Denodo vs. Fivetran vs. ibi Data Migrator

Denodo allows us to create and combine new views to create a
virtual repository and APIs without a single line of code. It is excellent
because it can present connectors with a view format for downstream consumers
by flattening a JSON file. Reading or connecting to various sources and
displaying a tabular view is an excellent feature. The product's technical data
catalog is well-organized.

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

• Database Agnostic: You can easily connect to different environments and mash up data sets.
• The "magic" of data virtualization: No data is created, so data is reported in near-real-time to end users.
• It's easy to use UI for developers. You just connect to a data source, create tables, and join them to other datasets.

Incentivized

• 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

• Caching - but I am sure it will be improved by now. There were times when we expected the cache to be refreshed but it was stale.
• Schema generation of endpoints from API response was sometimes incomplete as not all API calls returned all the fields. Will be good to have an ability to load the schema itself (XSD/JSON/Soap XML etc).
• Denodo exposed web services were in preliminary stage when we used; I'm sure it will be improved by now.
• Export/Import deployment, while it was helpful, there were unexpected issues without any errors during deployment. Issues were only identified during testing. Some views were not created properly and did not work. If it was working in the environment from where it was exported from, it should work in the environment where it is imported.

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

Denodo is very easy to use. It has a user-friendly drag and drop interface. I'm not a fan of the java platform it resides on.

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

Denodo is a tool to rapidly mash data sources together and create meaningful datasets. It does have its downfalls though. When you create larger, more complex datasets, you will most likely need to cache your datasets, regardless of how proper your joins are set up. Since DV takes data from multiple environments, you are taxing the corporate network, so you need to be conscious of how much data you are sending through the network and truly understand how and when to join datasets due to this.

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

Denodo is simple and easy to use. Highly recommended unless you have huge volumes of data

Incentivized

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

• It is a huge advantage that we can connect to many different databases to provide data rapidly and accurately.
• It has proven to be a valuable environment for deploying data virtualization solutions, and its user community is active in finding and fixing issues.

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