Apache Hive vs Apache Derby

• Hive syntax is almost like SQL, so for someone already familiar with SQL it takes almost no effort to pick up Hive.
• To be able to run map reduce jobs using json parsing and generate dynamic partitions in parquet file format.
• Simplifies your experience with Hadoop especially for non-technical/coding partners.

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• Much easier to use and learn than most commercial databases.
• Derby database errors are well documented.
• Most complex queries supported as well as by other database vendors.

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• Use Hive for analytical work loads. Write once and read many scenarios. Do not prefer updates and deletes.
• Behind scenes Hive creates map reduce jobs. Hive performance is slow compared to Apache Spark.
• Map reduce writes the intermediate outputs to dial whereas Spark operates in in-memory and uses DAG.

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• Not as well positioned in the market as the "big dogs" (Oracle, MSSQL)
• Been around awhile, and not a lot of exposure. This I believe is primarily due to it's relation to the Apache Project (which is not bad, but they don't force big, lumbering corporate volume licensing on you), and thus people are a bit gun-shy about NOT throwing money at something
• Java is still "slow" compared to C/C++, thus making Derby a bit slow too

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• It exposes the distributed calculation world (Hadoop) to the users but doesn't require the user to have the in-depth understanding of boilerplate details, it reduces the time of learning and let the data analyst can focus their efforts on the core business.

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• Saved money on database licensing.

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