The document provides an overview of big data and Hadoop, discussing what big data is, current trends and challenges, approaches to solving big data problems including distributed computing, NoSQL, and Hadoop, and introduces HDFS and the MapReduce framework in Hadoop for distributed storage and processing of large datasets.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
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.
Supporting Financial Services with a More Flexible Approach to Big DataWANdisco Plc
In this webinar, WANdisco and Hortonworks look at three examples of using 'Big Data' to get a more comprehensive view of customer behavior and activity in the banking and insurance industries. Then we'll pull out the common threads from these examples, and see how a flexible next-generation Hadoop architecture lets you get a step up on improving your business performance. Join us to learn:
- How to leverage data from across an entire global enterprise
- How to analyze a wide variety of structured and unstructured data to get quick, meaningful answers to critical questions
- What industry leaders have put in place
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
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.
Supporting Financial Services with a More Flexible Approach to Big DataWANdisco Plc
In this webinar, WANdisco and Hortonworks look at three examples of using 'Big Data' to get a more comprehensive view of customer behavior and activity in the banking and insurance industries. Then we'll pull out the common threads from these examples, and see how a flexible next-generation Hadoop architecture lets you get a step up on improving your business performance. Join us to learn:
- How to leverage data from across an entire global enterprise
- How to analyze a wide variety of structured and unstructured data to get quick, meaningful answers to critical questions
- What industry leaders have put in place
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Big Data with Hadoop and HDInsight. This is an intro to the technology. If you are new to BigData or just heard of it. This presentation help you to know just little bit more about the technology.
Hadoop is emerging as the preferred solution for big data analytics across unstructured data. Using real world examples learn how to achieve a competitive advantage by finding effective ways of analyzing new sources of unstructured and machine-generated data.
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
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 presentation Simplify the concepts of Big data and NoSQL databases & Hadoop components.
The Original Source:
http://zohararad.github.io/presentations/big-data-introduction/
Hadoop and WANdisco: The Future of Big DataWANdisco Plc
View the webinar recording here... http://youtu.be/O1pgMMyoJg0
Who: WANdisco CEO, David Richards, and core creaters of Apache Hadoop, Dr. Konstantin Shvachko and Jagane Sundare.
What: WANdisco recently acquired AltoStor, a pioneering firm with deep expertise in the multi-billion dollar Big Data market.
New to the WANdisco team are the Hadoop core creaters, Dr. Konstantin Shvachko and Jagane Sundare. They will cover the the acquisition and reveal how WANdisco's active-active replication technology will change the game of Big Data for the enterprise in 2013.
Hadoop, a proven open source Big Data technolgoy, is the backbone of Yahoo, Facebook, Netflix, Amazon, Ebay and many of the world's largest databases.
When: Tuesday, December 11th at 10am PST (1pm EST).
Why: In this 30-minute webinar you’ll learn:
The staggering, cross-industry growth of Hadoop in the enterprise
How Hadoop's limitations, including HDFS's single-point of failure, are impacting the productivity of the enterprise
How WANdisco's active-active replication technology will alleviate these issues by adding high-availability to Hadoop, taking a fundamentally different approach to Big Data
View the webinar Q&A on the WANdisco blog here...http://blogs.wandisco.com/2012/12/14/answers-to-questions-from-the-webinar-of-dec-11-2012/
Hadoop and Graph Data Management: Challenges and OpportunitiesDaniel Abadi
HadoopWorld 2011 Presentation by Daniel Abadi
As Hadoop rapidly becomes the universal standard for scalable data analysis and processing, it is increasingly important to understand its strengths and weaknesses for particular application scenarios in order to avoid inefficiency pitfalls. For example, Hadoop has great potential to perform scalable graph analysis if it is used correctly. Recent benchmarking has shown that simple implementations can be 1300 times less efficient than a more optimal Hadoop-centered implementation. In this talk, Daniel Abadi gives an overview of a recent research project at Yale University that investigates how to perform sub-graph pattern matching within a Hadoop-centered system that is three orders of magnitude faster than a more simple approach. This talk highlights how the cleaning, transforming, and parallel processing strengths of Hadoop are combined with storage optimized for graph data analysis. It then discusses further changes that are needed in the core Hadoop framework to take performance to the next level.
Slides for talk presented at Boulder Java User's Group on 9/10/2013, updated and improved for presentation at DOSUG, 3/4/2014
Code is available at https://github.com/jmctee/hadoopTools
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Big Data with Hadoop and HDInsight. This is an intro to the technology. If you are new to BigData or just heard of it. This presentation help you to know just little bit more about the technology.
Hadoop is emerging as the preferred solution for big data analytics across unstructured data. Using real world examples learn how to achieve a competitive advantage by finding effective ways of analyzing new sources of unstructured and machine-generated data.
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
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 presentation Simplify the concepts of Big data and NoSQL databases & Hadoop components.
The Original Source:
http://zohararad.github.io/presentations/big-data-introduction/
Hadoop and WANdisco: The Future of Big DataWANdisco Plc
View the webinar recording here... http://youtu.be/O1pgMMyoJg0
Who: WANdisco CEO, David Richards, and core creaters of Apache Hadoop, Dr. Konstantin Shvachko and Jagane Sundare.
What: WANdisco recently acquired AltoStor, a pioneering firm with deep expertise in the multi-billion dollar Big Data market.
New to the WANdisco team are the Hadoop core creaters, Dr. Konstantin Shvachko and Jagane Sundare. They will cover the the acquisition and reveal how WANdisco's active-active replication technology will change the game of Big Data for the enterprise in 2013.
Hadoop, a proven open source Big Data technolgoy, is the backbone of Yahoo, Facebook, Netflix, Amazon, Ebay and many of the world's largest databases.
When: Tuesday, December 11th at 10am PST (1pm EST).
Why: In this 30-minute webinar you’ll learn:
The staggering, cross-industry growth of Hadoop in the enterprise
How Hadoop's limitations, including HDFS's single-point of failure, are impacting the productivity of the enterprise
How WANdisco's active-active replication technology will alleviate these issues by adding high-availability to Hadoop, taking a fundamentally different approach to Big Data
View the webinar Q&A on the WANdisco blog here...http://blogs.wandisco.com/2012/12/14/answers-to-questions-from-the-webinar-of-dec-11-2012/
Hadoop and Graph Data Management: Challenges and OpportunitiesDaniel Abadi
HadoopWorld 2011 Presentation by Daniel Abadi
As Hadoop rapidly becomes the universal standard for scalable data analysis and processing, it is increasingly important to understand its strengths and weaknesses for particular application scenarios in order to avoid inefficiency pitfalls. For example, Hadoop has great potential to perform scalable graph analysis if it is used correctly. Recent benchmarking has shown that simple implementations can be 1300 times less efficient than a more optimal Hadoop-centered implementation. In this talk, Daniel Abadi gives an overview of a recent research project at Yale University that investigates how to perform sub-graph pattern matching within a Hadoop-centered system that is three orders of magnitude faster than a more simple approach. This talk highlights how the cleaning, transforming, and parallel processing strengths of Hadoop are combined with storage optimized for graph data analysis. It then discusses further changes that are needed in the core Hadoop framework to take performance to the next level.
Slides for talk presented at Boulder Java User's Group on 9/10/2013, updated and improved for presentation at DOSUG, 3/4/2014
Code is available at https://github.com/jmctee/hadoopTools
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Clínica Internacional | Hemorroides: síntomas, causas y tratamientoClínica Internacional
En Clínica Internacional sabemos que las hemorroides son una incómoda afección de la cual a penas se habla. Te explicaremos sobre las principales causas y síntomas, así como los mejores métodos caseros para aliviar las molestias que producen.
Best Practices to Achieve Quality Pressure-Volume Loop Data in Large Animal M...InsideScientific
During this webinar sponsored by Transonic, Dr. Tim Hacker and Dr. Filip Konecny present common hemodynamic set ups for large animal models. Using case studies from dogs and swine models, they show surgical best-practices, tips for catheter navigation and how to correctly position a PV-catheter in the left or right ventricle. In addition, they explain how researchers can verify accurate and reliable PV loop data at the bench-side.
Large animal hemodynamic research models are on the rise. They are increasingly used in various preclinical studies including pharmaco-safety and drug discovery assessment, ventricular assist-device testing and models of pulmonary artery hyperthrophy and right ventricular overload. Important to these applications and all cardiovascular studies is the collection of both central and peripheral hemodynamics, with a focus on instantaneous pressure and volume measurements from the beating heart (PV Loops). Only with PV loops can scientists obtain the most comprehensive evaluation of cardiac function. It is therefore critical for cardiovascular scientists to understand how PV Loop data should be collected along with these peripheral hemodynamic measurements. This webinar aims to discuss these essential elements and how they should be applied.
Key Topics:
Basic and advanced set ups for large animal
hemodynamic studies
Essential hemodynamic equipment/technology
Anaesthetic and drug considerations for large animal PV studies
Surgical approaches -- which one is best for you?
How to successfully navigate the PV catheter and validate
correct position in the ventricle
Right ventricle PV loops -- important surgical, data collection
and analysis considerations
Unique attributes of 'admittance' derived volume
IBM's big data seminar programme -moving beyond Hadoop - Ian Radmore, IBMInternet World
Big Data Meets Big Analytics Theatre - June 18th, 15:00-15:30
Eighty percent of the world's data is unstructured, and most businesses don't even attempt to use this data to their advantage. Imagine if you could afford to keep and analyse all the data generated by your business. Imagine that you had a way to analyse and exploit that data as it is created! Whether you're a telecoms provider trying to minimise customer churn, a utility company looking to exploit the potential of smart-metering or a surveillance company ensuring the security of clients' premises, there are genuine business opportunities from deploying big data analytics in real-time. Using live client examples, Ian will show how real-time analytics provide a powerful extension to any big data platform and is applicable across many types of information and real-world problems to deliver tangible business value.
If you are search Best Engineering college in India, Then you can trust RCE (Roorkee College of Engineering) services and facilities. They provide the best education facility, highly educated and experienced faculty, well furnished hostels for both boys and girls, top computerized Library, great placement opportunity and more at affordable fee.
Introduction to Big Data and NoSQL.
This presentation was given to the Master DBA course at John Bryce Education in Israel.
Work is based on presentations by Michael Naumov, Baruch Osoveskiy, Bill Graham and Ronen Fidel.
Hi all, its presentation about the big data analysis done using a data mining tool known as HADOOP, which is based on Distributive file system and uses parallel computing for working.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
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/
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.
Key Trends Shaping the Future of Infrastructure.pdf
Big data and hadoop overvew
1. Big Data and Hadoop Overview
Saurabh Khanna
Mob: +91-8147644946
2. Agenda
Introduction to Big Data
Current market trends and challenges of Big Data
Approach to solve Big Data Problems
Introduction to Hadoop
HDFS & Map Reduce
Hadoop Cluster Introduction & Creation
Hadoop Ecosystems
3. Introduction to Big Data
“Big data is a collection of large and complex data sets that it becomes difficult to
process using on-hand database management tools.The challenges include capture,
storage, search, sharing, analysis, and visualization”
Or
Big data is the realization of greater business intelligence by storing, processing, and
analyzing data that was previously ignored due to the limitations of traditional data
management technologies.And it has 3V’s
6. Big Data Source (2/2)
The Model of Generating/Consuming Data has Changed
Old Model: Few companies are generating data, all others are consuming data
New Model: all of us are generating data, and all of us are consuming data
9. Current market trends & challenges of Big Data (1/2)
We’re generating more data than ever
• Financial transactions
• Sensor networks
• Server logs
• Analytic
• e-mail and text messages
• Social media
And we’re generating data faster than ever
• Automation
• Ubiquitous internet connectivity
• User-generated content
For example, every day
• Twitter processes 340 million messages
• Amazon S3 storage adds more than one billion objects
• Facebook users generate 17.7 billion comments and “Likes”
10. Data isValue and we must process it to extract that value
This data has many valuable applications
• Marketing analysis
• Demand forecasting
• Fraud detection
• And many, many more…
Data Access is the Bottleneck
• Although we can process data more quickly but accessing is very slow and this
is true for both reads and writes. For example
• Reading a single 3TB disk takes almost four hours
• We cannot process the data till we’ve read the data
• We’re limited by the speed of a single disk
• We’ll see Hadoop’s solution in a few moments
Disk performance has also increased in the last 15 years but unfortunately, transfer
rates haven’t kept pace with capacity
Year Capacity (GB) Cost per GB (USD) Transfer Rate (MB/S) Disk Read Time
1997 2.1 $157 16.6 126 seconds
2004 2000 $1.05 56.5 59 minutes
2012 3,000 $0.5 210 3 hours,58 minutes
Current market trends & challenges of Big Data (2/2)
11. Approach to solve Big Data problem
Previously explained pain areas lead to below problems
• Large-scale data storage
• Large-scale data analysis
There are following approach we have to solve Big Data problems
• Option 1 - Distributed Computing
• Option 2 - NoSQL
• Option 3 - HDFS
12. Distributed Computing – Option 1
Typical processing pattern
Step 1: Copy input data from storage to compute node
Step 2: Perform necessary processing
Step 3: Copy output data back to storage
This works fine with relatively amounts of data but we have few problems
with this approach
• That is, where step 2 dominates overall runtime
• More time spent copying data than actually processing it
• Getting data to the processors is the bottleneck
• Grows worse as more compute nodes are added
• They’re competing for the same bandwidth
• Compute nodes become starved for data
• It is not fault tolerance
13. NoSQL - Option 2
NoSQL (commonly referred to as "Not Only SQL") represents a completely different
framework of databases that allows for high-performance, agile processing of information
at massive scale. In other words, it is a database infrastructure that has been very well-
adapted to the heavy demands of big data.
NoSQL is referring to non-relational or at least non-SQL database solutions such
as HBase (also a part of the Hadoop ecosystem like Casandra, Mongo DB, Riak, Couch
DB, and many others.
NoSQL centers around the concept of distributed databases, where unstructured data
may be stored across multiple processing nodes, and often across multiple servers.
This distributed architecture allows NoSQL databases to be horizontally scalable; as data
continues to explode, just add more hardware to keep up, with no slowdown in
performance.
The NoSQL distributed database infrastructure has been the solution to handling some
of the biggest data warehouses on the planet – i.e. the likes of Google,Amazon, and the
CIA.
14. Hadoop - Option 3
Hadoop is a software framework for distributed processing of large datasets
across large clusters of computers
Large datasets Terabytes or petabytes of data
Large clusters hundreds or thousands of nodes
Hadoop is open-source implementation for Google MapReduce
Hadoop is based on a simple programming model called MapReduce
Hadoop is based on a simple data model, any data will fit
Hadoop was started to improve scalability of Apache Nutch
• Nutch is an open source Web search engine.
15. Main Big dataTechnology
Hadoop NoSQL Databases Analytic Databases
Hadoop
• Low cost, reliable
scale-out architecture
• Distributed computing
Proven success in
Fortune 500 companies
• Exploding interest
NoSQL Databases
• Huge horizontal scaling
and high availability
• Highly optimized for
retrieval and appending
• Types
• Document stores
• Key Value stores
• Graph databases
Analytic RDBMS
• Optimized for bulk-load
and fast aggregate query
workloads
• Types
• Column-oriented
• MPP
• OLTP
• In-memory
16. Hadoop ?
“Apache Hadoop is an open-source software framework for storage and large-
scale processing of data-sets on clusters of commodity hardware. Hadoop is an
Apache top-level project being built and used by a global community of contributors
and users.”
Two Google whitepapers had a major influence on this effort
• The Google File System (storage)
• Map Reduce (processing)
17. Design principles of Hadoop
Invented byYahoo (Doug Cutting)
• Process internet scale data (search the web, store the web)
• Save costs - distributed workload on massively parallel system build with large numbers
of inexpensive computers
New way of storing and processing the data:
• Let system handle most of the issues automatically:
• Failures
• Scalability
• Reduce communications
• Distribute data and processing power to where the data is
• Make parallelism part of operating system
• Relatively inexpensive hardware ($2 – 4K)
• Reliability provided though replication
• Large files preferred over small
Bring processing to Data!
Hadoop = HDFS + Map / Reduce infrastructure
18. Search
Yahoo,Amazon, Zvents
Log processing
Facebook,Yahoo, ContextWeb. Joost, Last.fm
Recommendation Systems
Facebook
DataWarehouse
Facebook,AOL
Video and Image Analysis
NewYorkTimes, Eyealike
What is Hadoop used for?
19. Hadoop Users
Banking and financial
• JP Morgan and Chase
• Bank of America
• Commonwealth bank of Australia
Telecom
• China Mobile Corporation
Retail
• E-bay
• Amazon
Manufacturing
• IBM
• ADOBE
Web & Digital Media
• Facebook
• Twitter
• LinkedIn
• NewYorkTimes
20. 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
Able to recover on major failures:
• Regular backups
• Logging
• Mirror database at different site
Capability:
• Increase capacity without restarting the whole system (Pure Scale)
• 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
21. Consider the example of facebook,
Facebook data has grown upto 100TB/day by 2013 and in future shall produce data of a much
higher magnitude.
They have many web servers and huge MySql (profile,friends etc.) servers to hold the user
data.
Hadoop solution framework – A practical example (1/2)
22. Now to run various reports on these huge data
For eg: 1) Ratio of men vs. women users for a period.
2) No of users who commented on a particular day.
Soln:
For this requirement they had scripts written in python which uses ETL processes.
But as the size of data increased to this extent these scripts did not work.
Hence their main aim at this point of time was to handle data warehousing and their
home ground solutions were not working.
This is when Hadoop came into the picture..
Hadoop solution framework – A practical example (2/2)
24. 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 file system written in Java for the Hadoop
framework.
It has many similarities with existing distributed file systems.
Hadoop Distributed File System (HDFS™) is the primary storage system used by Hadoop
applications.
HDFS creates multiple replicas of data blocks and distributes them on compute nodes throughout a
cluster to enable reliable, extremely rapid computations.
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)
• Name Node
• Data Node
• Secondary Name Node
25. HDFS Components(1/2)
Name node:
Name Node, a master server, manages the file system namespace and regulates access to files by
clients. It has following properties.
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 Data Nodes for each block
• File attributes, e.g. creation time, replication factor
• ATransaction Log
• Records file creations, file deletions. Etc.
Data Node:
Data Nodes, a server where we store our actual data it should be one per node and it has following
properties
• 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 Data Nodes
26. Secondary Name Node
• It is not used as hot stand-by or mirror node. It is just a failover node is in future release.
• It is used for housekeeping purpose and in case of NN failure we can take data from this
node.
• It use to take backup Name Node periodically
• Memory requirements are the same as Name Node (big)
• Typically on a separate machine in large cluster ( > 10 nodes)
• Directory is same as Name Node except it keeps previous checkpoint version in addition
to current.
• It can be used to restore failed Name Node (just copy current directory to new Name
Node)
HDFS Components(2/2)
29. Introduction to MapReduce Framework
“A programming model for parallel data processing. Hadoop can run map reduce
programs in multiple languages like Java, Python, Ruby and C++.“
Map function:
• Operate on set of key, value pairs
• Map is applied in parallel on input data set
• This produces output keys and list of values for each key depending upon the
functionality
• Mapper output are partitioned per reducer = No. Of reduce task for that job
Reduce function:
Operate on set of key, value pairs
Reduce is then applied in parallel to each group, again producing a collection of
key, values.
No of reducers can be set by the user.
32. Map Reduce Components
JobTracker :
The Job-Tracker is responsible for accepting jobs from clients, dividing
those jobs into tasks, and assigning those tasks to be executed by worker nodes.
TaskTracker :
Task-Tracker process that manages the execution of the tasks currently
assigned to that node. EachTaskTracker has a fixed number of slots for executing
tasks (two maps and two reduces by default).
33. MapReduce co-located with HDFS
Slave node
A
Client submits
MapReduce
job
JobTracker
TaskTracker
Slave node B Slave node
C
TaskTracker TaskTracker
NameNode
JobTracker and NameNode
need not be on same
node
DataNode
TaskTrackers (compute nodes) and DataNodes colocate = high aggregate bandwidth
across cluster
DataNode DataNode
34. Understanding processing in a M/R framework (1/2)
User runs a program on the client computer
Program submits a job to HDFS. Job contains:
• Input data
• Map / Reduce program
• Configuration information
Two types of daemons that control job execution:
• Job Tracker (master node)
• Task Trackers (slave nodes)
Job sent to Job Tracker then Job Tracker communicates with Name Node and assigns parts of
job to Task Trackers (Task Tracker is run on each Data Node)
Task is a single MAP or REDUCE operation over piece of data
Hadoop divides the input to MAP / REDUCE job into equal splits
The Job Tracker knows (from Name Node) which node contains the data, and which other
machines are nearby.
Task processes send heartbeats to Task Tracker and Task Tracker sends heartbeats to the Job
Tracker.
35. Any tasks that did not report in certain time (default is 10 min) assumed to be
failed and it’s JVM will be killed byTask Tracker and reported to the Job
Tracker.
The JobTracker will reschedule any failed tasks (with differentTask Tracker)
If same task failed 4 times all job will fails
AnyTask Tracker reporting high number of failed jobs on particular node will
be blacklist the node (remove metadata from Name Node)
JobTracker maintains and manages the status of each job. Results from failed
tasks will be ignored
Understanding processing in a M/R framework (2/2)
36. Computing parallelism meet data locality
All map tasks are equivalent; so can run in parallel
All reduce tasks can also run in parallel
Input data on HDFS on can be processed independently
Therefore, run map task on whatever data is local (or closest) to a particular
node in HDFS and will be good performance
• For map task assignment, JobT racker has an affinity for a particular
node which has a replica of the input data
• If lots of data does happen to pile up on the same node, nearby
nodes will map instead
And improve recovery from partial failure of servers or storage during the
operation: if one map or reduce task fails, the work can be rescheduled
37. Programming using MapReduce
WordCount is a simple application that counts the number of occurences of each word
in a given input file.
Here we divide the entire code into 3 files
1)Mapper.java
2)Reducer.java
3)Basic.java
38. Mapper.java
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;
public class Mapper extends MapReduceBase implements Mapper<LongWritable,Text,Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
privateText word = new Text();
public void map(LongWritable key,Text value, OutputCollector<Text,IntWritable> output, Reporter
reporter) throws IOException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
output.collect(word, one);
}
}
}
39. For the following standard input mapper does the following
Input: I am working forTCS
TCS is a great company
The Mapper implementation, via the map method, processes one line at a time, as provided
by the specifiedTextInputFormat. It then splits the line into tokens separated by whitespaces,
via the StringTokenizer, and emits a key-value pair of < <word>, 1>.
Output: <I,1>
<am,1>
<working,1>
<for,1>
<TCS,1>
<TCS,1>
<is,1>
<a,1>
<great,1>
<company,1>
40. Sorted mapper output to reducer
Hence, the output of each map is passed through a sorting Algorithm which sorts the output of Map
according to the keys.
Output: <a,1>
<am,1>
<company,1>
<for,1>
<great,1>
<I,1>
<is,1>
<TCS,1>
<TCS,1>
<working,1>
41. Reducer.java
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;
public class Reducer extends MapReduceBase implements Reducer<Text, IntWritable,Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text,IntWritable> output,
Reporter reporter) throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
output.collect(key, new IntWritable(sum));
}
}
42. Reducer output
The output of Mapper is given to the Reducer, which Sums up the values, which are the
occurrence counts for each key ( i.e. words in this example).
Output: <a,1>
<am,1>
<company,1>
<for,1>
<great,1>
<I,1>
<is,1>
<TCS,2>
<working,1>
43. Basic.java
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;
public class Basic extends MapReduceBase implements Reducer<Text, IntWritable,Text, IntWritable> {
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(Basic.class);
conf.setJobName("wordcount");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(Mapper.class);
conf.setReducerClass(Reducer.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
}
44. Executing the MapReduce program
1)Compile all the 3 java files which will create 3 .class files
2)Add all 3 .class files into 1 single jar file by writing this command
jar –cvf file_name.jar *.class
3)Now you just need to execute single jar file by writing this command
bin/hadoop jar file_name.jar Basic input_file_name output_file_name
47. Clustering in Hadoop
Clustering in HADOOP can be achieved in the following modes
Local (Standalone) Mode-Used for Debugging:
• By default, Hadoop is configured to run in a non-distributed mode, as a
single Java process. This mode is useful for debugging.
Pseudo-Distributed Mode- Used for Development :
• Hadoop can also be run on a single-node in a pseudo-distributed mode
where all Hadoop daemon runs in a separate Java process
Fully-Distributed Mode- Used for Debugging, Development,
Production :
• In this mode all hadoop daemons will be running on separate nodes and
it is useful for production.
49. Executing a pseudo cluster
Format a new distributed-file system:
$ bin/hadoop namenode -format
Start the hadoop daemons:
$ bin/start-all.sh
Copy the input files into the distributed filesystem:
$ bin/hadoop fs –copyFromLocal input1 input
Run some of the examples provided:
$ bin/hadoop jar hadoop-examples.jar wordcount input output
Examine the output files:
Copy the output files from the distributed file system to the local file system and examine
them:
$ bin/hadoop fs -copyToLocal output output
$ cat output/part-00000
When you're done, stop the daemons with:
$ bin/stop-all.sh
52. Hadoop Ecosystems
Apache Hive
Apache Pig
Apache HBase
Sqoop
Oozie
Hue
Flume
Apache Whirr
Apache
Zookeeper
SQL-like language
and metadata
repository
High-level
language for
expressing data
analysis
programs
The Hadoop
database.
Random, real -
time read/write
access
Highly reliable
distributed
coordination
service
Library for
running Hadoop
in the cloud
Distributed
service for
collecting and
aggregating log
and event data
Browser-based
desktop interface
for interacting
with Hadoop
Server-based
workflow engine
for Hadoop
activities
Integrating
Hadoop with
RDBMS
53. Pig Concepts
What is Pig ?
It is an open-source high-level dataflow system and
introduced by Yahoo
Provides a simple language for queries and data
manipulation, Pig Latin, that is compiled into map-
reduce jobs that are run on Hadoop
Pig Latin combines the high-level data manipulation
constructs of SQL with the procedural programming of
map-reduce
Why is it important?
Companies and organizations like Yahoo, Google and
Microsoft are collecting enormous data sets in the form
of click streams, search logs, and web crawls
Some form of ad-hoc processing and analysis of all of this
information is required
55. Hive Concepts
What is Hive ?
It is an open-source DW solution built on top of Hadoop and
introduced by Facebook
Support SQL-like declarative language called HiveQL which
are compiled into map-reduce jobs executed on Hadoop
Also support custom map-reduce script to be plugged into
query.
Includes a system catalog, Hive Metastore for query
optimizations and data exploration
Why is it important?
It is very easy to learn because of its similar behavior like
SQL.
Built-in user defined functions (UDFs) to manipulate dates,
strings, and other data-mining tools. Hive supports
extending the UDF set to handle use-cases not supported by
built-in functions.
56. Hive execution plan
Clients either via CLI/
JBDC/ODBC
HIVE-QL
Driver
Invoke
Compiler
DAGof
Map-
Reduces
Execution Engine
hadoop
57. Difference between Pig & Hive
Apache Pig and Hive are two projects that layer on top of
Hadoop, and provide a higher-level language for using
Hadoop's MapReduce library
Pig provides a scripting language for describing operations like
reading, filtering, transforming, joining, and writing data.
If Pig is "scripting for Hadoop", then Hive is "SQL queries for
Hadoop".
Apache Hive offers an even more specific and higher-level
language, for querying data by running Hadoop jobs, rather
than directly scripting step-by-step the operation of several
Map Reduce jobs on Hadoop.
Hive is an excellent tool for analysts and business development
types who are accustomed to SQL-like queries and Business
Intelligence systems.
Pig lets users express them in a language not unlike a bash or
perl script.
58. HBase
Apache HBase in a few words:
“HBase is an open-source, distributed, versioned, column-oriented
store modeled after Google's Bigtable”
HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning
that the database isn't an RDBMS which supports SQL as its primary
access language, but there are many types of NoSQL databases.
HBase is very much a distributed database. Technically speaking, HBase
is really more a "Data Store" than "Data Base" because it lacks many of
the features you find in an RDBMS, such as typed columns, secondary
indexes, triggers, and advanced query languages, etc.
However, HBase has many features which supports both linear and
modular scaling.
HBase supports an easy to use Java API for programmatic access.
59. Why is it important?
HBase is a Bigtable clone.
It is open source
It has a good community and promise for the future
It is developed on top of and has good integration for the Hadoop
platform, if you are using Hadoop already.
It has a Cascading connector.
No real indexes
Automatic partitioning
Scale linearly and automatically with new nodes
Commodity hardware
Fault tolerance
Batch processing
60. Difference between HBase and Hadoop/HDFS?
HDFS is a distributed file system that is well suited for the storage of
large files. Its documentation states that it is not, however, a general
purpose file system, and does not provide fast individual record lookups
in files.
HBase, on the other hand, is built on top of HDFS and provides fast
record lookups (and updates) for large tables. This can sometimes be a
point of conceptual confusion. HBase internally puts your data in
indexed "StoreFiles" that exist on HDFS for high-speed lookups.