hive advanced interview questions - Wikitechy https://www.wikitechy.com/interview-questions/tag/hive-advanced-interview-questions/ Interview Questions Mon, 13 Sep 2021 04:38:19 +0000 en-US hourly 1 https://wordpress.org/?v=6.9 https://www.wikitechy.com/interview-questions/wp-content/uploads/2025/10/cropped-wikitechy-icon-32x32.png hive advanced interview questions - Wikitechy https://www.wikitechy.com/interview-questions/tag/hive-advanced-interview-questions/ 32 32 What is the difference between hive and pig ? https://www.wikitechy.com/interview-questions/hive/what-is-the-difference-between-hive-and-pig/ https://www.wikitechy.com/interview-questions/hive/what-is-the-difference-between-hive-and-pig/#respond Tue, 13 Jul 2021 22:18:37 +0000 https://www.wikitechy.com/interview-questions/?p=600

Difference between hive and pig

Hive pig
Hive is used by data analysts. Pig Hadoop Component is generally used by Researchers and Programmers.
Hive is used in structured Data Pig Hadoop Component is used for semi structured data.
Hive Hadoop Component has a
declarative SQL language (HiveQL).		 Pig has a procedural data flow language (Pig Latin).
Hive uses thrift based server that send queries
and corner directly to the Hive
server which execute them.		 This feature is not available with Pig.
Hive directly leverages SQL expertise
and thus can be learnt easily.		 Pig is also SQL but varies to a great extent and it will take some time efforts to master Pig.
Hive not support in Avro. Pig supports in Avro.
Hive Hadoop Component operates
on the server side of any cluster.		 Pig Hadoop Component operates on the client side of any cluster.
Hive Hadoop Component is mainly
used for creating reports.		 Pig Hadoop Component is mainly used for programming.
Hive helpful for ETL. Pig is a great (Extract, Transform and Load) tool for
big data its powerful transformation and processing capabilities.
Hive makes use of exact variation of the
SQL DLL language by defining the tables beforehand
and storing the schema details in any local database.		 In Pig there is no dedicated metadata database and the schemas or data types will be defined in script itself.
The Hive has a provision for partitions so that can
process the subset of data by date
or in an alphabetical order.		 Pig Hadoop component does not have any notion for partitions though might be one can achieve this through filters.
It renders users with sample data for each
scenario and each step through
its “Illustrate” function.		 This feature is not incorporated with the Hive Hadoop Component.
]]> https://www.wikitechy.com/interview-questions/hive/what-is-the-difference-between-hive-and-pig/feed/ 0 What is the difference between a normal hive table and hive table stored as ORC ? https://www.wikitechy.com/interview-questions/hive/what-is-the-difference-between-a-normal-hive-table-and-hive-table-stored-as-orc/ https://www.wikitechy.com/interview-questions/hive/what-is-the-difference-between-a-normal-hive-table-and-hive-table-stored-as-orc/#respond Tue, 13 Jul 2021 21:51:40 +0000 https://www.wikitechy.com/interview-questions/?p=584 Difference between a normal hive table and hive table stored as ORC
Normal hive table Hive table stored as orc
Hive data is organized into Databases:
Namespaces function to avoid naming conflicts for tables,
views, partitions, columns, and so on.		 It produce efficient way to store Hive data.It designed to overcome the drawback of others Hive file formats Using Optimized Row Columnar files improves speed when Hive is reading, writing, and processing data.
Databases can also be used to enforce security for a
user or group of users.		 Advantages- Single file as the output of each task, that reduces the NameNode’s load.
There are two types of tables in Hive,
one is Managed table and second is external table.
The difference is, when you drop a table,
if it is managed table hive deletes both data and meta data,
if it is external table Hive
only deletes metadata.		 An Optimized Row Columnar file contains types of data define as stripes,along with information in a file footer. At the end of the file a postscript holds compression parameters and the size of the compressed footer.
It damage how data is loaded,
controlled, and managed in Hive ;By use external
tables when the data is also used outside of Hive.		 Default stripe size is 250 MB. large stripe sizes enable large, efficient reads from HDFS.
The data files are read and processed by an existing
program that doesn’t lock the files. Data needs to
remain in the underlying location even after a DROP		 File footer having a list of stripes and number of rows,each columns contains count, min, max, and sum.
Storing data in Hive is nothing but
storing data in HDFS (Hadoop).		 The stripe file footer contains a directory of stream locations.Row data is used in table scans
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