Apache Hive vs. H2 Database Engine

Apache Hive shines for ad-hoc analysis and plugging into BI tools. Its SQL-like syntax allows for ease of use not for only for engineers but also for data analysts. Through our experience, there are probably more desirable tools to use if you are planning on integrating Hive into your processing pipeline.

Incentivized

This really depends on the use case. For an in-memory replacement database for running unit test cases with, H2 Database Engine is an excellent option. However, if you are looking for a general purpose database for your production systems, then H2 Database Engine is not suited for this purpose.

Incentivized

• 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.

Incentivized

• Can run as an in-memory database.
• Simple and quick to get started with, and is light weight (only 2MB).
• SQL compliant so it compatible with most relational databases.

Incentivized

• 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.

Incentivized

• There's a warning in official FAQ "Is it Reliable?"-section which makes it seem like H2 is not yet a mature product.
• If raw SQL queries are used there maybe be differences between MySQL & H2. ORM library should be used.
• Support seems to be community-based only.

Incentivized

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.

Incentivized

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.

Incentivized

We have used a simple but necessary function such as merging certain data tables, which although they may be from different areas, complement each other or are necessary, you can use metadata if what you need is to validate the origin of your information and what impact it has, is also feasible.

Incentivized

Both MySQL & H2 [Database Engine] are relational databases & use same query language. Application features can be implemented with both but if it's expected that the application will be used by large user base or is complex MySQL is better. Cloud providers provide scaling support for MySQL and also it's more battle-tested. H2 is good when it's a small application as H2 is easier & quicker to set up.

Incentivized

• Good ROI for being able to access data easily across the network, we have large amounts of data and this is a good system to access it
• Good ROI for being easy to learn how to use for new employees, not much time spent which saves costs
• Good ROI for being able to integrate with Spark and other applications, hence data can be analyzed through programs

Incentivized

• Doesn't take time from developers, once it's configs are set up for testing it works in everyone's development environments
• Easy to integrate in application, no need to setup separate database software, no maintenance
• No need to deal with infrastructure related issues/costs - database runs in same machine as the application that uses it.

Incentivized