There are various tools of SQL-on-Hadoop developed that permitted the programmers for utilizing the existing SQL experits on Hadoop data stores. Familiar and comfortable SQL is their motto based on the front end to ask large data stored under Hadoop architecture. Here in this article you will find the best tools to use and check out their advantages and disadvantages
SQL-on-Hadoop Tool: Cloudera Impala
A luxurious provision for the developers for running a user friendly SQL query on Hadoop Distributed File System (HDFS) and Hbase. Hive also provides an SQL like interface, for following the batch processing that lead to lags if something is looking for performance oriented alternative. This lag has been overcome for running queries in real time that allows integration of SOL BI tools with Hadoop data store.
An open source tool like Impala backs up the popular formats like LZO,Avro, RCFile, sequenceFile etc. A cloud based architecture through Amazon’s Elastic MapReduce. The ANSI SQL compatability of Impal says there is a small amount of business disruption as developers and analysts can be productive from the first day without the requirement of any new language.
SQL-on-Hadoop Tool: Presto
There is another help from Facebook that is provided as an open source tool. It has many similarities with Impala and is written in Java:
Interactive experience is provided.
Considerable groundwork is required that is installation across a number of nodes.
The data should be stored in a particular format (RC FILE)for optimal performance.
On the other hand, Presto gives interoperability with Hive meta-store. Combining data from multiple sources is done by Presto and this is a major advantage for enterprise wide deployments. The major difference from Impala is that Presto is not backed up by any of the major suppliers.
Therefore if you plan for getting an enterprise wide deployment you would need to consider other options even though some of the famous technology giants such as Airbnb and Dropbox are ready to use it.
This is an SQL-on-Hadoop product at the enterprise level capable of handling most of the demands of modern day analytics that tricks most of the boxes. The integrated analytics engine comes with learning capabilities of machine
that enchances the performance with usage. Data analysis, demand with focus on the modern day organizations for query language for handling statistical, mathematical and machine learning algorithms like regression, hypothesis testing, etc.
There are various options at your disposal and SQL experts would gain a lot from the tools for hitting the ground running after choosing the right tool with lots of options at your risk. Do some technical research on the background if you are planning to start in any Hadoop training in the near future.
SQL-on-Hadoop Tool: Shark
With respect to one of the first top SQL-on-Hadoop projects, initiation of Shark as an aliter to have run Hive on MapReduce. The aim is to retain the functionalities of Hive, for delivering superior performance. It has a very good popularity and a faster alternative to Map-Reduce and there are lots of users around the world for it.
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