Hbase can efficiently handle huge amount of data.

It is fault tolerance.

This is an out of box component of Hadoop, so we do not have to purchase it. Review collected by and hosted on G2.com.

The major drawback of Hbase is that we cannot perform joins in it, that is a very basic thing expected in a database.

Here we have single point of failure i.e., Hbase Master, on failure of which the whole hbase stops functioning. Review collected by and hosted on G2.com.

Storing bulk data on Hadoop file system, using NoSQL database. Ease of handling bulk data Review collected by and hosted on G2.com.

As it is a NoSQL tool, we cannot fire normal SQL queries, to fetch data from the same Review collected by and hosted on G2.com.

Scaling capability to 100 of nodes and super fast Review collected by and hosted on G2.com.

Two many processes makes it hard to manage Review collected by and hosted on G2.com.

It is open source, written in Java, and it is non-relational. Review collected by and hosted on G2.com.

SQL integration layer is a bit complex, and it runs particularly on UNIX Review collected by and hosted on G2.com.

HBase is a good solution if you are kind of SQL guy and you work in Hadoop ecosystem, and you don't want to write a lot of code to handle the low-level HDFS I/O. HBase is really fast, and can store a very big data with hundreds of columns.

Also, HBase is bundled in Cloudera, and has a very active community of supporters. You can easily find answers for your questions in StackOverflow or elsewhere. Review collected by and hosted on G2.com.

HBase takes many extra hard-disk, because it builds many index for one table. Also, you have to be careful if you want to get the best trade-offs between writing and querying - fine tuning is necessary.

HBase is also too level to support some kind of data schema such as time series. For that, you usually need to install other database on top of it, such as OpenTSDB Review collected by and hosted on G2.com.

The query and structured storage of big data. Can be connected via different platform or programming language. Review collected by and hosted on G2.com.

I have not found anything to dislike in hbase . Sequential processing takes more time for big data and as data size increases. Review collected by and hosted on G2.com.

With HBase, you get what you asked for. HBase is great because it's an open-source, well-known Apache project. As a result, the community and documentation are well-developed -- most likely, any issue you run into will be on StackOverflow. Review collected by and hosted on G2.com.

Unless you're buying into a fully-managed, bare-metal Hadoop environment, I'd say you should first look into what cloud-providers offer in terms of HBase-like, columnar databases. Check out Google Cloud Platform or Amazon Web Services before ever installing HBase from Apache, Cloudera, HortonWorks, etc., and even then, know that cloud instances are expensive. In addition, I'd make sure that a columnar database is even what you need. In most cases, a traditional relational database is probably just fine for your use case, as you'll later find exporting data from HBase or using HBase for a variety of queries is a lot of work. Review collected by and hosted on G2.com.

Easy to set up, and easy to use. Fact that it sits on top of hdfs is even better Review collected by and hosted on G2.com.

not very easy to diagnose issues. GC tuning is a real p.i.t.a. Review collected by and hosted on G2.com.

HBase is very much a developer-centric database. Hbase is built on Hadoop ecosystem, stored procedures through coprocessors and consistent Review collected by and hosted on G2.com.

Doesn't have a easy query language like SQL, too complex and too many variable parts, mostly can only be used with single node cluster Review collected by and hosted on G2.com.

High performance overall. Open source nature makes it easy to contribute and review the internals of the project. Also makes it easy for tools adding functionality to be built and integrated on top. Costs nothing. Well supported by PAAS providers such as AWS.

Consistency ensures correct results will be found, though they may take longer.

Can use SQL like language through integration with Apache Hive, allowing users new to NoSQL databases to limit the initial learning curve. Review collected by and hosted on G2.com.

Choice of consistency and partitioning at the expense of availability results in some queries taking longer than necessary.

Ability to use SQL like language can lead users to view it as suitable for traditional relational DB use cases when this is not the case. Review collected by and hosted on G2.com.