Is Hadoop a necessity for Data Science
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This document discusses big data, Hadoop, data science, and why Hadoop is useful for data science. It begins with defining big data and the 3 V's of big data. It then explains what Hadoop is and how it works using HDFS for storage and MapReduce for processing. The document defines what a data product is and provides examples. It defines data science as extracting meaning from data and building data products. Finally, it argues that Hadoop is useful for data science because it allows exploration of full datasets, mining of larger datasets, large-scale data preparation, and can accelerate data-driven innovation by removing speed barriers of traditional architectures.
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Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python is continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
Python in the Hadoop Ecosystem (Rock Health presentation)
A presentation covering the use of Python frameworks on the Hadoop ecosystem. Covers, in particular, Hadoop Streaming, mrjob, luigi, PySpark, and using Numba with Impala.
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Pig and Python to Process Big Data
Shawn Hermans gave a presentation on using Pig and Python for big data analysis. He began with an introduction and background about himself. The presentation covered what big data is, challenges in working with large datasets, and how tools like MapReduce, Pig and Python can help address these challenges. As examples, he demonstrated using Pig to analyze US Census data and propagate satellite positions from Two Line Element sets. He also showed how to extend Pig with Python user defined functions.
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Python in the Hadoop Ecosystem (Rock Health presentation)
Python in the Hadoop Ecosystem (Rock Health presentation)
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Hadoop Webinar 28July15
This document discusses why Hadoop is important to learn. It predicts that by 2020 there will be 40 zettabytes of data in the world and big data is being used by companies like Amazon, Twitter, and stock markets. Hadoop allows for the use of big data by being fast, flexible, scalable, cost effective and fault tolerant. The document outlines career paths working with Hadoop and that 2015 will see further adoption by enterprises and emergence of Hadoop distributions from companies like IBM. It concludes by answering that it is the right time to learn Hadoop due to its proven worth, growing adoption, disappearing skills shortage, and job opportunities.
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The document is from Edureka about their Big Data and Hadoop course. It provides an overview of Hadoop including what it is, its key characteristics of being reliable, economical, scalable and flexible. It describes the core Hadoop components of HDFS for storage and YARN for processing. It also discusses when to use Hadoop such as for large datasets that are diverse, growing, and not time critical. Examples are provided for processing different data types like images, XML files, and CSVs. Contact information is given for the Big Data and Hadoop course.
Is Hadoop a Necessity for Data Science
This document discusses big data, Hadoop, data science, and whether Hadoop is necessary for data science. It defines big data and its 3 V's, describes Hadoop as a framework for processing large datasets across many nodes, discusses how Hadoop uses HDFS for storage and MapReduce for distributed computation, and explains why Hadoop is useful for data science tasks like exploring large datasets, data preparation, and accelerating data-driven innovation.
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...
This Edureka Big Data tutorial helps you to understand Big Data in detail. This tutorial will be discussing about evolution of Big Data, factors associated with Big Data, different opportunities in Big Data. Further it will discuss about problems associated with Big Data and how Hadoop emerged as a solution. Below are the topics covered in this tutorial:
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Talend For Big Data : Secret Key to Hadoop
Today, when data is mushrooming and coming in heterogeneous forms, there is a growing need for a flexible, adaptable platforms. Talend fits just perfect in this space with a proven track record, making scope for vast opportunities. If you understand how to manage, transform, store your organisation data (retail, banking, airlines, research, insurance, cards etc.) and effectively represent it, then you are the resource organizations are looking for.
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This Hadoop tutorial on Big Data Career Path and Learning Path ( Why Big Data Career blog: https://goo.gl/Hx1hbk ) will tell you why Big Data analytics is the best career move. Learn about various job roles, salary trends and learning paths in Big Data domain. Below are the topics covered in this Big Data Career Path and Learning Path Tutorial:
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4) Big Data Career Path
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6) Edureka Big Data Certification Courses
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Check our complete Hadoop playlist here: https://goo.gl/4OyoTW
#BigDataCareer #HadoopCareer #BigDataLearningPath
Hadoop, Iot and Analytics- The Three Musketeers
The document discusses how Hadoop, IoT, and analytics are interrelated and important for the future. It defines each concept and provides examples of how they can work together. Hadoop is a framework for storing and processing large amounts of data across clusters of computers. IoT refers to networks of physical objects embedded with sensors that can communicate electronically. Analytics involves mining vast amounts of data through tools like Hadoop to extract useful insights and information.
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The document discusses big data and Hadoop, providing an introduction to big data, use cases across industries, an overview of the Hadoop ecosystem and architecture, and learning paths for professionals. It also includes examples of how companies like Facebook use large Hadoop clusters to store and process massive amounts of user data at petabyte scale. The presentation aims to help attendees understand big data, Hadoop, and career opportunities working with these technologies.
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The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
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The document is a presentation on big data and Hadoop. It introduces the speaker, Adam Muise, and discusses the challenges of dealing with large and diverse datasets. Traditional approaches of separating data into silos are no longer sufficient. The presentation argues that a distributed system like Hadoop is needed to bring all data together and enable it to be analyzed as a whole.
Talend webinar
This document discusses using Talend for Big Data integration and analytics. It provides an overview of how Talend can be used to extract, load, and transform big data in Hadoop. Specifically, it describes how Talend allows users to design ETL jobs graphically that run as MapReduce jobs on Hadoop without requiring MapReduce coding. The document also outlines a banking use case where Talend is used to analyze web log data from a marketing campaign stored in Hadoop and generate business insights in minutes.
TSE_Pres12.pptx
This document discusses big data workflows. It begins by defining big data and workflows, noting that workflows are task-oriented processes for decision making. Big data workflows require many servers to run one application, unlike traditional IT workflows which run on one server. The document then covers the 5Vs and 1C characteristics of big data: volume, velocity, variety, variability, veracity, and complexity. It lists software tools for big data platforms, business analytics, databases, data mining, and programming. Challenges of big data are also discussed: dealing with size and variety of data, scalability, analysis, and management issues. Major application areas are listed in private sector domains like retail, banking, manufacturing, and government.
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This video on "How to become a Data Scientist" includes all the skills required for becoming a modern day Data Scientist. This video will answer the below questions:
1. Why should you go for data science?
2. What is the roadmap to become a data scientist?
3. What are the tools and techniques required to become a data scientist?
4. What are the roles of a data scientist?
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Check out our Data Science Training Playlist: https://goo.gl/Jg1pJJ
ETL using Big Data Talend
Today, when data is mushrooming and coming in heterogeneous forms, there is a growing need for a flexible, adaptable, efficient and cost effective integration platform which will take minimum on-boarding time and interact and entertain n number of platforms. Talend fits just perfect in this space with a proven track record, so learning talend makes lot of sense for anybody associated with data world.
If you understand how to manage, transform, store your organisation data (retail, banking, airlines, research, insurance, cards etc.) and effectively represent it which is the backbone behind any successful MIS system/reporting/dash board then you are a key person that organisation most sought after.
Oh! Session on Introduction to BIG Data
This document provides an introduction to big data, including definitions of big data and its key characteristics of volume, variety, velocity, variability, and veracity. It discusses big data analysis and how it differs from traditional analytics by examining large, diverse datasets. Hadoop is presented as a popular open-source framework for managing and analyzing big data, and its use by companies like Facebook, LinkedIn, Walmart, and Twitter is described. The document also briefly outlines Hadoop's history and architecture, common Hadoop variants, skills needed to work with Hadoop, and examples of big data case studies.
5 Tips to Building a Successful Big Data Strategy
Watch the full webinar here: http://bit.ly/1Yqr5Lz
Companies are seeking ways to leverage big data and analytics to improve business operations or create new revenue streams. But where do you begin? Join Janet George, SanDisk Chief Data Scientist, as she shares the biggest challenges companies face when first analyzing their data, common mistakes and 5 tips on how to build a successful big data strategy.
Hadoop at the Center: The Next Generation of Hadoop
This document discusses Hortonworks' approach to addressing challenges around managing large volumes of diverse data. It presents Hortonworks' Hadoop Data Platform (HDP) as a solution for consolidating siloed data into a central data lake on a single cluster. This allows different data types and workloads like batch, interactive, and real-time processing to leverage shared services for security, governance and operations while preserving existing tools. The HDP also enables new use cases for analytics like real-time personalization and segmentation using diverse data sources.
Big Data & Open Source - Neil Jadhav
The document discusses big data and open source tools and technologies. It provides an overview of key challenges for data leaders, introduces the top 10 big data tools including Apache Spark, R, and Talend Open Studio. It outlines the benefits of open source including low costs, flexibility, and innovation. The document advocates adopting both corporate and open source software using a "bi-modal" approach to support innovative and engineered analytics. It provides a template for a 1-page big data strategy.
Understanding Big Data And Hadoop
This document provides an overview of the key concepts related to big data and Hadoop. It begins with defining big data and its characteristics. It then introduces Hadoop as an open-source framework for distributed storage and processing of large datasets. The document discusses Hadoop's core components like HDFS for storage and YARN for resource management. It also covers concepts like data replication, rack awareness, and how clients can read and write data from HDFS. Finally, it mentions some other Hadoop distributions and provides pointers for further reading.
Introduction to Data Science
This document provides an overview of a data science course. It discusses topics like big data, data science components, use cases, Hadoop, R, and machine learning. The course objectives are to understand big data challenges, implement big data solutions, learn about data science components and prospects, analyze use cases using R and Hadoop, and understand machine learning concepts. The document outlines the topics that will be covered each day of the course including big data scenarios, introduction to data science, types of data scientists, and more.
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SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
The Microsoft 365 Migration Tutorial For Beginner.pptx
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You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
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Paper Link: https://eprint.iacr.org/2024/257
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Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
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before the computed tomography, it had been widely used.
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Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Is Hadoop a necessity for Data Science
- 1. www.edureka.co/r-for-analytics www.edureka.co/big-data-and-hadoop Is Hadoop a necessity for data Science ?
- 2. Slide 2Slide 2Slide 2 www.edureka.co/big-data-and-hadoop Today we will take you through the following: What is Big Data & Hadoop? What is a Data Product? What is Data Science? Why Hadoop for Data Science? Is Hadoop a necessity for Data Science? AGENDA
- 3. Slide 3Slide 3Slide 3 www.edureka.co/big-data-and-hadoop What is Big Data & Hadoop?
- 4. Slide 4Slide 4Slide 4 www.edureka.co/big-data-and-hadoop BIG DATA Big data is a popular term used to describe the exponential growth of data. Big Data can be either Structured data or Unstructured data or a combination of both. Big Data
- 5. Slide 5Slide 5Slide 5 www.edureka.co/big-data-and-hadoop BIGDATA 3 V’s (Volume, Variety and Velocity) are three defining properties or dimensions of Big Data.
- 6. Slide 6Slide 6Slide 6 www.edureka.co/big-data-and-hadoop HADOOP Hadoop is a programming framework that supports the processing of large data sets in a distributed computing environment. Hadoop was the first and still the best tool to handle Big Data.
- 7. Slide 7Slide 7Slide 7 www.edureka.co/big-data-and-hadoop A BRIEF HISTORY OF HADOOP
- 8. Slide 8Slide 8Slide 8 www.edureka.co/big-data-and-hadoop HADOOP:- HDFS & MAP-REDUCE Most efficient for Large-Scale Storage & Processing HDFS: Distributed file system Self-Healing Data store MAP-REDUCE: Distributed computation framework that handles the complexities of distributed programming
- 9. Slide 9Slide 9Slide 9 www.edureka.co/big-data-and-hadoop KEY TO HADOOP’S POWER Computation co-located with data Data and computation system co-designed and co-developed to work together Process data in parallel across thousands of “commodity” hardware nodes Self-healing; failure handled by software Designed for one write and multiple reads There are no random writes Optimized for minimum seek on hard drives
- 10. Slide 10Slide 10Slide 10 www.edureka.co/big-data-and-hadoop What is a Data product? “A software system whose core functionality depends on the application of statistical analysis and machine learning to data.”
- 11. Slide 11Slide 11Slide 11 www.edureka.co/big-data-and-hadoop Example #1: People you may know
- 12. Slide 12Slide 12Slide 12 www.edureka.co/big-data-and-hadoop Example #2: Spell Correction
- 13. Slide 13Slide 13Slide 13 www.edureka.co/big-data-and-hadoop What is Data Science?
- 14. Slide 14Slide 14Slide 14 www.edureka.co/big-data-and-hadoop DATA SCIENCE #1: Extracting deep meaning from data (data mining; finding “gems” in data)
- 15. Slide 15Slide 15Slide 15 www.edureka.co/big-data-and-hadoop Common Data Science tasks
- 16. Slide 16Slide 16Slide 16 www.edureka.co/big-data-and-hadoop DATA SCIENCE #2: Building Data Products (Delivering Gems on a regular basis)
- 17. Slide 17Slide 17Slide 17 www.edureka.co/big-data-and-hadoop Why HADOOP for DATA SCIENCE? Reason #1: Explore full datasets
- 18. Slide 18Slide 18Slide 18 www.edureka.co/big-data-and-hadoop #1: Exploration of Data sets
- 19. Slide 19Slide 19Slide 19 www.edureka.co/big-data-and-hadoop Why HADOOP for DATA SCIENCE? Reason #2: Mining of larger datasets
- 20. Slide 20Slide 20Slide 20 www.edureka.co/big-data-and-hadoop #2: Mining of larger data sets More Data ---> Better Outcomes
- 21. Slide 21Slide 21Slide 21 www.edureka.co/big-data-and-hadoop Why HADOOP for DATA SCIENCE? Reason #3: Large-scale data preparation
- 22. Slide 22Slide 22Slide 22 www.edureka.co/big-data-and-hadoop #3: Large-Scale Data preparation 80% of data science work is data preparation
- 23. Slide 23Slide 23Slide 23 www.edureka.co/big-data-and-hadoop Reason #4: Accelerate data-driven innovation Why HADOOP for DATA SCIENCE?
- 24. Slide 24Slide 24Slide 24 www.edureka.co/big-data-and-hadoop Speed Barriers of traditional Data Architectures
- 25. Slide 25Slide 25Slide 25 www.edureka.co/big-data-and-hadoop “Schema on read” means faster time-to-innovation
- 26. Demo
- 28. Slide 28 Your feedback is vital for us, be it a compliment, a suggestion or a complaint. It helps us to make your experience better! Please spare few minutes to take the survey after the webinar. SURVEY