ActiveBatch Workload Automation vs. Azure Data Factory

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

In a data pipeline, you will be able to add different kinds of activities for example connect from your on-premise SFTP and move CSV files to storage accounts. As well data factory has its own data flow if you are an ETL developer who experimented with maybe you have worked with SSIS, thus, you will start quickly with this new feature of the data factory.

Incentivized

• Makes scheduling easy to understand, follow, and rerun jobs when necessary.
• Allows for cross-team coordination of scheduled tasks which reduces errors.
• Makes stopping jobs easy when needed for server/database downtime.
• Scripting enables us to easily change email addresses for failed job alerts.
• Nested plans/jobs make creating and changing dependencies simple.

Incentivized

• Creating ETL and ELT workflows as well as orchestrating and monitoring pipelines without writing any code.
• Hybrid data integration is easily and agilely possible through this software.
• It has lot of various useful components

Incentivized

• String handling / parsing. I find myself using PowerShell to do a fair amount of text parsing (particularly if manipulations are needed) - not necessarily a bad thing, but certainly a place where ActiveBatch could be improved.
• Debugging - or lack of it! With no stepping debugger, it can be a longer process than many other programming / scripting environments: rather than simply stepping through and observing state changes, I find myself inserting logging steps to excess, then having to clean them up once the error is found.
• The perennial - Documentation! While a near-universal complaint for *any* software, ActiveBatch's developer documentation is somewhat spotty - just where I need detail, I find summary-level info. There is lots of documentation (as there should be for a tool with such a wide range of applications), but it is in mixed formats (some PDF, some CHM), and the descriptions of specific fields within job steps is often little more than I can get in a tool-tip in the GUI. Allowable ranges, expected formats for string data, and similar helpful details are inconsistent.
• The KnowledgeBase at ASCI's web site often has examples which answer the questions I have, but not always - and not always under the search terms one would think to use.

• Learning curve for pipeline creation interface.
• Alerting isn't necessarily built in. Had to work around this to meet team needs.
• With GIT enabled, some features can only be done via git, while some need to be done via the portal.

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

So far product has performed as expected. We were noticing some performance issues, but they were largely Synapse related. This has led to a shift from Synapse to Databricks. Overall this has delayed our analytic platform. Once databricks becomes fully operational, Azure Data Factory will be critical to our environment and future success.

Incentivized

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

Incentivized

We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue

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

Azure Data Factory fits well into our overall systems architecture where we already utilize largely Azure services and also Microsoft based products in the on-premises environment. I think cost structure is also very competitive with Azure Data Factory. Most services provide a visual interface for designing ETL workflows, but our team found Azure Data Factory's interface more intuitive.

Incentivized

• ActiveBatch can automate intricate procedures and minimise manual involvement, which can boost an organization's production and efficiency.
• Organisations can save money by using ActiveBatch to automate operations, which lowers the expenses of manual labour and potential mistakes.
• Implementing ActiveBatch could come with hefty up-front expenses including licencing, instruction, and consultancy fees, which could have a short-term negative impact on ROI.

• Limiting the amount of data moving up and down from the cloud for cloud-native applications.
• Overall simple to use interface which is actually easier for a first time ETL developer than SSIS.

Incentivized