Blog

The primary difference between the two involves how the clients connect to Hive. The Hive CLI, which connects directly to HDFS and the Hive Metastore, and can be used only on a host with access to those services. Beeline, which connects to HiveServer2 and requires access to only one .

Hive is a database present in Hadoop ecosystem performs DDL and DML operations, and it provides flexible query language such as HQL for better querying and processing of data.Oct 7, 2021

Understanding Hive big data through the lens of data analytics can help us get more insights into the working of Apache Hive. By using a batch processing sequence, Hive generates data analytics in a much easier and organized form that also requires less time as compared to traditional tools.May 5, 2021

To simply state, Hive performs batch processing operations that take a while to process and give a result. Whereas, Hbase is mostly used for fetching or writing data which is relatively faster than Hive. Hive is a SQL-like query engine that runs MapReduce jobs on Hadoop. HBase is a NoSQL key/value database on Hadoop.Jun 9, 2021

Yes, Pig differs from MapReduce because, in MapReduce, the group by operation is performed at reducer side and filter, and also in the map phase the projection is implemented. Pig Latin provides the operations that are similar to MapReduce, such as groupby, orderby, and filters.Jul 10, 2021

• Difference between HBase and Hive. Hive is used for Batch processing whereas HBase is used for transactional processing. Hive is a query engine whereas Hbase is data storage for unstructured data. Hive is an SQL-like engine that runs MapReduce jobs, whereasHBase is a NoSQL key/value database on Hadoop.

• Hive is most suitable for structured Data&PIG is most suitable for semi-structured data
• Hive is practiced for reporting&PIG for programming
• Hive is used as a declarative SQL&PIG is used as a procedural language
• Hive supports partitions&PIG does not
• Hive can start an optional thrift based server&PIG cannot

• Key Differences Between Hive & Pig Pig is a data flow programming language, whereas Hive is a dataware house and SQL oriented. Pig is all about loading and storing the datasets, whereas Hive can perform update/delete on datasets also. Pig allows you to save intermediate transformation values, whereas Hive doesn't.

• Hadoop, formally called Apache Hadoop, is an Apache Software Foundation project and open source software platform for scalable, distributed computing. Hadoop can provide fast and reliable analysis of both structured data and unstructured data.

Pig operates on the Hadoop platform, writing data to and reading data from the Hadoop Distributed File System (HDFS) and performing processing by means of one or more MapReduce jobs. Apache Pig is available as open source.Jan 17, 2017

1) Hive Hadoop Component is used mainly by data analysts whereas Pig Hadoop Component is generally used by Researchers and Programmers. 2) Hive Hadoop Component is used for completely structured Data whereas Pig Hadoop Component is used for semi structured data.Nov 6, 2021

Pig runs on Hadoop. It makes use of both the Hadoop Distributed File System, HDFS, and Hadoop's processing system, MapReduce.

Pig is used for the analysis of a large amount of data. It is abstract over MapReduce. Pig is used to perform all kinds of data manipulation operations in Hadoop. It provides the Pig-Latin language to write the code that contains many inbuilt functions like join, filter, etc.Jul 9, 2020

Pig–Pig is a data-flow language for expressing Map/Reduce programs for analyzing large HDFS distributed datasets. Pig provides relational (SQL) operators such as JOIN, Group By, etc. Pig is also having easy to plug in Java functions. Cascading pipe and filter processing model.

Which guarantee that Hadoop provides does Pig break? Calls to the Reducer's reduce() method only occur after the last Mapper has finished running. Incorrect. All values associated with a single key are processed by the same Reducer.

Pig has three complex data types: maps, tuples, and bags. All of these types can contain data of any type, including other complex types. So it is possible to have a map where the value field is a bag, which contains a tuple where one of the fields is a map.

Apache Pig is an open-source technology that offers a high-level mechanism for the parallel programming of MapReduce jobs to be executed on Hadoop clusters. ... Pig is intended to handle all kinds of data, including structured and unstructured information and relational and nested data.

Spark is an open-source distributed general-purpose cluster-computing framework. Scala is a general-purpose programming language providing support for functional programming and a strong static type system. Thus, this is the fundamental difference between Spark and Scala.Oct 10, 2018

Apache Hadoop and Apache Spark are both open-source frameworks for big data processing with some key differences. Hadoop uses the MapReduce to process data, while Spark uses resilient distributed datasets (RDDs).

Yes, it is used by our data science and data engineering orgs. It is being used to build big data workflows (pipelines) for ETL and analytics. It provides easy and better alternatives to writing Java map-reduce code.

MapReduce Mode

It is the default mode. In this Pig renders Pig Latin into MapReduce jobs and executes them on the cluster. It can be executed against semi-distributed or fully distributed Hadoop installation.

• Hypertable is a promising upcoming alternative to Hadoop. It is under active development. Unlike Java based Hadoop, Hypertable is written in C++ for performance. It is sponsored and used by Zvents, Baidu, and Rediff.com.

• Examples of Hadoop. Here are five examples of Hadoop use cases: Financial services companies use analytics to assess risk, build investment models, and create trading algorithms; Hadoop has been used to help build and run those applications.

• Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

• The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a NameNode and DataNode architecture to implement a distributed file system that provides high-performance access to data across highly scalable Hadoop clusters.

Pig is a data flow language that performs data manipulation operations for Hadoop and analyzes a huge amount of data in an efficient manner using its Pig Latin Scripts. While Hive provides SQL like language, i.e. Hive query language for better querying and processing of data.

Step 1) Download the stable latest release of Pig Hadoop from any one of the mirrors sites available at Select tar.gz (and not src.tar.gz) file to download. Step 2) Once a download is complete, navigate to the directory containing the downloaded tar file and move the tar to the location where you want to setup Pig Hadoop.

This is managed by the Apache software foundation. It has a high-level scripting language known as pig Latin scripts that help programmers to focus on data level operation, and it implicitly manages the map-reduce processes for data computation. It efficiently interacts with the Hadoop distributed file system (HDFS).

Pig in Hadoop has two execution modes: Local mode: In this mode, Hadoop Pig language runs in a single JVM and makes use of local file system. This mode is suitable only for analysis of small datasets using Pig in Hadoop