Azure Data Factory vs. Oracle Warehouse Builder

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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The best place for Oracle Warehouse Builder is at the business IT level. It's not suited for business-level users. They are easy confused. One way to reduce the confusion for the developers is to set up the workspaces based on the requirements that are discovered in design sessions. Once this is complete, the implementation of Oracle Warehouse Builder can take flight and be successful.

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• 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.

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• Easy transformation.
• Easy implementation from Oracle to Oracle systems.
• Ease of usage and easy to learn.
• Starting component of metadata management.

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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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• What I noticed is that sometimes OWB doesn't generate the best SQL in the package especially when there are a high number of source tables in the ETL. It would be nice if ETL developers were allowed to update the generated packages in the database directly.
• Another thing - moving OWB ETLs from one database to another one could be easier - for example it would be nice to just copy the generated packages from one database to the other one without doing the deployment of these ETLs through OWB.

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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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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

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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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Ab>initio, IBM Datastage 8.0

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• 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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• It improved understanding of ETL functions. Data is consistent. The speed is pretty good.

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