Azure Event Hubs vs. Hevo Data

Large IoT data ingestion which needs to be processed as batches or data which requires certain time to process in such scenario Azure Event Hubs is working very effective.

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

• message queuing
• data processing for notification.
• Handling large volumes of data from multiple sources.

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

• pricing should be less
• integration with the services should be enhanced.
• Deduplication functionality should be there in Azure Event Hubs

Incentivized

• Transformation
• More Sources
• More Destinations

Incentivized

All platforms are good, costing wise it is effective, scalable is being managed by Azure Event Hubs only so only configuration is being required to do, also provides the encrypted, reliable and secure solution .

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

• reducing the cost as you don't need to create a solution which is scalable
• increasing the productivity of the solution.
• customer satisfaction can be increased by integrating it as a solution.

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