Azure Data Factory vs. Oracle Data Integration Platform Cloud (DIPC)

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

For daily transactional information and customer data needs it has been an extremely fast and reliable system. Several thousand users are able to connect many third party tools and run a large number of extracts while still experiencing great performance. For smaller systems in use with limited user accounts this system is less appropriate due to maintenance resources required.

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

• ODI uses ELT architecture, which enhances the load performance and is very scalable.
• ODI is platform independent. It supports all platforms, hardware and operating systems.
• It comes with pre-built integration, allowing immediate data replication, transformation, and data governance while ensuring data quality.

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

• Training and ramping up to learn all the Oracle specific intricacies.
• Security concerns with administering some of the tools to end users.

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

We are longtime ODI on-premise users. So, it was easy for us to migrate to the ODI cloud rather than implementing another solution.

Incentivized

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

Incentivized

• Ability to setup reliable disaster recovery.
• Upgrades and performance monitoring tools are readily available.

Incentivized