Azure Data Factory vs. Hevo Data

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

It is of great help for unstructured data sources. The way Hevo Data flattens the high nested data is amazing. Schema management is also good by Hevo Data. The way it's tell about the data type and then we can identify any error in the model. Additionally, It is very easy to setup for any new user and once model is created then we do not have to worry about the script maintenance and updating the script daily.

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

• Hevo is the best tool when you want to manage a large number of pipelines. With Hevo you can automate the pipelines very easily.
• Hevo's great features are automatic detection and notification of failed data pipelines.
• Auto mapping feature is also good. It's makes really easy to move the data.
• Integration is easy with so many databases like SQL, Google Sheet, etc.

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

• Transformation
• More Sources
• More Destinations

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

Much lower cost and faster syncs but not as good support. More flexible in terms of pricing, though. Fivetran requires annual contracts and is very inflexible.

Incentivized

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

Incentivized

• Comprehensive results from the data processing is a perfect approach from the tool.
• Authentic data models brings a high automation process and more productivity.
• Expansive data sourcing brings clarity and genuine results after inferential analytics.

Incentivized