Hadoop was developed to solve problems with data warehousing systems at Yahoo and Facebook that were limited in processing large amounts of raw data in real-time. Hadoop uses HDFS for scalable storage and MapReduce for distributed processing. It allows for agile access to raw data at scale for ad-hoc queries, data mining and analytics without being constrained by traditional database schemas. Hadoop has been widely adopted for large-scale data processing and analytics across many companies.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart 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. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
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
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart 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. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
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 introduction to Apache Hive the following topics are covered:
1. Hive Origin
2. Hive philosophy and architecture
3. Hive vs. RDBMS
4. HiveQL and Hive Shell
5. Managing tables
6. Data types and schemas
7. Querying data
8. HiveODBC
9. Resources
Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Ha...Edureka!
This Edureka "Hadoop tutorial For Beginners" ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand the problem with traditional system while processing Big Data and how Hadoop solves it. This tutorial will provide you a comprehensive idea about HDFS and YARN along with their architecture that has been explained in a very simple manner using examples and practical demonstration. At the end, you will get to know how to analyze Olympic data set using Hadoop and gain useful insights.
Below are the topics covered in this tutorial:
1. Big Data Growth Drivers
2. What is Big Data?
3. Hadoop Introduction
4. Hadoop Master/Slave Architecture
5. Hadoop Core Components
6. HDFS Data Blocks
7. HDFS Read/Write Mechanism
8. What is MapReduce
9. MapReduce Program
10. MapReduce Job Workflow
11. Hadoop Ecosystem
12. Hadoop Use Case: Analyzing Olympic Dataset
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
What is HDFS | Hadoop Distributed File System | EdurekaEdureka!
( Hadoop Training: https://www.edureka.co/hadoop )
This What is HDFS PPT will help you to understand about Hadoop Distributed File System and its features along with practical. In this What is HDFS PPT, we will cover:
1. What is DFS and Why Do We Need It?
2. What is HDFS?
3. HDFS Architecture
4. HDFS Replication Factor
5. HDFS Commands Demonstration on a Production Hadoop Cluster
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
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
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HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
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
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.
this presentation describes the company from where I did my summer training and what is bigdata why we use big data, big data challenges, the issue in big data, the solution of big data issues, hadoop, docker , Ansible etc.
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
In this introduction to Apache Hive the following topics are covered:
1. Hive Origin
2. Hive philosophy and architecture
3. Hive vs. RDBMS
4. HiveQL and Hive Shell
5. Managing tables
6. Data types and schemas
7. Querying data
8. HiveODBC
9. Resources
Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Ha...Edureka!
This Edureka "Hadoop tutorial For Beginners" ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand the problem with traditional system while processing Big Data and how Hadoop solves it. This tutorial will provide you a comprehensive idea about HDFS and YARN along with their architecture that has been explained in a very simple manner using examples and practical demonstration. At the end, you will get to know how to analyze Olympic data set using Hadoop and gain useful insights.
Below are the topics covered in this tutorial:
1. Big Data Growth Drivers
2. What is Big Data?
3. Hadoop Introduction
4. Hadoop Master/Slave Architecture
5. Hadoop Core Components
6. HDFS Data Blocks
7. HDFS Read/Write Mechanism
8. What is MapReduce
9. MapReduce Program
10. MapReduce Job Workflow
11. Hadoop Ecosystem
12. Hadoop Use Case: Analyzing Olympic Dataset
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
What is HDFS | Hadoop Distributed File System | EdurekaEdureka!
( Hadoop Training: https://www.edureka.co/hadoop )
This What is HDFS PPT will help you to understand about Hadoop Distributed File System and its features along with practical. In this What is HDFS PPT, we will cover:
1. What is DFS and Why Do We Need It?
2. What is HDFS?
3. HDFS Architecture
4. HDFS Replication Factor
5. HDFS Commands Demonstration on a Production Hadoop Cluster
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
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
r packagesdata analytics study material;
learn data analytics online;
data analytics courses;
courses for data analysis;
courses for data analytics;
online data analysis courses;
courses on data analysis;
data analytics classes;
data analysis training courses online;
courses in data analysis;
data analysis courses online;
data analytics training;
courses for data analyst;
data analysis online course;
data analysis certification;
data analysis courses;
data analysis classes;
online course data analysis;
learn data analysis online;
data analysis training;
python for data analysis course;
learn data analytics;
study data analytics;
how to learn data analytics;
data analysis course free;
statistical methods and data analysis;
big data analytics;
data analysis companies;
python data analysis course;
tools that can be used to analyse data;
data analysis consulting;
basic data analytics;
data analysis programs;
examples of data analysis tools;
big data analysis tools;
data analytics tools and techniques;
statistics for data analytics;
data analytics tools;
data analytics and big data;
data analytics big data;
data analysis software;
data analytics with excel;
website data analysis;
data analytics companies;
data analysis qualifications;
tools for data analytics;
data analysis tools;
qualitative data analysis software;
free data analytics;
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tools for analyzing data;
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free data analysis software;
tools for analysing data;
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learn data analysis;
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it data analytics;
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unstructured data analytics;
data analytics using excel;
dissertation data analysis;
sample of data analysis;
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data analytics;
tools of data analysis;
analytical tools for data analysis;
statistical tools to analyse data;
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statistical technique for data analysis;
tools for data analysis;
how to learn data analysis;
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excel data analytics;
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statistical analysis software;
tools to analyse data;
online data analysis;
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data analyst tools;
business data analysis;
tools and techniques of data analysis;
education data analysis;
advanced data analytics;
study data analysis;
spreadsheet data analysis;
learn data analysis in excel;
software for data analysis;
shared data warehouse;
what are data analysis tools;
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HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
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
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.
this presentation describes the company from where I did my summer training and what is bigdata why we use big data, big data challenges, the issue in big data, the solution of big data issues, hadoop, docker , Ansible etc.
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
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.
Big-Data Hadoop Tutorials - MindScripts Technologies, Pune amrutupre
MindScripts Technologies, is the leading Big-Data Hadoop Training institutes in Pune, providing a complete Big-Data Hadoop Course with Cloud-Era certification.
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
ارائه در زمینه کلان داده،
کارگاه آموزشی "عصر کلان داده، چرا و چگونه؟" در بیست و دومین کنفرانس انجمن کامپیوتر ایران csicc2017.ir
وحید امیری
vahidamiry.ir
datastack.ir
This is the first time I introduced the concept of Schema-on-Read vs Schema-on-Write to the public. It was at Berkeley EECS RAD Lab retreat Open Mic Session on May 28th, 2009 at Santa Cruz, California.
This is a talk that I gave at Stanford's EE203 (Entrepreneurial Engineer) on Tuesday Feb 9th, 2010. It covers my experience at Stanford, VivaSmart, Yahoo, Accel Partners, and Cloudera.
Service Primitives for Internet Scale ApplicationsAmr Awadallah
A general framework to describe internet scale applications and characterize the functional properties that can be traded away to improve the following operational metrics:
* Throughput (how many user requests/sec?)
* Interactivity (latency, how fast user requests finish?)
* Availability (% of time user perceives service as up), including fast recovery to improve availability
* TCO (Total Cost of Ownership)
Applications of Virtual Machine Monitors for Scalable, Reliable, and Interact...Amr Awadallah
My PhD oral defense.
An overlay network of VMMs (the vMatrix) which enables backward-compatible improvement of the scalability, reliability, and interactivity of Internet services.
Three applications demonstrated:
1. Dynamic Content Distribution
2. Server Switching
3. Fair placement of Game Servers
I was meaning to put this talk up for grabs for some time now, but kept forgetting. I was invited to give the keynote speech for the Microstrategy World 2008 conference. The talk was very well received, so here it is.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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
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
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
How Hadoop Revolutionized Data Warehousing at Yahoo and Facebook
1. Amr Awadallah CTO, Cloudera, Inc. August 5, 2009 How Hadoop Revolutionized Data Warehousing at Yahoo and Facebook
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3. Our Older Systems Limited Raw Data Access Storage Farm for Unstructured Data (20TB/day) Instrumentation Collection RDBMS (200GB/day) BI / Reports Mostly Append Ad hoc Queries & Data Mining ETL Grid Non-Consumption Filer heads are a bottleneck
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6. The Solution: A Store-Compute Grid Storage + Computation Instrumentation Collection RDBMS Interactive Apps “ Batch” Apps Mostly Append ETL and Aggregations Ad hoc Queries & Data Mining
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10. HDFS: Hadoop Distributed File System Block Size = 64MB Replication Factor = 3 Cost/GB is a few ¢/month vs $/month
12. MapReduce Example for Word Count cat *.txt | mapper.pl | sort | reducer.pl > out.txt Split 1 Split i Split N Map 1 (docid, text) (docid, text) Map i (docid, text) Map M Reduce 1 Output File 1 (sorted words, sum of counts) Reduce i Output File i (sorted words, sum of counts) Reduce R Output File R (sorted words, sum of counts) (words, counts) (sorted words, counts) Map (in_key, in_value) => list of (out_key, intermediate_value) Reduce (out_key, list of intermediate_values) => out_value(s) Shuffle (words, counts) (sorted words, counts) “ To Be Or Not To Be?” Be, 5 Be, 12 Be, 7 Be, 6 Be, 30
24. Hadoop High-Level Architecture Name Node Maintains mapping of file blocks to data node slaves Job Tracker Schedules jobs across task tracker slaves Data Node Stores and serves blocks of data Hadoop Client Contacts Name Node for data or Job Tracker to submit jobs Task Tracker Runs tasks (work units) within a job Share Physical Node
Editor's Notes
Data-As-Product is also referred to as Active DW, Operational BI, Online BI, etc.
The solution is to *augment* the current RDBMSes with a “smart” storage/processing system. The original event level data is kept in this smart storage layer and can be mined as needed. The aggregate data is kept in the RDBMSes for interactive reporting and analytics.
The system is self-healing in the sense that it automatically routes around failure. If a node fails then its workload and data are transparently shifted some where else. The system is intelligent in the sense that the MapReduce scheduler optimizes for the processing to happen on the same node storing the associated data (or co-located on the same leaf Ethernet switch), it also speculatively executes redundant tasks if certain nodes are detected to be slow. One of the key benefits of Hadoop is the ability to just upload any unstructured files to it without having to “schematize” them first. You can dump any type of data into Hadoop then the input record readers will abstract it out as if it was structured (i.e. schema on read vs on write) Open Source Software allows for innovation by partners and customers. It also enables third-party inspection of source code which provides assurances on security and product quality. 1 HDD = 75 MB/sec, 1000 HDDs = 75 GB/sec, the “head of fileserver” bottleneck is eliminated.
Speculative Execution, Data rebalancing, Background Checksumming, etc.
Pool commodity servers in a single hierarchical namespace. Designed for large files that are written once and read many times. Example here shows what happens with a replication factor of 3, each data block is present in at least 3 separate data nodes. Typical Hadoop node is eight cores with 16GB ram and four 1TB SATA disks. Default block size is 64MB, though most folks now set it to 128MB
Differentiate between MapReduce the platform and MapReduce the programming model. The analogy is similar to the RDBMs which executes the queries, and SQL which is the language for the queries. MapReduce can run on top of HDFS or a selection of other storage systems Intelligent scheduling algorithms for locality, sharing, and resource optimization.
Think: SELECT word, count(*) FROM documents GROUP BY word Checkout ParBASH: http://cloud-dev.blogspot.com/2009/06/introduction-to-parbash.html
Other uses like face recognition, document discovery, OCR, gene sequence alignment, etc. Data Mining: ** Search and Text Analytics ** Clustering/Categorization ** Modeling/Machine Learning ** Optimization/Operations Research ** Response Prediction/Forecasting ** Simulation, Monte-Carlo like. ** Random Walks of Connectivity Graphs
HBase: Low Latency Random-Access with per-row consistency for updates/inserts/deletes
First bullet is like assembly, then it gets higher level from there.
Query: SELECT, FROM, WHERE, JOIN, GROUP BY, SORT BY, LIMIT, DISTINCT, UNION ALL Join: LEFT, RIGHT, FULL, OUTER, INNER DDL: CREATE TABLE, ALTER TABLE, DROP TABLE, DROP PARTITION, SHOW TABLES, SHOW PARTITIONS DML: LOAD DATA INTO, FROM INSERT Types: TINYINT, INT, BIGINT, BOOLEAN, DOUBLE, STRING, ARRAY, MAP, STRUCT, JSON OBJECT Query: Subqueries in FROM, User Defined Functions, User Defined Aggregates, Sampling (TABLESAMPLE) Relational: IS NULL, IS NOT NULL, LIKE, REGEXP Built in aggregates: COUNT, MAX, MIN, AVG, SUM Built in functions: CAST, IF, REGEXP_REPLACE, … Other: EXPLAIN, MAP, REDUCE, DISTRIBUTE BY List and Map operators: array[i], map[k], struct.field
Hadoop is good for storing and processing large amounts of unstructured or structured data in batch form (i.e. full table scans) Hadoop with HBASE (or Hypertable) can do inserts/updates/deletes with reasonable interactive response times (also see Cassandra).
Sports car is refined, accelerates very fast, and has a lot of addons/features. But it is pricey on a per bit basis and is expensive to maintain. Cargo train is rough, missing a lot of functionality, slow to start, but once it gets going it can carry a lot of stuff very economically.
Hadoop is efficient on a cost basis. Security: Need better integration with systems like LDAP or Kerberos. Also need better isolation against malicious users, though auditing can potentially catch those.
The Data Node slave and the Task Tracker slave can, and should, share the same server instance to leverage data locality whenever possible.