HDFS is a distributed file system designed for storing very large data sets reliably and efficiently across commodity hardware. It has three main components - the NameNode, Secondary NameNode, and DataNodes. The NameNode manages the file system namespace and regulates access to files. DataNodes store and retrieve blocks when requested by clients. HDFS provides reliable storage through replication of blocks across DataNodes and detects hardware failures to ensure data is not lost. It is highly scalable, fault-tolerant, and suitable for applications processing large datasets.
This presentation covers the following topics:
1. HBase versions and origins
2. HBase core concepts
3. HBase vs. RDBMS
4. Data Modeling
5. HBase architecture
6. HBase Master and Region Servers
7. Column Families and Regions
8. HBase Internals: Bloom Filters and Block Indexes
9. Write Pipeline / Read Pipeline
10. Compactions
11. Learning Resources
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The core of Apache Hadoop consists of a storage part (HDFS) and a processing part (MapReduce).
Learn how to setup Samba and NFS in ubuntu server-ubuntu client and ubuntu server-windows client. Also, comparsion of NAS vs SAN, NAS vs DAS, why we are using NAS, its comonents and challanges with actual real world scenario that what if we use NAS and what if we not use NAS.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
In this lecture we analyze key-values databases. At first we introduce key-value characteristics, advantages and disadvantages.
Then we analyze the major Key-Value data stores and finally we discuss about Dynamo DB.
In particular we consider how Dynamo DB: How is implemented
1. Motivation Background
2. Partitioning: Consistent Hashing
3. High Availability for writes: Vector Clocks
4. Handling temporary failures: Sloppy Quorum
5. Recovering from failures: Merkle Trees
6. Membership and failure detection: Gossip Protocol
This presentation covers the following topics:
1. HBase versions and origins
2. HBase core concepts
3. HBase vs. RDBMS
4. Data Modeling
5. HBase architecture
6. HBase Master and Region Servers
7. Column Families and Regions
8. HBase Internals: Bloom Filters and Block Indexes
9. Write Pipeline / Read Pipeline
10. Compactions
11. Learning Resources
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The core of Apache Hadoop consists of a storage part (HDFS) and a processing part (MapReduce).
Learn how to setup Samba and NFS in ubuntu server-ubuntu client and ubuntu server-windows client. Also, comparsion of NAS vs SAN, NAS vs DAS, why we are using NAS, its comonents and challanges with actual real world scenario that what if we use NAS and what if we not use NAS.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
In this lecture we analyze key-values databases. At first we introduce key-value characteristics, advantages and disadvantages.
Then we analyze the major Key-Value data stores and finally we discuss about Dynamo DB.
In particular we consider how Dynamo DB: How is implemented
1. Motivation Background
2. Partitioning: Consistent Hashing
3. High Availability for writes: Vector Clocks
4. Handling temporary failures: Sloppy Quorum
5. Recovering from failures: Merkle Trees
6. Membership and failure detection: Gossip Protocol
This is an updated version of Amr's Hadoop presentation. Amr gave this talk recently at NASA CIDU event, TDWI LA Chapter, and also Netflix HQ. You should watch the powerpoint version as it has animations. The slides also include handout notes with additional information.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
This is an updated version of Amr's Hadoop presentation. Amr gave this talk recently at NASA CIDU event, TDWI LA Chapter, and also Netflix HQ. You should watch the powerpoint version as it has animations. The slides also include handout notes with additional information.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Hadoop Interview Questions and Answers by rohit kapakapa rohit
Hadoop Interview Questions and Answers - More than 130 real time questions and answers covering hadoop hdfs,mapreduce and administrative concepts by rohit kapa
A brief introduction to Hadoop distributed file system. How a file is broken into blocks, written and replicated on HDFS. How missing replicas are taken care of. How a job is launched and its status is checked. Some advantages and disadvantages of HDFS-1.x
Here is our most popular Hadoop Interview Questions and Answers from our Hadoop Developer Interview Guide. Hadoop Developer Interview Guide has over 100 REAL Hadoop Developer Interview Questions with detailed answers and illustrations asked in REAL interviews. The Hadoop Interview Questions listed in the guide are not "might be" asked interview question, they were asked in interviews at least once.
Hadoop Interview Questions and Answers | Big Data Interview Questions | Hadoo...Edureka!
This Hadoop Tutorial on Hadoop Interview Questions and Answers ( Hadoop Interview Blog series: https://goo.gl/ndqlss ) will help you to prepare yourself for Big Data and Hadoop interviews. Learn about the most important Hadoop interview questions and answers and know what will set you apart in the interview process. Below are the topics covered in this Hadoop Interview Questions and Answers Tutorial:
Hadoop Interview Questions on:
1) Big Data & Hadoop
2) HDFS
3) MapReduce
4) Apache Hive
5) Apache Pig
6) Apache HBase and Sqoop
Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
#HadoopInterviewQuestions #BigDataInterviewQuestions #HadoopInterview
In this session you will learn:
1. History of hadoop
2. Hadoop Ecosystem
3. Hadoop Animal Planet
4. What is Hadoop?
5. Distinctions of hadoop
6. Hadoop Components
7. The Hadoop Distributed Filesystem
8. Design of HDFS
9. When Not to use Hadoop?
10. HDFS Concepts
11. Anatomy of a File Read
12. Anatomy of a File Write
13. Replication & Rack awareness
14. Mapreduce Components
15. Typical Mapreduce Job
In this session you will learn:
History of Hadoop
Hadoop Ecosystem
Hadoop Animal Planet
What is Hadoop?
Distinctions of Hadoop
Hadoop Components
The Hadoop Distributed Filesystem
Design of HDFS
When Not to use Hadoop?
HDFS Concepts
Anatomy of a File Read
Anatomy of a File Write
Replication & Rack awareness
Mapreduce Components
Typical Mapreduce Job
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
Hadoop Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Enroll Free Live demo of Hadoop online training and big data analytics courses online and become certified data analyst/ Hadoop developer. Get online Hadoop training & certification.
HADOOP online training by Keylabstraining is excellent and teached by real time faculty. Our Hadoop Big Data course content designed as per the current IT industry requirement. Apache Hadoop is having very good demand in the market, huge number of job openings are there in the IT world. Based on this demand, Keylabstrainings has started providing online classes on Hadoop training through the various online training methods like Gotomeeting.
For more information Contact us : info@keylabstraining.com
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
2. Topics Covered
Big Data and Hadoop Introduction
HDFS Introduction
HDFS Definition
HDFS Components
Architecture of HDFS
Understanding the File System
Read and Write in HDFS
HDFS CLI
Summary
2
3. What is Big Data ?
Big Data refers to datasets that grow so large that it is difficult to
capture, store, manage, share, analyze and visualize with the
typical database software tools.
Big Data actually comes in complex, unstructured formats,
mostly everything from web sites, social media and email, to
videos, Data-warehouses and Scientific world.
Four Vs are that make a data so challenging to classified as BIG
DATA are
Volume Velocity Variety Value
3
4. What is Hadoop?
It is an Apache Software Foundation project
• Framework for running applications on large clusters
• Modeled after Google’s MapReduce / GFS framework
• Implemented in Java
A software platform that lets one easily write and run applications
that process vast amounts of data. It includes:
– MapReduce – offline computing engine
– HDFS – Hadoop distributed file system
Here's what makes it especially useful for:
Scalable: It can reliably store and process petabytes.
Economical: It distributes the data and processing across clusters of
commonly available computers (in thousands).
Efficient: By distributing the data, it can process it in parallel on the nodes
where the data is located.
Reliable: It automatically maintains multiple copies of data and automatically
redeploys computing tasks based on failures.
4
5. Why Hadoop?
Handle partial hardware failures without going down:
− If machine fails, we should be switch over to stand by machine
− If disk fails – use RAID or mirror disk
Fault Tolerance:
Regular backups
Logging
Mirror database at different site
Elasticity of resources:
Increase capacity without restarting the whole system (PureScale)
More computing power should equal to faster processing
Result consistency:
Answer should be consistent (independent of something failing) and returned in
reasonable amount of time
5
6. HDFS -Introduction
●
Hadoop Distributed File System (HDFS)
●
Based on Google File System
●
Google file system was derrived from 'bigfiles' paper authored by
Larry and Sergey in Stanford
●
Hadoop provides a distributed filesystem and a framework for the
analysis and transformation of very large data sets using the
MapReduce paradigm
●
The interface to HDFS is patterned after the Unix filesystem
●
Other distributed file system types are
PVFS,Lustre,GFS,KDFS,FTP,Amazon S3
6
7. HDFS – Goals and Assumptions
●
Hardware Failure
●
Streaming Data Access
●
Large Data Sets
●
Simple Coherency Model
●
“Moving Computation is Cheaper than Moving Data”
●
Portability Across Heterogeneous Hardware and Software
Platforms
7
8. HDFS Definition
– The Hadoop Distributed File System (HDFS) is a distributed file system
designed to run on commodity hardware.
– HDFS is a distributed, scalable, and portable filesystem written in Java
for the Hadoop framework.
– HDFS is the primary storage system used by Hadoop applications.
– HDFS is highly fault-tolerant and is designed to be deployed on low-cost
hardware.
– HDFS provides high throughput access to application data and is
suitable for applications that have large data sets
HDFS consists of following components (daemons)
•HDFS Master “Namenode”
•HDFS Workers “Datanodes”
•Secondary Name Node
8
9. HDFS Components
Namenode:
NameNode, a master server, manages the file system namespace and regulates access to files by clients.
Meta-data in Memory
– The entire metadata is in main memory
Types of Metadata
– List of files
– List of Blocks for each file
– List of DataNodes for each block
– File attributes, e.g creation time, replication factor
A Transaction Log
– Records file creations, file deletions. Etc
Data Node:
DataNodes, one per node in the cluster, manages storage attached to the nodes that they run on
A Block Server
– Stores data in the local file system (e.g. ext3)
– Stores meta-data of a block (e.g. CRC)
– Serves data and meta-data to Clients
– Block Report
– Periodically sends a report of all existing blocks to the NameNode
Facilitates Pipelining of Data
– Forwards data to other specified DataNodes 9
10. HDFS Components
Secondary Name Node
– Not used as hot stand-by or mirror node. Failover node is in future release.
– Will be renamed in 0.21 to CheckNode
– Bakup nameNode periodically wakes up and processes check point and updates
the nameNode
– Memory requirements are the same as nameNode (big)
– Typically on a separate machine in large cluster ( > 10 nodes)
– Directory is same as nameNode except it keeps previous checkpoint version in
addition to current.
– It can be used to restore failed nameNode (just copy current directory to new
nameNode)
10
11. HDFS Block
– Large data sets are divide into small chunks for easy processing.
– Default is 64 MB
– Can be increased more to 128 MB
– Reason for this default size and how it effects HDFS
11
14. Understanding the File system
Block placement
• Current Strategy
− One replica on local node
− Second replica on a remote rack
− Third replica on same remote rack
− Additional replicas are randomly placed
• Clients read from nearest replica
Data Correctness
• Use Checksums to validate data
− Use CRC32
• File Creation
− Client computes checksum per 512 byte
− DataNode stores the checksum
• File access
− Client retrieves the data and checksum from DataNode
− If Validation fails, Client tries other replicas 14
15. Understanding the File system
Data pipelining
− Client retrieves a list of DataNodes on which to place replicas of a block
− Client writes block to the first DataNode
− The first DataNode forwards the data to the next DataNode in the Pipeline
− When all replicas are written, the Client moves on to write the next block in file
Rebalancer
– Goal: % of disk occupied on Datanodes should be similar
− Usually run when new Datanodes are added
− Cluster is online when Rebalancer is active
− Rebalancer is throttled to avoid network congestion
− Command line tool
15
34. Command Line interface
– HDFS has a UNIX based command line interface and we have to access this using
HDFS using this CLI.
– HDFS can also be accessed through a web interface but its limit is only for viewing
HDFS contents.
– We will go through this part in detail in Practical sessions.
– Below are few examples of CLI based operations
hadoop fs -mkdir /input
hadoop fs -copyFromLocal input/docs/tweets.txt /input/tweets.txt
hadoop fs -put input/docs/tweets.txt /input/tweets.txt
hadoop fs -ls /input
hadoop fs -rmr /input
35. Resource
● Apache Hadoop Wiki
● Bradhed Lund Website(special thanks for making easy to
understand HDFS in real time)