This document provides information about Hadoop and its components. It discusses that Big Data comprises large datasets that cannot be processed using traditional techniques due to huge volumes, high velocity, and variety of data. It then describes Hadoop as an open source framework that allows distributed processing of large datasets across computer clusters using simple programming models. Finally, it lists and provides brief descriptions of the main Hadoop components, such as Hadoop Distributed File System, MapReduce, HBase, Pig, Hive, Sqoop, and Spark.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
BigQuery best practices and recommendations to reduce costs with BI Engine, S...Márton Kodok
best practices and recommendations for tuning BI Engine for your existing BigQuery workloads for cheaper and faster queries. Learn how we at REEA are orchestrating BI Engine reservations, on a 5TB dataset, considered small for BigQuery but with big cost savings and accelerated queries. We are seeing many presentations for big enterprises, but now we are showcasing how our queries perform better with lower costs. We are going to address the top considerations when to turn on BI Engine, how to use cloud orchestration for making this an automatic process, and combined with BigQuery and Datastudio query complexity that might save precious development time, lower bills, faster queries.
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
Introduction to Apache NiFi dws19 DWS - DC 2019Timothy Spann
A quick introduction to Apache NiFi and it's ecosystem. Also a hands on demo on using processors, examining provenance, ingesting REST Feeds, XML, Cameras, Files, Running TensorFlow, Running Apache MXNet, integrating with Spark and Kafka. Storing to HDFS, HBase, Phoenix, Hive and S3.
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
BigQuery best practices and recommendations to reduce costs with BI Engine, S...Márton Kodok
best practices and recommendations for tuning BI Engine for your existing BigQuery workloads for cheaper and faster queries. Learn how we at REEA are orchestrating BI Engine reservations, on a 5TB dataset, considered small for BigQuery but with big cost savings and accelerated queries. We are seeing many presentations for big enterprises, but now we are showcasing how our queries perform better with lower costs. We are going to address the top considerations when to turn on BI Engine, how to use cloud orchestration for making this an automatic process, and combined with BigQuery and Datastudio query complexity that might save precious development time, lower bills, faster queries.
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
Introduction to Apache NiFi dws19 DWS - DC 2019Timothy Spann
A quick introduction to Apache NiFi and it's ecosystem. Also a hands on demo on using processors, examining provenance, ingesting REST Feeds, XML, Cameras, Files, Running TensorFlow, Running Apache MXNet, integrating with Spark and Kafka. Storing to HDFS, HBase, Phoenix, Hive and S3.
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
Building Data Quality Audit Framework using Delta Lake at CernerDatabricks
Cerner needs to know what assets it owns, where they are located, and the status of those assets. A configuration management system is an inventory of IT assets and IT things like servers, network devices, storage arrays, and software licenses.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?Kai Wähner
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems.
Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation.
The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
ELT vs. ETL - How they’re different and why it mattersMatillion
ELT is a fundamentally better way to load and transform your data. It’s faster. It’s more efficient. And Matillion’s browser-based interface makes it easier than ever to work with your data. You’re using data to improve your world: shouldn’t the tools you use return the favor?
In this webinar:
- Explore the differences between ELT and ETL
- Learn why ELT is a better, more modern process
- Discover the latest trends in ELT and how they apply to your business
- Find out how Matillion ETL makes loading large amounts of data easier
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and PrometheusManasi Vartak
These are slides from Manasi Vartak's Strata Talk in March 2020 on Robust MLOps with Open-Source.
* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
Data in Hadoop is getting bigger every day, consumers of the data are growing, organizations are now looking at making their Hadoop cluster compliant to federal regulations and commercial demands. Apache Ranger simplifies the management of security policies across all components in Hadoop. Ranger provides granular access controls to data.
The deck describes what security tools are available in Hadoop and their purpose then it moves on to discuss in detail Apache Ranger.
[EN] Building modern data pipeline with Snowflake + DBT + Airflow.pdfChris Hoyean Song
I'm posting the slide presented at the Snowflake user group meet up.
NFT Bank has introduced DBT to rebuild and operate the entire data pipeline from scratch.
Data quality control and monitoring are critical as data is at the core of the company.
You can manage your numerous data validation tests in organized way. You can add one data validation test with just single line of yaml.
You can build the data catalog and data lineage docs if you just implement your data pipeline on top of DBT without big effort.
---
Session 1: Data Quality & Productivity
Data Quality
Data Quality Validation
Data Catalog, Lineage Documentation
DBT Introduction
Session 2: Integrate DBT with Airflow
DBT Cloud or Airflow?
Astronomer Cosmos
dbt deps
Session 3: Cost Optimization
Query Optimization
Cost Monitoring
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
High-speed Database Throughput Using Apache Arrow Flight SQLScyllaDB
Flight SQL is a revolutionary new open database protocol designed for modern architectures. Key features in Flight SQL include a columnar-oriented design and native support for parallel processing of data partitions. This talk will go over how these new features can push SQL query throughput beyond existing standards such as ODBC.
DataOps is a methodology and culture shift that brings the successful combination of development and operations (DevOps) to data processing environments. It breaks down silos between developers, data scientists, and operators, resulting in lean data feature development processes with quick feedback. In this presentation, we will explain the methodology, and focus on practical aspects of DataOps.
Building Data Quality Audit Framework using Delta Lake at CernerDatabricks
Cerner needs to know what assets it owns, where they are located, and the status of those assets. A configuration management system is an inventory of IT assets and IT things like servers, network devices, storage arrays, and software licenses.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?Kai Wähner
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems.
Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation.
The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
ELT vs. ETL - How they’re different and why it mattersMatillion
ELT is a fundamentally better way to load and transform your data. It’s faster. It’s more efficient. And Matillion’s browser-based interface makes it easier than ever to work with your data. You’re using data to improve your world: shouldn’t the tools you use return the favor?
In this webinar:
- Explore the differences between ELT and ETL
- Learn why ELT is a better, more modern process
- Discover the latest trends in ELT and how they apply to your business
- Find out how Matillion ETL makes loading large amounts of data easier
Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and PrometheusManasi Vartak
These are slides from Manasi Vartak's Strata Talk in March 2020 on Robust MLOps with Open-Source.
* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
Data in Hadoop is getting bigger every day, consumers of the data are growing, organizations are now looking at making their Hadoop cluster compliant to federal regulations and commercial demands. Apache Ranger simplifies the management of security policies across all components in Hadoop. Ranger provides granular access controls to data.
The deck describes what security tools are available in Hadoop and their purpose then it moves on to discuss in detail Apache Ranger.
[EN] Building modern data pipeline with Snowflake + DBT + Airflow.pdfChris Hoyean Song
I'm posting the slide presented at the Snowflake user group meet up.
NFT Bank has introduced DBT to rebuild and operate the entire data pipeline from scratch.
Data quality control and monitoring are critical as data is at the core of the company.
You can manage your numerous data validation tests in organized way. You can add one data validation test with just single line of yaml.
You can build the data catalog and data lineage docs if you just implement your data pipeline on top of DBT without big effort.
---
Session 1: Data Quality & Productivity
Data Quality
Data Quality Validation
Data Catalog, Lineage Documentation
DBT Introduction
Session 2: Integrate DBT with Airflow
DBT Cloud or Airflow?
Astronomer Cosmos
dbt deps
Session 3: Cost Optimization
Query Optimization
Cost Monitoring
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
High-speed Database Throughput Using Apache Arrow Flight SQLScyllaDB
Flight SQL is a revolutionary new open database protocol designed for modern architectures. Key features in Flight SQL include a columnar-oriented design and native support for parallel processing of data partitions. This talk will go over how these new features can push SQL query throughput beyond existing standards such as ODBC.
DataOps is a methodology and culture shift that brings the successful combination of development and operations (DevOps) to data processing environments. It breaks down silos between developers, data scientists, and operators, resulting in lean data feature development processes with quick feedback. In this presentation, we will explain the methodology, and focus on practical aspects of DataOps.
A quick comparison of Hadoop and Apache Spark with a detailed introduction.
Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes. They do different things.
Looking for Similar IT Services?
Write to us business@altencalsoftlabs.com
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The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
Design and Research of Hadoop Distributed Cluster Based on RaspberryIJRESJOURNAL
ABSTRACT : Based on the cost saving, this Hadoop distributed cluster based on raspberry is designed for the storage and processing of massive data. This paper expounds the two core technologies in the Hadoop software framework - HDFS distributed file system architecture and MapReduce distributed processing mechanism. The construction method of the cluster is described in detail, and the Hadoop distributed cluster platform is successfully constructed based on the two raspberry factions. The technical knowledge about Hadoop is well understood in theory and practice.
Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets 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. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Apache Hadoop software library is essentially a framework that
allows for the distributed processing of large data-sets across
clusters of computers using a simple programming model.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
Multi-cluster Kubernetes Networking- Patterns, Projects and Guidelines
Big data-cheat-sheet
1. FURTHERMORE:
Big Data Hadoop Certification Training
BIG DATA HADOOP
CHEAT SHEETBig Data
Comprises of large datasets that cannot be processed using traditional computing
techniques, which includes huge volumes, high velocity and extensible variety of data.
Hadoop
An Apache open source framework written in JAVA which allows distributed processing
of large datasets across clusters of computers using simple programming models.
Hadoop Common
These are the JAVA libraries and utilities required by other Hadoop modules which
contains the necessary scripts and files required to start Hadoop
Hadoop YARN
A framework used for job scheduling and managing the cluster resources
Hadoop Distributed File System
A Java based file system that provides scalable and reliable data storage and it provides
high throughput access to the application data
Hadoop MapReduce
A software framework, which is used for writing the applications easily which process
big amount of data in parallel on large clusters
Apache hive
An infrastructure for data warehousing for Hadoop
Apache oozie
An application in Java responsible for scheduling Hadoop jobs
Apache Pig
A data flow platform that is responsible for the execution of the MapReduce jobs
Apache Spark
An open source framework used for cluster computing
Flume
An open source aggregation service responsible for collection and transport of data
from source to destination
Hbase
A column-oriented database of Hadoop that stores big data in a scalable way
Sqoop
An interface application that is used to transfer data between Hadoop and relational
database through commands
Hadoop Distributed File System
Map-Reduce
HBASE
Database with Real-
Time Access
PIG Hive Sqoop
ETL BI Reporting RDBMSTop Level
Interfaces
Top Level
Abstractions
Distributed
Data
Processing
At the base is a Self-
healing clustered
storage system
Different components of Hadoop Architecture involves:
Hadoop File Automation Commands
Commands Task Syntax
cat
Used to copy the source path to the
destination or the standard output
hdfsdfs –cat URI [URI- – -]
chgrp Used to change the group of the files hdfsdfs –chgrp [-R] GROUP URI [URI—]
chmod Used to change the permissions of the file
hdfsdfs –chmod [-R] <MODE[,MODE]- – -:
OCTALMODE> URI [URI – – -]
chown Used to change the owner of the file
hdfsdfs –chown [-
R][OWNER][:{GROUP]]URI[URI]
count Used to count the number of directories hdfs dfs –count [-q] <paths>
cp
Used to copy one or more than one files from
the source to destination path
hdfsdfs –cp URI[URI – – -]<dest>
Du Used to display the size of directories or files hdfsdfs –du [-s][-h]URI [URI – – -]
get Used to copy files to the local file system hdfs dfs –get[-ignorecrc][-crc]<src><localdst>
ls
Used to display the statistics of any file or
directory
hdfsdfs –ls <args>
mkdir Used to create one or more directories hdfsdfs –mkdir<path>
mv
Used to move one or more files from one
location to other
hdfs dfs –mv URI[URI – – -]<dest>
put Used to read from one file system to other hdfsdfs –put<localsrc>- – -<dest>
rm Used to delete one or more than one files hdfsdfs –rmr[-skipTrash]URI[URI- – – ]
stat
Used to display the information of any specific
path
hdfsdfs –stat URI[URI – – -]
help
Used to display the usage information of the
command
help<cmd-name>
Commands Task
Balancer To run cluster balancing utility
daemonlog To get or set the log level of each daemon
dfsadmin To run many HDFS administrative operations
Datanode To run HDFS datanode service
mradmin
To run a number of mapReduce administrative
operations
Jobtracker To run mapReduce job tracker
Namenode To run name node
Tasktracker To run mapReduce task tracker node
Secondary namenode To run secondary namenode
Hadoop Administration Commands
• Learn from industry experts and be sought-after by the industry!
• Learn any technology, show exemplary skills and have an
unmatched career!
• The most trending technology courses to help you fast-track your
career!
• Logical modules for both beginners and mid-level learners
• All recorded sessions available in LMS for lifetime
• 24*7 Support for Lifetime
• Learn Anytime, Anywhere
![FURTHERMORE:
Big Data Hadoop Certification Training
BIG DATA HADOOP
CHEAT SHEETBig Data
Comprises of large datasets that cannot be processed using traditional computing
techniques, which includes huge volumes, high velocity and extensible variety of data.
Hadoop
An Apache open source framework written in JAVA which allows distributed processing
of large datasets across clusters of computers using simple programming models.
Hadoop Common
These are the JAVA libraries and utilities required by other Hadoop modules which
contains the necessary scripts and files required to start Hadoop
Hadoop YARN
A framework used for job scheduling and managing the cluster resources
Hadoop Distributed File System
A Java based file system that provides scalable and reliable data storage and it provides
high throughput access to the application data
Hadoop MapReduce
A software framework, which is used for writing the applications easily which process
big amount of data in parallel on large clusters
Apache hive
An infrastructure for data warehousing for Hadoop
Apache oozie
An application in Java responsible for scheduling Hadoop jobs
Apache Pig
A data flow platform that is responsible for the execution of the MapReduce jobs
Apache Spark
An open source framework used for cluster computing
Flume
An open source aggregation service responsible for collection and transport of data
from source to destination
Hbase
A column-oriented database of Hadoop that stores big data in a scalable way
Sqoop
An interface application that is used to transfer data between Hadoop and relational
database through commands
Hadoop Distributed File System
Map-Reduce
HBASE
Database with Real-
Time Access
PIG Hive Sqoop
ETL BI Reporting RDBMSTop Level
Interfaces
Top Level
Abstractions
Distributed
Data
Processing
At the base is a Self-
healing clustered
storage system
Different components of Hadoop Architecture involves:
Hadoop File Automation Commands
Commands Task Syntax
cat
Used to copy the source path to the
destination or the standard output
hdfsdfs –cat URI [URI- – -]
chgrp Used to change the group of the files hdfsdfs –chgrp [-R] GROUP URI [URI—]
chmod Used to change the permissions of the file
hdfsdfs –chmod [-R] <MODE[,MODE]- – -:
OCTALMODE> URI [URI – – -]
chown Used to change the owner of the file
hdfsdfs –chown [-
R][OWNER][:{GROUP]]URI[URI]
count Used to count the number of directories hdfs dfs –count [-q] <paths>
cp
Used to copy one or more than one files from
the source to destination path
hdfsdfs –cp URI[URI – – -]<dest>
Du Used to display the size of directories or files hdfsdfs –du [-s][-h]URI [URI – – -]
get Used to copy files to the local file system hdfs dfs –get[-ignorecrc][-crc]<src><localdst>
ls
Used to display the statistics of any file or
directory
hdfsdfs –ls <args>
mkdir Used to create one or more directories hdfsdfs –mkdir<path>
mv
Used to move one or more files from one
location to other
hdfs dfs –mv URI[URI – – -]<dest>
put Used to read from one file system to other hdfsdfs –put<localsrc>- – -<dest>
rm Used to delete one or more than one files hdfsdfs –rmr[-skipTrash]URI[URI- – – ]
stat
Used to display the information of any specific
path
hdfsdfs –stat URI[URI – – -]
help
Used to display the usage information of the
command
help<cmd-name>
Commands Task
Balancer To run cluster balancing utility
daemonlog To get or set the log level of each daemon
dfsadmin To run many HDFS administrative operations
Datanode To run HDFS datanode service
mradmin
To run a number of mapReduce administrative
operations
Jobtracker To run mapReduce job tracker
Namenode To run name node
Tasktracker To run mapReduce task tracker node
Secondary namenode To run secondary namenode
Hadoop Administration Commands
• Learn from industry experts and be sought-after by the industry!
• Learn any technology, show exemplary skills and have an
unmatched career!
• The most trending technology courses to help you fast-track your
career!
• Logical modules for both beginners and mid-level learners
• All recorded sessions available in LMS for lifetime
• 24*7 Support for Lifetime
• Learn Anytime, Anywhere](https://image.slidesharecdn.com/big-data-cheat-sheet-190822040939/85/Big-data-cheat-sheet-1-320.jpg)
