Apache Hive vs. MySQL vs. SQLite

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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MySQL is best suited for applications on platform like high-traffic content-driven websites, small-scale web apps, data warehouses which regards light analytical workloads. However its less suited for areas like enterprise data warehouse, OLAP cubes, large-scale reporting, applications requiring flexible or semi-structured data like event logging systems, product configurations, dynamic forms.

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SQLite is a lightweight and efficient database management system. With SQLite, performance increases as memory are added. It's reliable and well-tested before release. SQLite handles memory allocation and I/O errors gracefully. SQLite provides bug lists and code-change chronologies. All bugs are disclosed, and it's compatible with iOS, Android, MAC, and Windows. SQLite is open-source, allowing developers to tailor it to their specific needs.

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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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• Stable - it just runs, with minimal downtime or errors
• Fast - well-structured data is quickly written and read
• Secure - MySQL is easy to keep data secure from people and applications that shouldn't see it
• Easy to use - SQL is industry standard so no problems with adding, editing and reading data stored in MySQL

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• Our products are using SQLite in embedded Linux and in Windows software.
• It is truly a stable cross-platform database.
• The fact that it is a zero-configuration database engine, it is particularly a nice advantage when you work in the embedded world.
• Being a single database file, it provides ease for those who need to quickly transfer data to someone else, or quickly browse data.

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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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• Learning curve: is big. Newbies will face problems in understanding the platform initially. However, with plenty of online resources, one can easily find solutions to problems and learn on the go.
• Backup and restore: MySQL is not very seamless. Although the data is never ruptured or missed, the process involved is not very much user-friendly. Maybe, a new command-line interface for only the backup-restore functionality shall be set up again to make this very important step much easier to perform and maintain.

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• Although it is excellent at what it does, you should be really careful and plan accordingly if you know that your database is going to scale at a huge level because it is not suitable of databases which are of Enterprise level and demands top-notch security and protection.
• If your project involves multiple people working on the same database simultaneously, then that becomes a big problem, because it only allows single write at one time. You really need to be forward thinking in a manner to predict if this database will cater to all the needs of your project.
• The most common difficulty with this is the lack of some of the basic functionality which is present in the other premier databases like Joints, Stored Procedure calls, Security and permission grants. If you do require all those things then you are better off not using this software.
• Lastly, if you are using this in an Andriod App development cycle then also your options are limited because it does not integrate with PostgreSQL and MYSQL.

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

For teaching Databases and SQL, I would definitely continue to use MySQL. It provides a good, solid foundation to learn about databases. Also to learn about the SQL language and how it works with the creation, insertion, deletion, updating, and manipulation of data, tables, and databases. This SQL language is a foundation and can be used to learn many other database related concepts.

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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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I give MySQL a 9/10 overall because I really like it but I feel like there are a lot of tech people who would hate it if I gave it a 10/10. I've never had any problems with it or reached any of its limitations but I know a few people who have so I can't give it a 10/10 based on those complaints.

I have given this rating cause its irreplaceable in some of the areas like no more installation need except from a single library. I find dialect is simple in use cases. its suitable for any professionals with various skill levels. its easily connect with various os and devices. very less maintenance or administration required.

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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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We have never contacted MySQL enterprise support team for any issues related to MySQL. This is because we have been using primarily the MySQL Server community edition and have been using the MySQL support forums for any questions and practical guidance that we needed before and during the technical implementations. Overall, the support community has been very helpful and allowed us to make the most out of the community edition.

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I haven't needed support yet.

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1. Estimate your data size. 2. Test, test, and test.

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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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MongoDB has a dynamic schema for how data is stored in 'documents' whereas MySQL is more structured with tables, columns, and rows. MongoDB was built for high availability whereas MySQL can be a challenge when it comes to replication of the data and making everything redundant in the event of a DR or outage.

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We looked at other traditional RDBMS products, but found them to be cumbersome to deploy. They take up more space, and consume more computing resources than SQLite does. While the performance or direct integration to our primary applications may have been better or easier if we had gone with a traditional RDBMS, the performance of SQLite has been more than acceptable. The performance and speed to deploy made SQLite a much more attractive option for us than a traditional RDBMS.

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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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• As it is an open source solution through community solution, we can use it in a multitude of projects without cost license
• The acquisition by Oracle makes you need to contract support for the enterprise version
• If you have knowledge about oracle databases, you can get more out of the enterprise version

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• No licensing fees has a net increase on ROI
• The active community has kept support costs low, further increasing ROI
• The wide range of supported platforms and high level of compatibility has increased ROI by reducing time spent porting the database model to any platform specific solutions.

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