There are areas where Hevo could improve, particularly regarding its cost structure, as the pricing is based on usage and the advanced features can become costly for startups that are not well-established. Additionally, the lack of flexibility in building gateways and pipelines limits customization options. Furthermore, despite the simplicity of Hevo, technical knowledge is still needed for monitoring and ensuring the pipelines and connectors function efficiently, particularly in implementing the ETL process and managing data recovery accurately.
Hevo could be improved as we hit limits, particularly when it couldn't keep up with the high number of databases we wanted to sync frequently—ideally updating everything hourly for near real-time analytics rather than a day behind. We found that with many pipelines, performance degraded. After discussing with Hevo engineers, I learned that Hevo is designed for fast individual pipelines, but struggles with many concurrent pipelines. They suggested that consolidating data from fewer pipelines with more fields would perform better, but even that failed when I tested it with a Business Central database that had numerous fields. Hevo does have limits on the number of pipelines it can manage concurrently and on the number of fields in a single pipeline.Additionally, the API was slower to release compared to the user interface and was not feature-complete, which was frustrating. That said, Hevo actively worked on bridging many gaps. Overall, these were significant pain points I encountered.
Data Integration facilitates the combination of data from diverse sources into a unified view, crucial for businesses to make informed decisions and enhance operational efficiency. With comprehensive solutions available, organizations can streamline their data workflows. Data Integration solutions are vital for businesses aiming to handle large volumes of data efficiently. These solutions help in synchronizing data from multiple sources, ensuring consistent data across platforms, and...
There are areas where Hevo could improve, particularly regarding its cost structure, as the pricing is based on usage and the advanced features can become costly for startups that are not well-established. Additionally, the lack of flexibility in building gateways and pipelines limits customization options. Furthermore, despite the simplicity of Hevo, technical knowledge is still needed for monitoring and ensuring the pipelines and connectors function efficiently, particularly in implementing the ETL process and managing data recovery accurately.
Hevo could be improved as we hit limits, particularly when it couldn't keep up with the high number of databases we wanted to sync frequently—ideally updating everything hourly for near real-time analytics rather than a day behind. We found that with many pipelines, performance degraded. After discussing with Hevo engineers, I learned that Hevo is designed for fast individual pipelines, but struggles with many concurrent pipelines. They suggested that consolidating data from fewer pipelines with more fields would perform better, but even that failed when I tested it with a Business Central database that had numerous fields. Hevo does have limits on the number of pipelines it can manage concurrently and on the number of fields in a single pipeline.Additionally, the API was slower to release compared to the user interface and was not feature-complete, which was frustrating. That said, Hevo actively worked on bridging many gaps. Overall, these were significant pain points I encountered.