In my project, we are using Hadoop 2
, Spark
, Scala
. Scala
is the programming language and Spark
is using here for analysing. we are using Hive
and HBase
both. I can access all details like file etc. of HDFS
using Hive
.
But my confusions are -
Hive
, Then why HBase
is required to store the data. Is it not an overhead?HIVE
and HBase
? Can anyone please let me know.
- When I can able to performed all jobs using
Hive
, Then whyHBASE
is required to store the data. Is it not a overhead?- What are the functionality of
Hive
andHbase
HBase
is No Sql database which stores the data in key value pair. Hive
has integration with Hbase
.Hbase HIve Integration
Advantage :- Hive
queries over HBase
. Think joins and a easy way to do aggregates and simple operations on your Hbase
data.
Hbase
gives you a scalable storage infrastructure that keeps data online. StumbleUpon uses Hbase
for their live website. Hive
is not a real-time query engine, so its data store could not be used for similar purposes. Hive
over HBase
gives you the benefit of both worlds.
- If we only used Hive, Then what should be the problem?
If we will use Hive
There is no problem . But in project there so many scenarios we have to consider .
When I can able to performed all jobs using Hive, Then why HBase is required to store the data. Is it not an overhead?
I can't say it's overhead or not. But HBase responds to requests in real-time as its database when it comes to Hive it runs jobs on MapReduce/Spark/Tez engines.
What are the functionality of Hive and HBase?
Hive:
It's a SQL-like language that gets translated into MapReduce/Spark/Tez jobs. it only runs batch processes on Hadoop. for more check this how Hive queries run on MapReduce engine
HBase:
It's key/value store database which runs on top of HDFS/S3(on AWS). It does real-time operations for requests.
If we only used Hive, Then what should be the problem?
As discussed If the query needs to process in real-time then HBase is the choice over Hive.
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