NoSQL

1) One of the key ones is low latency for executing SQL queries on top of Hadoop. And part of this has to do with bypassing the MapReduce infrastructure which involves significant overhead, especially when starting and stopping JBMs.

2) Cloudera also claims several magnitudes of improvement in performance compared to executing the same SQL queries using Hive.

3) Another benefit is that if we really wanted to look under the hood at what Cloudera has provided in Impala or if we wanted to tinker with the code, the source code is available for you to access and download.

a) Because of the high degree of interconnectedness between nodes, graph

databases are generally not suitable for network partitioning.

b) Graph databases don’t scale out well.

Impala is a SQL query system for Hadoop from Cloudera. It is currently in beta; and it has been opensource and it's source can be downloaded from Gitap. It supports the same SQL syntax, the ODBC driver and the user interface (which is Beeswax) as Apache Hive.We can use it to query data, whether it is stored in ADFS or Apache H-base. And we can do selects, joins and aggregate functions.

Impala is a SQL query system for Hadoop from Cloudera. The Cloudera positions Impala as a "real-time" query engine for Hadoop and by "real-time" they imply that

rather than running batch oriented jobs like with MapReduce, we can get much faster query results for a certain types of queries using Impala over an SQL based front-end.

It does not rely on the MapReduce infrastructure of Hadoop, instead Impala implements a completely separate engine for processing queries. So this engine is a specialized distributed query engine that is similar to what you can find in some of the commercial pattern related databases. So in essence it bypasses MapReduce.

IBM claims that Big SQL provides robust SQL support for the Hadoop ecosystem:

- it has a scalable architecture;

- it supports SQL and data types available in SQL '92, plus it has some additional capabilities;

- it supports JDBC and ODBC client drivers;

- it has efficient handling of "point queries" (and we'll get to what that means);

- there are a wide variety of data sources and file formats for HDFS and HBase that it supports;

- And it, although is not open source, it does interoperate well with the open source ecosystem within Hadoop.