementation, etc.
So, my question is, if you use Segment and Google Tag Manager, how did you define what you will push through Segment and what will you push through Google Tag Manager? For example, when implementing a Facebook Pixel or any other 3rd party marketing tag?
From my point of view, implementing marketing pixels should stay in GTM because of the tag/trigger control.
If you are using Segment and GTM together, I would love to learn more about your best practice.
Thanks!