Dexi.io with Mozenda vs. Fivetran vs. Hevo Data

The Mozenda platform is well suited to some specific scenarios that may work well for your organization. It is especially helpful for basic scraping of information from simple websites. If the information you need to obtain is clearly stated on a website, then Mozenda is a simple solution to capture that data quickly and easily.

Incentivized

Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.

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

• Action Items. Mozenda is broken down into a series of actions that can be performed on a page. Some actions take you to other pages. The action list, while short, provides a comprehensive set of tools for scraping just about any website (I haven't met one where it didn't work yet).
• Error Handling. I've used some scraping tools that have virtually no error handling. Mozenda has great error handling; the software almost always pinpoints the exact action where the agent failed and why.
• Speed. Mozenda is very fast at obtaining results. Other "bot" software can be slow and cumbersome but Mozenda plows through pages rapidly and accurately aggregates information.
• XPath compatibility. One of the most powerful features of Mozenda is the integrated ability to use XPaths to get at the exact bits of information we're after on a particular website. Access to visible information is only limited by imagination.

• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

Incentivized

• Extract
• Load
• Transform
• Support

Incentivized

• The user interface for managing agents can be improved
• When websites change, the agents could be better at dynamically updating
• The "credit" usage can be confusing

Incentivized

• Very difficult to get connectors enhanced if a specific needed object is not supported by them
• Depending on the edition needed and the data volumes, can get quite expensive

Incentivized

• Support - their support teams try to be helpful, but often miss the mark
• Error logging - we've run into a few issues debugging errors and limited support is provided by Hevo

Incentivized

Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.

Incentivized

It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

Incentivized

I'm only personally experienced with UBot Studio and Scrapy. UBot Studio isn't a bad application, though it has quite a large number of bugs when compared to Mozenda. UBot touts itself as low-code, but requires a significant understanding of programming logic to use the program effectively. Using UBot, we were able to get virtually all the results we wanted, but at a much higher labor cost than Mozenda. During initial implementation of UBot, it took me a full week to develop a particularly complicated agent. Using Mozenda, I was able to recreate the same agent in 4 hours. The ROI has been remarkable, and Mozenda really is a low-code platform.

We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.

Incentivized

1. Cost efficient 2. Creation of automated pipeline 3. Can load data from multiple data sources 4. Updates data in near real-time - We were able to get near real time insights from the data model which we have created in hevo 5. It has good integration with different BI tools

Incentivized

• Stay relevant in the market
• Ensure product placement against competitors
• Dramatically enhance profitability

Incentivized

• It has been very positive in serving BI team with new source requests
• It has been OK at scaling to match as data volumes as source data size grows

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