Azure Data Factory vs. Oracle Data Service Integrator

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.

Incentivized

Once Oracle Data Service Integrator is set up properly, it is quite simple to snatch data from a myriad of disparate sources and put them into a single repository. Due to cost factors, it is not for the faint of heart; however, if you can handle it, the benefits will quickly outpace the initial investment. To take full advantage of the power of ODSI, I would recommend the training that Oracle provides.

Incentivized

• 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

• Very easy to use. Very easy to just grab data when needed.
• Very robust and has so many features to move and transform data.
• Functionality is very easy to implement with other Oracle data products.

Incentivized

• 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.

Incentivized

• If not bundled with other products, the price can be prohibitively expensive
• SAP connectors tend to be a little expensive and slightly difficult to use
• The audit module had some difficulties debugging failed instances

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 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

While we tested and evaluated multiple data service providers, we came to choose Oracle Data Service Integrator since our infrastructure was so heavily dependent on Oracle software. The implementation would be easier and the difference in the number of features and performance wasn't enough to sway us in another direction.

Incentivized

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

Incentivized

• It provided terrific bulk data movement and data transformation services which we desperately needed
• It allowed to reduce development costs and lower the total cost of ownership among disparate architectures
• The enterprise edition's ELT allowed us to generate native code for wildly varied RDBMS

Incentivized