Apache Hive vs. Azure App Service vs. PrestoDB (or Presto)

Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.

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You may easily deploy your apps to Azure App Service if they were written in Visual Studio IDE (typically.NET applications). With a few clicks of the mouse, you may already deploy your application to a remote server using the Visual Studio IDE. As a result of the portal's bulk and complexity, I propose Heroku for less-experienced developers.

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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

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• Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
• Relatively easy to set up and start using.
• Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.

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• Extremely easy to deploy and update from Visual Studio
• It integrates seamlessly with other Azure PaaS resources
• It has an in-depth integration with AppInsights, so you can understand errors and their root cause easily.
• Easy to create and delete, what is not the same case in a IaaS resource
• It escalates based on CPU workload and some other resource variables.
• Configuration changes are almost immediate
• Offers an excellent abstraction from hardware backend of the platform
• That's updated very often, saving time and the risk of a self-performed update over a IaaS
• That's really easy to develop for Web Apps
• It supports Function Apps and Web Apps into the same "cost black box"

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• 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.

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• Some queries, particularly complex joins, are still quite slow and can take hours
• Previous jobs and queries are not stored sometimes
• Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
• Sometimes, directories and tables don't load properly which causes confusion

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• Jumps between resource sizes can get expensive
• You may wind up putting a lot of eggs in one basket--not necessarily a con but something to keep in mind (most of your data will likely be managed and processed through Microsoft products/services if you fully commit to Azure App Service).
• Learning new technology. If you're moving from on-premises to Azure App Service (or any cloud solutions), you'll likely have to rethink how things are done to achieve the same end results (and/or resources may become expensive quickly).

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• 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.

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Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.

Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.

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I have given this rating because Azure App Service performs very well in terms of speed, reliability, and reducing overhead, and improves overall team productivity, with a little scope for improvement in complex testing scenarios and configurations, scalability concerns in a large setup, and similar tracking and audit needs.

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Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.

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Microsoft has always been known for providing a high standard in terms of customer support and Azure App Service (and as a matter of fact the whole Azure Platform) is no exception. Azure App Service never caused us any issues and we only contacted their customer support for questions regarding server locations and pricing. I feel pretty satisfied with how they treat their customers.

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Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed. Amazon Redshift is the another product, I used in my recent organisation. Both Redshift and BigQuery are managed solution whereas Hive needs to be managed

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When we chose it, we did so because of its integration with Microsoft applications; now we need to integrate with AI, and Azure doesn't offer a good integration. That is the main reason to change it. It is still great to develop Windows- and Microsoft-based applications, but if we need to integrate with AI, Google wins by far.

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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.

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• Apache hive is secured and scalable solution that helps in increasing the overall organization productivity.
• Apache hive can handle and process large amount of data in a sufficient time manner.
• It simplifies writing SQL queries, hence helping the organization as most companies use SQL for all query jobs.

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• Deployment of ASP.NET apps at the organization has been sped up.
• An option to offer access to the version control system on a third platform so that we could easily deploy our apps.
• Because of Azure App Service's scalability capabilities, the costs of running the services are kept to a minimum. As a result, we may save hundreds of dollars each month compared to the expenses of traditional servers by using fewer resources during slack periods.

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• 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.

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