Denodo is the eponymous data integration platform from the global company headquartered in Silicon Valley.
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gathr.ai
Score8.9 out of 10
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Gathr.ai powers AI with complete data context for higher quality intelligence.
Product suite:
Data Warehouse Intelligence | Document Intelligence | System Intelligence | Data Pipelining | Data+AI Fabric | Analytics
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Pricing
Denodo
gathr.ai
Editions & Modules
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No answers on this topic
Offerings
Pricing Offerings
Denodo
gathr.ai
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Features
Denodo
gathr.ai
Data Source Connection
Comparison of Data Source Connection features of Denodo and gathr.ai
Feature
Denodo
-
Ratings
gathr.ai
8.8
2 Ratings
5% above category average
Connect to traditional data sources
00 Ratings
8.62 Ratings
Connecto to Big Data and NoSQL
00 Ratings
9.12 Ratings
Data Transformations
Comparison of Data Transformations features of Denodo and gathr.ai
Feature
Denodo
-
Ratings
gathr.ai
9.5
2 Ratings
15% above category average
Simple transformations
00 Ratings
9.52 Ratings
Complex transformations
00 Ratings
9.52 Ratings
Data Modeling
Comparison of Data Modeling features of Denodo and gathr.ai
Feature
Denodo
-
Ratings
gathr.ai
9.2
2 Ratings
15% above category average
Data model creation
00 Ratings
9.11 Ratings
Metadata management
00 Ratings
9.11 Ratings
Business rules and workflow
00 Ratings
9.52 Ratings
Collaboration
00 Ratings
9.02 Ratings
Testing and debugging
00 Ratings
9.02 Ratings
Data Governance
Comparison of Data Governance features of Denodo and gathr.ai
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
1. For ingesting multiple data sources to multiple data emitters. 2. Creating ETL pipelines in easier manner. 3. Easier to collaborate with multiple products. 4. No manual efforts, as it provides drag and drop feature for creating and maintaining data pipelines. 5. Easy to play with structured and unstructured data and schemas.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info