Denodo is the eponymous data integration platform from the global company headquartered in Silicon Valley.
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Presto
Score 10.0 out of 10
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Presto is an open source SQL query engine designed to run queries on data stored in Hadoop or in traditional databases.
Teradata supported development of Presto followed the acquisition of Hadapt and Revelytix.
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TIBCO Data Virtualization
Score 8.6 out of 10
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TIBCO Data Virtualization is an enterprise data virtualization solution that orchestrates access to multiple and varied data sources and delivers the datasets and IT-curated data services foundation for nearly any solution.
In its tool selection process, Cloetta used a Value for Money model on Excel, scoring the functions, performance, vendor qualifications, support, and consultancy required against the total cost of ownership for the first five years of usage. The company went with TIBCO® Data …
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.
Presto is for interactive simple queries, where Hive is for reliable processing. If you have a fact-dim join, presto is great..however for fact-fact joins presto is not the solution.. Presto is a great replacement for proprietary technology like Vertica
TIBCO Data Virtualization is well suited for customers who are challenged to deal with extracting data from dozens of different sources and systems, and do not have the time and liberty to hire data engineers and/or ETL developers to write dozens or hundreds of complex ETLs. However, there are situations where TIBCO Data Virtualization severely underperforms, and those are where we are dealing with large volumes of data, in tera bytes or peta byte scale system. For example, a messaging queue which sends 200 million messages every hour will choke TIBCO Data Virtualization if the technology is chosen to route the data.
Linking, embedding links and adding images is easy enough.
Once you have become familiar with the interface, Presto becomes very quick & easy to use (but, you have to practice & repeat to know what you are doing - it is not as intuitive as one would hope).
Organizing & design is fairly simple with click & drag parameters.
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.
Presto was not designed for large fact fact joins. This is by design as presto does not leverage disk and used memory for processing which in turn makes it fast.. However, this is a tradeoff..in an ideal world, people would like to use one system for all their use cases, and presto should get exhaustive by solving this problem.
Resource allocation is not similar to YARN and presto has a priority queue based query resource allocation..so a query that takes long takes longer...this might be alleviated by giving some more control back to the user to define priority/override.
UDF Support is not available in presto. You will have to write your own functions..while this is good for performance, it comes at a huge overhead of building exclusively for presto and not being interoperable with other systems like Hive, SparkSQL etc.
Performance of TDV repository database is rather poor for larger numbers of objects .(Note: We have approx. 9tsd objects introspected in TDV and approx. 20tsd objects generated in upper DV layers.)
Propagation of privileges to parent/child dependencies does not work when applying recursively on a folder. (It's a huge setback when working with large number of objects organized semantically into subfolders.)
Lack of command line client interface for scripting at the time of version 8.4 (I had to write my own CLI.)
TDV Studio does an absolutely horrible job with its own code editors when indentation is in place. Also, the editor is brutally slow and feature-poor.
Tracking privileges on the level of table/view columns causes occasional problems when regranting.
TDV's stored programs ("SQL scripts" in their own terminology) compiler leaves out many syntactic and semantic checks, making them hugely prone to run-time errors.
TDV Server's REST API is a very poor (in terms of features) and flawed cousin to its SOAP API (at the time of version 8.4).
TDV's interface is a bit dated and not entirely intuitive. Would recommend some UX design review as the interface leaves a bit to be better understood to be used by users without inherent knowledge of Tibco. Overall I'd suggest more improvement here to ensure usability by a lesser tech audience.
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.
This product's performance is very consistent. It is extremely rare for templates to fail. I've been using this software for 5 years and find it to be both simple and powerful. The impact within the company has been very positive as different processes in different areas, such as data analysis, development, and integrations, have been improved, and, best of all, it has not affected the users. Various systems with which it is connected in order to obtain information.
On a few occasions I have asked TIBCO technical support for help because I have adapted perfectly to their tools, but in those few that I have communicated with their technical team I have received personalized, attentive, responsible attention and I am always assisted by an expert staff the topic. A TIBCO technical support technician spent more than an hour helping me to solve a problem in the initial stage of implementation in my department and this is something that I always appreciate.
The training was helpful. I was able to understand how to use TIBCO for the data load process that we implemented and how to perform various troubleshooting steps based on the training I received. The technician was thorough and took the time to answer any questions. Once we were shown how to use TIBCO in the test environment, we were able to configure the production environment ourselves.
Other vendors have clearer, more visual implementation documentation. We also did not have our data architect and and server administrator available full-time for implementation. In the future, we will secure the necessary internal resources.
Presto is good for a templated design appeal. You cannot be too creative via this interface - but, the layout and options make the finalized visual product appealing to customers. The other design products I use are for different purposes and not really comparable to Presto.
We did not need to evaluate another technology in the same category for data virtualization, since we are 100% sure of the capabilities and benefits that we would have with TIBCO Data Virtualization, both for market positioning as well as success stories from other companies. great renown worldwide. From the first day of use, it meets our needs to provide the expected solutions.
