Apache Airflow vs. Astro by Astronomer vs. Salesforce Data 360

Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.

Incentivized

Astronomer is well suited for workflow and dependency management for enterprise-level data lakes. It is not a product for data processing though. Different source systems can be integrated, it also provides powerful interfaces for alerting and monitoring. Easy to build DAGs, graphical UI, API support makes the product more user-friendly as well. Astronomer also does a great job on user training.

Incentivized

Great tool for client management, sales tracking, cases studies. Using is as the source of truth for all client communication. Allowing more integration options for cc and ACH payments. Right now the options are limited and only integrate with strict processing capabilities that are not always in the businesses best interest.

Incentivized

• Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
• Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
• Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.

Incentivized

• Workflow management
• Wide availability of plugins
• Dependency management on upstream

Incentivized

• Allows use to bring external data into Salesforce as zero-copy
• Is able to present data using standard Salesforce reports familiar to our users
• Allows us to establish multiple relationships between our CRM data and the external data to contextualize the imported data.

Incentivized

• UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
• Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
• Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.

Incentivized

• More language agnostic
• Flexible fork and join capabilities
• Near real time UI updates in case of deployment of enhanced DAGs

Incentivized

• pricing or costing visibilty, we can't estimate what would be consumption this month and suddenly we get surprised
• CI/CD can be improved, that looks a difficult and hard
• Also sandbox comes with many limitation so very hard to replicate production like environment to test

Incentivized

For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.

Incentivized

Most of the daily use features, are very usr friendly, intuitive, every end user can learn how to manage and operate basic activities such as enter information, send an email, send a campaign, create an email marketing template, run a report on the renewals for the month, open and close a support case, create new account or a new contact, etc

Incentivized

Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.

Incentivized

Astronomer is a fast, secure, scalable workload management solution. It provides world-class user training along with easy to interact support.

Incentivized

we have evaluated Snowflake, adobe rt-cdp as well. They also offer a very strong capabilities but the Data 360 is more suitable for us because of the same Salesforce ecosystem. The main key factor is without any custom code we were able to include it into our automations. And ofcourse with agentforce already in plans, we had to pick Data 360

Incentivized

• Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
• Donot use it if you have very less usecases

Incentivized

• It helps to build scalable, available and low maintenance workloads
• Integrated Alerts and notifications helps to detect load issues in the early stages
• Ensures meeting data SLAs

Incentivized

• Salesforce Data Cloud has a positive impact on me as a realtor because it is easy to generate reports on customer data and see what is needed to be bumped to the next level. We have to earn a certain commission by the end of the year and it makes it easy to see how close you are
• It has a positive impact on keeping track of customers because it is nice to have it all in one place which is a nice time saver
• I like that you can even see customer info from other agents making it nice to compare and try and pass others
• Makes the customers journey smooth sailing because everything is in one place

Incentivized