Likelihood to Recommend I find HDP easy to use and solves most of the problems for people looking to manage their big data. Evaluating the Hortonworks Data Platform is easy as it is free to download and install in your cluster. Single node cluster available as Sandbox is also easy for POCs.
Read full review 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 Read full review Pros It does a good job of packaging a lot of big data components into bundles and lets you use the ones you are interested in or need. It supports an extensive list of components which lets us solve many problems. It provides the ability to manage installations and maintenance using Apache Ambari. It helps us in using management packs to install/upgrade components easily. It also helps us add, remove components, add, remove hosts, perform upgrades in a convenient manner. It also provides alerts and notifications and monitors the environment. What they excel in is packaging open source components that are relevant and are useful to solve and complement each other as well as contribute to enhancing those components. They do a great job in the community to keep on top of what would be useful to users, fixing bugs and working with other companies and individuals to make the platform better. Read full review 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. Read full review Cons Since it doesn't come with propriety tools for big data management, additional integration is need (for query handling, search, etc). It was very straightforward to store clinical data without relations, such as data from sensors of a medical device. But it has limitations when needed to combine the data with other clinical data in structured format (e.g. lab results, diagnosis). Overall look and feel of front-end management tools (e.g. monitoring) are not good. It is not bad but it doesn't look professional. Read full review 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. Read full review Implementation Rating Try not to change variable names.
Read full review Alternatives Considered We chose [Hortonworks Data Platform] because it's free and because [it] was an IBM partner, suggested as big data platform after biginsights platform.
You can install in more physical computer without high specs, then you can use it in order to learn how to deploy, configure a complete big data cluster.
We installed also in a cloud infrastructure of 5 virtual machine
Read full review 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.
Read full review Return on Investment It is difficult to have a negative impact, because the required investment is not that high. The big open community behind Hortonworks and related Apache Project makes it easy to put 'the wheel to meet the road' quite quickly. We have seen management meetings where the attendants were impressed by the results achieved with the datalake built on HDP. Read full review Presto has helped scale Uber's interactive data needs. We have migrated a lot out of proprietary tech like Vertica. Presto has helped build data driven applications on its stack than maintain a separate online/offline stack. Presto has helped us build data exploration tools by leveraging it's power of interactive and is immensely valuable for data scientists. Read full review ScreenShots