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
Introduction of mesos persistent storageZhou Weitao
1. How to run stateful service against current Mesos-0.22
2. Disk isolation and monitoring
3. Persistent Volumes
4. Dynamic Reservations
5. What we can contribute for Mesos persistent storage
Introduction of mesos persistent storageZhou Weitao
1. How to run stateful service against current Mesos-0.22
2. Disk isolation and monitoring
3. Persistent Volumes
4. Dynamic Reservations
5. What we can contribute for Mesos persistent storage
Storage Systems for big data - HDFS, HBase, and intro to KV Store - RedisSameer Tiwari
There is a plethora of storage solutions for big data, each having its own pros and cons. The objective of this talk is to delve deeper into specific classes of storage types like Distributed File Systems, in-memory Key Value Stores, Big Table Stores and provide insights on how to choose the right storage solution for a specific class of problems. For instance, running large analytic workloads, iterative machine learning algorithms, and real time analytics.
The talk will cover HDFS, HBase and brief introduction to Redis
The Google Chubby lock service for loosely-coupled distributed systemsRomain Jacotin
The Google Chubby lock service presented in 2006 is the inspiration for Apache ZooKeeper: let's take a deep dive into Chubby to better understand ZooKeeper and distributed consensus.
Adobe has packaged HBase in Docker containers and uses Marathon and Mesos to schedule them—allowing them to decouple the HBase RegionServer from the host, express resource requirements declaratively, and open the door for unassisted real-time deployments, elastic (up and down) real-time scalability, and more.
PostgreSQL connections at scale was the presentation by our external speaker at our 8th opensource database meetup. The presentation helps you comprehend on database connections with its cost, gauge the need for a connection pooler, Pgbouncer overview with its features, monitoring, and deployment best practices.
Replication, Durability, and Disaster RecoverySteven Francia
This session introduces the basic components of high availability before going into a deep dive on MongoDB replication. We'll explore some of the advanced capabilities with MongoDB replication and best practices to ensure data durability and redundancy. We'll also look at various deployment scenarios and disaster recovery configurations.
Actually any people and employers get high available in your applications, maintain ours environment always available not is easy task. In Open Source World exist tools that maje it possible. This appresentation is a module course UTAH NETWORXS about Hight Available and Performance Course. Utah Networxs is business school in Sao Paulo Brazil Work a Linux System for more than 17 years. Maked to Fabio Pires Directory Utah Networxs and Linux Specialist focusing Clusters and HA services.
Redis is an open source in memory database which is easy to use. In this introductory presentation, several features will be discussed including use cases. The datatypes will be elaborated, publish subscribe features, persistence will be discussed including client implementations in Node and Spring Boot. After this presentation, you will have a basic understanding of what Redis is and you will have enough knowledge to get started with your first implementation!
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
Storage Systems for big data - HDFS, HBase, and intro to KV Store - RedisSameer Tiwari
There is a plethora of storage solutions for big data, each having its own pros and cons. The objective of this talk is to delve deeper into specific classes of storage types like Distributed File Systems, in-memory Key Value Stores, Big Table Stores and provide insights on how to choose the right storage solution for a specific class of problems. For instance, running large analytic workloads, iterative machine learning algorithms, and real time analytics.
The talk will cover HDFS, HBase and brief introduction to Redis
The Google Chubby lock service for loosely-coupled distributed systemsRomain Jacotin
The Google Chubby lock service presented in 2006 is the inspiration for Apache ZooKeeper: let's take a deep dive into Chubby to better understand ZooKeeper and distributed consensus.
Adobe has packaged HBase in Docker containers and uses Marathon and Mesos to schedule them—allowing them to decouple the HBase RegionServer from the host, express resource requirements declaratively, and open the door for unassisted real-time deployments, elastic (up and down) real-time scalability, and more.
PostgreSQL connections at scale was the presentation by our external speaker at our 8th opensource database meetup. The presentation helps you comprehend on database connections with its cost, gauge the need for a connection pooler, Pgbouncer overview with its features, monitoring, and deployment best practices.
Replication, Durability, and Disaster RecoverySteven Francia
This session introduces the basic components of high availability before going into a deep dive on MongoDB replication. We'll explore some of the advanced capabilities with MongoDB replication and best practices to ensure data durability and redundancy. We'll also look at various deployment scenarios and disaster recovery configurations.
Actually any people and employers get high available in your applications, maintain ours environment always available not is easy task. In Open Source World exist tools that maje it possible. This appresentation is a module course UTAH NETWORXS about Hight Available and Performance Course. Utah Networxs is business school in Sao Paulo Brazil Work a Linux System for more than 17 years. Maked to Fabio Pires Directory Utah Networxs and Linux Specialist focusing Clusters and HA services.
Redis is an open source in memory database which is easy to use. In this introductory presentation, several features will be discussed including use cases. The datatypes will be elaborated, publish subscribe features, persistence will be discussed including client implementations in Node and Spring Boot. After this presentation, you will have a basic understanding of what Redis is and you will have enough knowledge to get started with your first implementation!
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
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/
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.
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.
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of 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
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. 2
Big Data
Wikipedia Definition:
In information technology, big data is a loosely-
defined term used to describe data sets so large
and complex that they become awkward to work
with using on-hand database management tools.
3. 3
How Big is Big Data?
2008: Google processed 20 PB a day
2009: Facebook had 2.5 PB user data + 15
TB/day
2009: eBay had 6.5 PB user data + 50 TB/day
2011: Yahoo! had 180-200 PB of data
2012: Facebook ingests 500 TB/day
6. 6
But Parallel Processing is complicated
How do we assign tasks to workers?
What if we have more tasks than slots?
What happens when tasks fail?
How do you handle distributed synchronization?
8. 8
GFS to HDFS
It started when google researchers wrote a
paper on a distributed file system to resolve
storage and analysis issues of Big Data
The researchers proposed a file system named
Google File System which in turn, gave birth to
Hadoop Distributed File System (HDFS)
The paper on MapReduce resulted in
MapReduce programming structure
The paper on BigTable produced Hadoop
Hbase, Data warehouse schema over HDFS
10. 10
Key Features
Accesible
Hadoop runs on large clusters of commodity machines or on
cloud computing services such as Amazon's Elastic Compute
Cloud (EC2).
Robust
As Hadoop is intended to run on commodity hardware, It is
architected with the assumption of frequent hardware
malfunctions. It can gracefully handle most such failures.
Scalable
Hadoop scales linearly to handle larger data by adding more
nodes to the cluster.
Simple
Hadoop allows users to quickly write efficient parallel code.
13. 13
Hadoop Platforms
Platforms: Unix and on Windows.
Linux: the only supported production platform.
Other variants of Unix, like Mac OS X: run Hadoop for
development.
Windows + Cygwin: development platform (openssh)
Java 6
Java 1.6.x (aka 6.0.x aka 6) is recommended for
running Hadoop.
14. 14
Hadoop Modes
• Standalone (or local) mode
– There are no daemons running and everything runs in
a single JVM. Standalone mode is suitable for running
MapReduce programs during development, since it is
easy to test and debug them.
• Pseudo-distributed mode
– The Hadoop daemons run on the local machine, thus
simulating a cluster on a small scale.
• Fully distributed mode
– The Hadoop daemons run on a cluster of machines.
16. 16
Master-Slave Architecture
HDFS has a master-slave architecture.
The master node or the name node governs the cluster.
It takes care of tasks and resource allocation.
It stores all the metadata related to file breakage, block
storage, block replication and task execution status.
The slave nodes or the data nodes are the one which
stores all the data blocks and perform task executions
Tasktracker is the program which runs on each individual
data node and monitors the task execution over each
node.
Jobtracker runs on name node and monitors the
complete job execution.
18. 18
HDFS File Distribution
Name node stores metadata related to:
File split
Block allocation
Task allocation
Each file is split into data blocks. Default size is
64 Mb
Each data block is replicated on different data
node. The replication factor in configurable.
Default value is 3
19. 19
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
20. 20
Rack awareness
DN 1
DN 2
DN 3
DN 4
DN 5
DN 6
DN 7
DN 8
DN 9
DN 10
DN 11
DN 12
Rack 1 Rack 2 Rack 3
NameNode
File X=
Blk:A in
DN:1,5,6
Blk:B in
DN: 7, 10,
11
Rack 1 =
DN:1,2,3,4
Rack 2 =
DN:5,6,7,8
Rack 3 =
DN:9,10,11,
12
Switch Switch Switch
Data
block A
Data
block B
FILE X
21. 21
Rack awareness
HDFS is aware of the placement of each data
node and on the racks
To prevent data loss due to a complete rack
failure, Hadoop intelligently replicates each data
block onto other racks also
This helps HDSF to recover the data even if
complete rack of data node shuts down.
This information is stored in the name node.
22. 22
File Write in Hadoop
DN 1
DN 2
DN 3
DN 4
DN 9
DN 10
DN 11
DN 12
Rack 1 Rack 3
NameNode
File.txt=
Blk:A in
DN:1,5,6
Blk:B in
DN: 7, 10,
11
Blk C in…..
Switch Switch
Switch
Client
File.txt
[A , B, C]
Broken
down
using
Hadoop
client API
DN 5
DN 6
DN 7
DN 8
Rack 2
Switch
First block
in one rack
next blocks
in different
rack
Intelligent
storage of
data
Heartbeat
Request
Response
MetaData
Creation
Block A Write
23. 23
File Write in Hadoop
HDFS client system requests the name node to
write down a file onto HDFS.
It also provide the file size and other metadata
information to the name node.
Meanwhile, each slave node sends a heartbeat
signal to namenode telling it about their status
24. 24
File Write in Hadoop
The namenode tells the client system where to
store the data blocks
Also, it tells the data node to get ready for data
write.
After the data write procedure is complete the
data node sends a success message to both
client and name node.
25. 25
File Read in Hadoop
DN 1
DN 2
DN 3
DN 4
DN 9
DN 10
DN 11
DN 12
Rack 1 Rack 3
NameNode
File.txt=
Blk:A in
DN:1,5,6
Blk:B in
DN: 7, 10,
11
Blk C in…..
Switch Switch
Switch
Client
DN 5
DN 6
DN 7
DN 8
Rack 2
Switch
An
ordered
list of
nodes.
Heartbeat
Request
Response
27. 27
Re-replication
Missing Heartbeats signify lost Nodes
Name Node consults metadata, finds affected
data
Name Node consults Rack Awareness script
Name Node tells the Data node to re-replicate
28. 28
3 main configuration files
Core-site.xml
Contains configuration information that overrides the
default core Hadoop properties
Mapred-site.xml
Contains configuration information that overrides the
default core Mapreduce properties
Also defines the host and port that the MapReduce job
tracker runs at
Hdfs-site.xml
Mainly, to set the block replication factor
31. 31
Limitations of Hadoop -1
Scalability
Maximum Cluster size – 4,000 nodes for best
performance
Maximum Concurrent tasks- 40,000
Name Node as a single point of failure
Failure kills all running and queued jobs
Jobs need to be re-submitted by the user
Re-Start ability
Restart is very tricky due to complex state
32. 32
Who has the biggest cluster setups
Facebook 400
Microsoft 400
LinkedIn 4100
Yahoo 42,000
