Likelihood to Recommend Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
Read full review 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.
Read full review Pros Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models. Customization and easy to alter and change. If you already are an Amazon user, you do not need to transition over to another software. Read full review 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. Read full review Cons The UI can be eased up a bit for use by business analysts and non technical users For huge amount of data pull from legacy solutions, the platform lags a bit Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized Read full review 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. Read full review Usability 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.
Read full review Performance 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.
Read full review Alternatives Considered Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
Read full review Denodo is simple and easy to use. Highly recommended unless you have huge volumes of data
Read full review Return on Investment We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers. We can prototype more rapidly because it is easy to configure notebooks to access AWS resources. For our use-cases, serving models is less expensive with SageMaker than bespoke servers. Read full review 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. Read full review ScreenShots