Apache Airflow vs. Astro by Astronomer vs. HCL DataConnect

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

The choice to use Actian DataConnect really comes down to pricing and the type of integration project you are performing, specifically with the requirements, whether you want the integration to be on-premise or in the cloud, can you install agents on your servers etc. For integration tools it runs head to head with its competitors.

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

• Map multiple source and destination tables - what would be done in many steps in SSIS can be done in one or two "maps" in Data Integrator.
• Platform independent, cloud based, no client software.
• Conversion of data types, write flat files - this helps us with integrating systems.
• Fairly straightforward to use if the user has ETL experience.

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

• Integration Manager needs better scheduling improvements. Like dependencies, and not running a process again if the prior run is still running.
• New connector development does not seem a priority.

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

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

Data Integrator was initially a part of a package deal for us with our new Student Information System. It was chosen prior to my arrival at the institution. Since I discovered we have SQL Server licenses, we have decided to use SSIS instead since I have a background in the MSBI stack and SSIS appears to be more straightforward (process flow philosophy vs "map" philosophy.)

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

• Our customer likes the consistent reliable integrations we have built so we [got] a definite return on investment.
• Problems are fairly easy to handle and rework.

Incentivized