Denodo vs Talend Data Fabric

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.

Truly trusted contact center where the effective solution is always guaranteed. It is not a one-off fix to a specific data integration or management problem. It is a permanent and scalable solution to manage all of your data under a unified environment. Easy to use, great performance, used it for our internal data warehouse. Easy to build and connect to our data sources such as Salesforce, Netsuite, and Marketo.

• 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.

• Generated optimized code for building data pipelines.
• Ingested and integrated data from both On-Premises and Cloud environment.
• Created roles for data cleansing, or need to trace data lineage to ensure compliance and integrity.

• 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.

• Hadoop-related and big data components need to be rewritten and improved by Talend.
• The exception handling is not convenient. You can either let go of the exception or stop the process or use an external controller to resume the applications. But you cannot customize the try-catch block of the exception itself.
• Sometimes you have to use "Ctrl" sometimes "Cmd" is really badly done. In my experience the speed of learning clashes with the consumption of resources and the slowness of the interfaces. An interface made in pure Java requires more time to implement but is definitely more performing.

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.

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.

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

1) Talend offers more than 200 palettes, but most of them are repeated. For Example in Pentaho, we can use a single component ( Microsoft Excel input ) and we can get the data, sheet name, file name, and all other things like using a wildcard, etc. 2. Talend is an open-source tool that makes it easily accessible.

• 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.

• Open source
• Full of transformations to support different scenarios
• Integration support with Big Data and MDM.