Apache HBase vs. H2 Database Engine

HBase is well suited for streaming ingest, fast lookups, massive datasets, data warehouse lookup tables, RDBMS replacement, MongoDB replacement, key-value store, data scans, logs, JSON storage and some binary storage. My preferred use case is for storing data points like time series or data produced by sensors. I often use HBase when I need data available immediately and I am not looking for transactions. This is a great store for really wide tables with tons of columns. It is also great if you are not sure what type of data you are going to have. It really excels at sparse data.

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

• Scalable and truly non-relational data
• HBase operations run in real-time on its database rather than MapReduce jobs
• Scales linearly to support billions of rows with millions of columns

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

• Write performance
• Performance support for parquet file format. supports, but performance wise still not there
• API / library availability for spark, rather than creating a new library for it

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

There's really not anything else out there that I've seen comparable for my use cases. HBase has never proven me wrong. Some companies align their whole business on HBase and are moving all of their infrastructure from other database engines to HBase. It's also open source and has a very collaborative community.

Incentivized

Compared NoSQL databases with traditional databases for faster retrieval and consistency. As MongoDB is a NoSQL supports dynamic fields, however, query performance is bad for aggregations and added maintenance. When compared with MySQL and Teradata, it could not scale up as fast as Hbase and added cost involved to it. HBase can be easily scalable to a huge volume of records, have a faster lookup and provides consistency

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

• Positive: Open source, easy to use, good to store big data.
• Negative: SQL functionalities are not available.
• More memory utilization
• More troubleshooting

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