ActiveBatch Workload Automation vs. Apache Airflow

Any large business or organisation that wants to manage their workload effectively and with the least amount of room for error might choose the ActiveBatch Automation tool. Being a consultant I feel that It aids in task automation and has the flexibility to change in response to varying company requirements. It helps to save huge time by doing all the repetitive tasks on daily basis. During the patching activity the schedulers can be stopped. It also help by alerting us if any system/job is down so that SLA can be saved. Overall ActiveBatch Automation stands as a dependable cornerstone for ensuring the seamless operation of our tasks.

Incentivized

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

• Businesses can use ActiveBatch to plan tasks based on parameters like frequency, dependencies, and the time of day. By automating typical actions like backups and data transfers, businesses can make sure that crucial operations go off without a hitch.
• Multiple systems and apps can be used in complicated workflows that ActiveBatch can automate. For instance, it can automate a workflow for processing orders from beginning to end, from the customer order through inventory control and delivery through the processing of invoices and payments.
• Files can be sent between many platforms and systems safely with ActiveBatch. Transfers to cloud-based storage systems like Amazon S3 and Microsoft Azure are also included in this. SFTP and FTP transfers are also included.

• 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

• On RARE occasions, have seen scheduling properties changed that don't take effect.
• Simpler to understand/more robust reporting options would be nice to have.
• Maybe I'm missing something, but why doesn't the Instances view show completion time? Just execution time and duration.

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

We can easily add new plans/jobs in our batch schedules. Also, coordination with reporting and QA jobs is simple to do. Building schedules, restarting jobs, triggering dependencies is easy to understand. The system is very stable and allows us to easily see overall processing times.

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

My colleague contacted them directly, I only know hearsay on this but it was not good.

Incentivized

The workload automation solution is based on the specific needs of an organization, as well as the features, capabilities, and costs of various solutions. A thorough evaluation process and consideration of these factors can help ensure the selection of a solution that aligns with overall business objectives and meets the specific needs of the organization.

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

• I have not run numbers to determine hard impact, but a quick estimate is that at least one job is running for a average of about 6 hours per day - that 6 hours, if done by hand, would equate to about 30 - 40 hours per day (and in some cases, could not be duplicated manually, as the job repeats faster than a person could accomplish one cycle.)

• 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