Apache Hive vs. Oracle SQL Developer 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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Almost all development activities (the tool is called "SQL Developer", not "DBA Toolset") can be done easily and quick with [Oracle] SQL Developer. From data model creation (tables, views) to development (creation of procedures, functions, packages) and then testing (SQL Developer includes an easy to use debugger), all tasks can be performed in a single tool.

It may not be as complete as other solutions for DBA tasks like instance monitoring, but it is usually OK for development and testing environments if you want to do some basic troubleshooting.

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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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• Object Browser in SQL Developer allows you to explore the contents of your database using the connection tree.
• The SQL Worksheet is an editor that allows for execution of SQL statements, scripts, and PL/SQL anonymous blocks. SELECT statements can be executed to return results in a spreadsheet-like 'grid' or can be executed as a script such to emulate SQL*Plus behavior and output
• DBA Console allows users with administrative privileges to access DBA features such as database init file configuration, RMAN backup, storage, etc.

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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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• Inability to run multiple queries on the same database. You can only run one query on a given database.
• Analytical models created from complex tables isn't accurate, and needs work.
• Inability to view multiple tables of a database side-by-side. When trying to find correlations between tables, it would help to be able to see them at once on the same page.

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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.

We had already thought of changing to TOAD, but we decided to stick with Oracle SQL Developer until the end.

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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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Oracle SQL Developer is very easy to use and there are a wide range of courses available which can help you get started just within a day. Data can be exported in multiple formats based on user requirements. Organizational data can be stored and management effectively using Oracle SQL Developer. All the data, tables, sequences, indexes can be easily created and updated in Oracle SQL Developer.

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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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Large user community support

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Just download and uncompress!

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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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I have started to use Toad for Oracle recently because it is easier to sort and filter results, due to their memory sort feature that puts the results from your query in memory so that you don't have to rerun your query. I have used SQL Developer to easily update records in tables that I need to fix. I haven't found an easy way to do this in Toad other than writing SQL insert statements.

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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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• It gives 100% return on investment as it is free of cost.
• No need to have multiple tools for each database
• Considering the employee training, so one can save money on training, as it is not very hard to use so still savings.

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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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