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

Azure Data Factory is a great data integration tool for developing a cloud data platform, especially within the Azure ecosystem. Azure Data Factory is very good for the Data Ingestion part. It can work for simple data transformation with its Data Flow, but it will also need cluster configuration, and there is some cost. Also, it is an excellent tool for orchestrating data pipelines. But for complex data transformations, you may need to use technologies like Databricks and PySpark.

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.

• Azure Data Factory supports a vast array of source and destination connectors, both from within the Microsoft ecosystem (like Azure Blob Storage, Azure SQL Database, Azure Cosmos DB) and external platforms (like Amazon S3, Google Cloud Storage, SAP, Salesforce, and many more).
• Azure Data Factory's Mapping Data Flows provides a code-free environment to design data transformations visually. Users can drag and drop elements to create complex ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes without needing to write any code.
• Azure Data Factory provides a unified monitoring dashboard that offers a holistic view of all pipeline activities. I think this makes it easier for users to track the status of various jobs, identify failures, and pinpoint bottlenecks.

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

• For complex JSON when it comes to mapping nested attribute it's not easy to flatten out
• Data Factory V1 does not have a good implementation experience as compared to V2
• Work with on premise solutions sometimes is not too friendly because you will need to set a VPN

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

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.

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.)

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

Incentivized