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

Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.

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• 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.

• It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
• We can use linked service in multiple pipeline/data load.
• It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.

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.

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• Limited source/sink (target) connectors depending on which area of Azure Data Factory you are using.
• Does not yet have parity with SSIS as far as the transforms available.

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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.

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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.

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

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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.

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The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.

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• 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.)

• It is very useful and make things easier
• Debugging can improve
• Its better suited than other products with the same objective

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