Well illustrated with definitions of Apache Hive with its architecture workflows plus with the types of data available for Apache Hive
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Import Database Data using RODBC in R StudioRupak Roy
Well-documented instructions to access the database management systems using ODBC API with the benefit of processing R code in the database using database server resources.
Let me know if anything is required. Ping me @ google @bobrupakroy
Bundled with the documentation to the introduction of Apache Hbase to the configuration.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Get acquainted with the differences in scoop, the added advantages with hands-on implementation
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Well-defined introduction about working with Big Data and introduction to the Hadoop Ecosystem.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Import and Export Big Data using R StudioRupak Roy
Acknowledge R Studio working with BIg Data, Import & Export and R-Hadoop and distinguish between the base functions vs big data functions like read.csv.fffdf with optimized memory management.
Let me know if anything is required. Ping @ bobrupakroy
Get to know the configuration with Hadoop installation types and also handling of the HDFS files.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Apache Scoop - Import with Append mode and Last Modified mode Rupak Roy
Familiar with scoop advanced functions like import with append and last modified mode.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Import Database Data using RODBC in R StudioRupak Roy
Well-documented instructions to access the database management systems using ODBC API with the benefit of processing R code in the database using database server resources.
Let me know if anything is required. Ping me @ google @bobrupakroy
Bundled with the documentation to the introduction of Apache Hbase to the configuration.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Get acquainted with the differences in scoop, the added advantages with hands-on implementation
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Well-defined introduction about working with Big Data and introduction to the Hadoop Ecosystem.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Import and Export Big Data using R StudioRupak Roy
Acknowledge R Studio working with BIg Data, Import & Export and R-Hadoop and distinguish between the base functions vs big data functions like read.csv.fffdf with optimized memory management.
Let me know if anything is required. Ping @ bobrupakroy
Get to know the configuration with Hadoop installation types and also handling of the HDFS files.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Apache Scoop - Import with Append mode and Last Modified mode Rupak Roy
Familiar with scoop advanced functions like import with append and last modified mode.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Get acquainted with a distributed, reliable tool/service for collecting a large amount of streaming data to centralized storage with their architecture.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
take care!
Apache Sqoop Tutorial | Sqoop: Import & Export Data From MySQL To HDFS | Hado...Edureka!
** Hadoop Training: https://www.edureka.co/hadoop **
This Edureka PPT on Sqoop Tutorial will explain you the fundamentals of Apache Sqoop. It will also give you a brief idea on Sqoop Architecture. In the end, it will showcase a demo of data transfer between Mysql and Hadoop
Below topics are covered in this video:
1. Problems with RDBMS
2. Need for Apache Sqoop
3. Introduction to Sqoop
4. Apache Sqoop Architecture
5. Sqoop Commands
6. Demo to transfer data between Mysql and Hadoop
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introduction to Apache Hive | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2xkCd84
This CloudxLab Introduction to Hive tutorial helps you to understand Hive in detail. Below are the topics covered in this tutorial:
1) Hive Introduction
2) Why Do We Need Hive?
3) Hive - Components
4) Hive - Limitations
5) Hive - Data Types
6) Hive - Metastore
7) Hive - Warehouse
8) Accessing Hive using Command Line
9) Accessing Hive using Hue
10) Tables in Hive - Managed and External
11) Hive - Loading Data From Local Directory
12) Hive - Loading Data From HDFS
13) S3 Based External Tables in Hive
14) Hive - Select Statements
15) Hive - Aggregations
16) Saving Data in Hive
17) Hive Tables - DDL - ALTER
18) Partitions in Hive
19) Views in Hive
20) Load JSON Data
21) Sorting & Distributing - Order By, Sort By, Distribute By, Cluster By
22) Bucketing in Hive
23) Hive - ORC Files
24) Connecting to Tableau using Hive
25) Analyzing MovieLens Data using Hive
26) Hands-on demos on CloudxLab
Apache Spark - Loading & Saving data | Big Data Hadoop Spark Tutorial | Cloud...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2shXBpj
This CloudxLab Apache Spark - Loading & Saving data tutorial helps you to understand Loading & Saving data in Apache Spark in detail. Below are the topics covered in this tutorial:
1) Common Data Sources
2) Common Supported File Formats
3) Handling Text Files using Scala
4) Loading CSV
5) SequenceFiles
6) Object Files
7) Hadoop Input and Output Format - Old and New API
8) Protocol Buffers
9) File Compression
10) Handling LZO
Understand and implement the terminology of why partitioning the table is important and the Hive Query Language (HQL)
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
In this session you will learn:
PIG
PIG - Overview
Installation and Running Pig
Load in Pig
Macros in Pig
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
In Hive, tables and databases are created first and then data is loaded into these tables.
Hive as data warehouse designed for managing and querying only structured data that is stored in tables.
While dealing with structured data, Map Reduce doesn't have optimization and usability features like UDFs but Hive framework does.
Get acquainted with a distributed, reliable tool/service for collecting a large amount of streaming data to centralized storage with their architecture.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
take care!
Apache Sqoop Tutorial | Sqoop: Import & Export Data From MySQL To HDFS | Hado...Edureka!
** Hadoop Training: https://www.edureka.co/hadoop **
This Edureka PPT on Sqoop Tutorial will explain you the fundamentals of Apache Sqoop. It will also give you a brief idea on Sqoop Architecture. In the end, it will showcase a demo of data transfer between Mysql and Hadoop
Below topics are covered in this video:
1. Problems with RDBMS
2. Need for Apache Sqoop
3. Introduction to Sqoop
4. Apache Sqoop Architecture
5. Sqoop Commands
6. Demo to transfer data between Mysql and Hadoop
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introduction to Apache Hive | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2xkCd84
This CloudxLab Introduction to Hive tutorial helps you to understand Hive in detail. Below are the topics covered in this tutorial:
1) Hive Introduction
2) Why Do We Need Hive?
3) Hive - Components
4) Hive - Limitations
5) Hive - Data Types
6) Hive - Metastore
7) Hive - Warehouse
8) Accessing Hive using Command Line
9) Accessing Hive using Hue
10) Tables in Hive - Managed and External
11) Hive - Loading Data From Local Directory
12) Hive - Loading Data From HDFS
13) S3 Based External Tables in Hive
14) Hive - Select Statements
15) Hive - Aggregations
16) Saving Data in Hive
17) Hive Tables - DDL - ALTER
18) Partitions in Hive
19) Views in Hive
20) Load JSON Data
21) Sorting & Distributing - Order By, Sort By, Distribute By, Cluster By
22) Bucketing in Hive
23) Hive - ORC Files
24) Connecting to Tableau using Hive
25) Analyzing MovieLens Data using Hive
26) Hands-on demos on CloudxLab
Apache Spark - Loading & Saving data | Big Data Hadoop Spark Tutorial | Cloud...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2shXBpj
This CloudxLab Apache Spark - Loading & Saving data tutorial helps you to understand Loading & Saving data in Apache Spark in detail. Below are the topics covered in this tutorial:
1) Common Data Sources
2) Common Supported File Formats
3) Handling Text Files using Scala
4) Loading CSV
5) SequenceFiles
6) Object Files
7) Hadoop Input and Output Format - Old and New API
8) Protocol Buffers
9) File Compression
10) Handling LZO
Understand and implement the terminology of why partitioning the table is important and the Hive Query Language (HQL)
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
In this session you will learn:
PIG
PIG - Overview
Installation and Running Pig
Load in Pig
Macros in Pig
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
In Hive, tables and databases are created first and then data is loaded into these tables.
Hive as data warehouse designed for managing and querying only structured data that is stored in tables.
While dealing with structured data, Map Reduce doesn't have optimization and usability features like UDFs but Hive framework does.
It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
Interested in learning Hadoop, but you’re overwhelmed by the number of components in the Hadoop ecosystem? You’d like to get some hands on experience with Hadoop but you don’t know Linux or Java? This session will focus on giving a high level explanation of Hive and HiveQL and how you can use them to get started with Hadoop without knowing Linux or Java.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
The real estate market is one of the most competitive in terms of pricing, and as a result, prices tend to vary significantly based on a variety of factors. Forecasting property prices is an important module in decision making for both buyers and investors in supporting budget allocation, finding property finding stratagems, and determining suitable policies, making it one of the top fields to apply the concepts of machine learning to optimise and predict the prices with high accuracy.
The literature study provides a clear concept and will benefit any next endeavours. The majority of writers have come to the conclusion that artificial neural networks are more effective at forecasting the future, but in the actual world, there are other algorithms that should have been taken into account. In order to maximise profits, investors base their judgments on market trends. Developers are curious in future trends because it might help them weigh the advantages and downsides and assist them create new products.
This is the Day-4 lab exercise for CGI group webinar series. It primarily includes demonstrations on Hive, Analytics and other tools on the Cloudera Hadoop Platform.
Introduction to Apache Hadoop. Includes Hadoop v.1.0 and HDFS / MapReduce to v.2.0. Includes Impala, Yarn, Tez and the entire arsenal of projects for Apache Hadoop.
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016MLconf
Big Data Processing Above and Beyond Hadoop: Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
Hierarchical Clustering - Text Mining/NLPRupak Roy
Documented Hierarchical clustering using Hclust for text mining, natural language processing.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Clustering K means and Hierarchical - NLPRupak Roy
Classify to cluster the natural language processing via K means, Hierarchical and more.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Network Analysis using 3D interactive plots along with their steps for implementation.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Widely accepted steps for sentiment analysis.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Detailed Pattern Search using regular expressions using grepl, grep, grepexpr and Replace with sub, gsub and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Detailed documented with the definition of text mining along with challenges, implementing modeling techniques, word cloud and much more.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Installing Apache Hive, internal and external table, import-export Rupak Roy
Perform Hive installation with internal and external table import-export and much more
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Automate the complete big data process from import to export data from HDFS to RDBMS like sql with apache sqoop
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Enhance analysis with detailed examples of Relational Operators - II includes Foreash, Filter, Join, Co-Group, Union and much more.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Passing Parameters using File and Command LineRupak Roy
Explore well versed other functions, flatten operator and other available options to pass parameters
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Get to know the implementation of apache Pig relational operators like order, limit, distinct, groupby.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Get to know about casting of data from one to another type and reference field by position and much more
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Pig Latin, Data Model with Load and Store FunctionsRupak Roy
Documented with the two data types of PiG Data Model including Complex PIG data types in detail.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Well-versed explanation of apache pig for analyzing the massive amount of data with its components pig latin, execution environments, and the high-level language pig architecture.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
The Next generation of Hadoop version from the Apache Software Foundation with a detailed comparison of Map-Reduce V1 versus Yarn and the Architecture with important updates
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Talk soon!
Hands-on with data visualization using ggplot, gemo_density, facet_grid, and more with a case study.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2. Apache hive
Hive is a data warehouse platform built for hadoop
to provide data summarization, query analysis.
Using MapReduce of hadoop for query analysis is a
bit complicated, so in order to overcome the issue
Apache Hive came into the picture with its easy
structured query language-like in short: SQL-like that
gets translated into MapReduce job for query
analysis on Big Data.
We also got a name for Hive SQL–like that is Hive
Query Language or Hive QL or HQL in short.
Internally a compiler translates HiveQL statements
into a directed acyclic graph (DAG) of Mapreduce
which are transferred to hadoop for execution.
Finally, it gives a mechanism to setup structured onto
data stored in HDFS for faster processing the data.
Rupak Roy
3. Hive Architecture
Frist, we have the following user interfaces:
CLI : command Line interface
Web UI: web user interface
Thrift Server is an optional service that allows clients to access Hive to
execute jobs using variety of programming languages similar to JDBC or
ODBC protocols.
ODBC and JDBC driver: allows applications to connect to hive.
Both uses thrift to communicate with the Hive ecosystem.
Second comes the Driver, it passes/forwards the HiveQL statements and
stores the metadata generated during the execution of an HiveQL. It
comprises of 3 important functions:
Compiler: performs compilation of the HiveQL to Directed acyclic graph
(DAG) for hadoop MapReduce jobs
Optimizer: optimizes various transformations to get a optimized DAG.
Executor: it simply executes the tasks by interacting with the Hadoop
Ecosystems.
Lastly the Metastore: is basically stores meta data i.e. information about
the table format, schemas and location. MetaData helps the driver to
keep a track of the data distributed over the cluster.
Rupak Roy
5. HiveQL doesn’t strictly follows the full SQL-92
standard.
Because generally SQL for typical data provides
multiple inserts, updates and deletes, however in
HiveQL we can’t as the reason is it saved in HDFS
and we are aware that we cant update any
data inside the file in HDFS.
Alternative solution to this is we can upload new
fresh file using HiveQL.
Rupak Roy
6. Data Types of Apache Hive
Some of the Primitive data types.
1) Numeric Types:
TINYINT:1-byte size ranges from -128 to 127
SMALLINT: 2-byte size ranges from -32,768 to 32,767
INT:4-byte size
BIGINT : 8 byte size
FLOAT : 4-byte size
DOUBLE : 8-byte size
DECIMAL
Rupak Roy
7. Data Types of Apache Hive
2. Data/Time types
3. String Types: STRING, CHAR, VARCHAR
4. Others like BOOLEAN , BINARY
Complex Data Types:
1) Arrays: it is a collection of elements of same type.
Example: store the data like char[] arrary=(i,am,…..)
then,
elements can be called by
array[0] means i
array[1] means am
array[2] means array
Rupak Roy
8. Data Types of Apache Hive
2) STRUCTS: it’s a collection of elements of different
data types.
Example: student STRUCT(name:STIRNG,
Course:STRING, ID: TINYINT)
Now if we save the name as Emma, course as MBA
and ID as 782:
Then we can recall or access each different type
value by
student.name which will give us ‘Emma’
student.course which will give us ‘ MBA’
student.ID which will give us ‘782’;
Rupak Roy
9. Data Types of Apache Hive
MAP: is a collection of elements in Key-Value pairs.
The key-value pairs can be of different data
types.
Example: MAP < PRIMITIVE_type, data_type>
student MAP < STRING,TINYINT>
Now if we save the value in STRING(KEY) as ‘Emma’
Then TINYINT(VALUE) as 782
Then you can accessed by using
student[“Emma”] will give 782
Rupak Roy
10. Next
We will learn how to install Hive, import
export and more.
Rupak Roy