Apache Spark is a fast, general engine for large-scale data processing. It supports batch, interactive, and stream processing using a unified API. Spark uses resilient distributed datasets (RDDs), which are immutable distributed collections of objects that can be operated on in parallel. RDDs support transformations like map, filter, and reduce and actions that return final results to the driver program. Spark provides high-level APIs in Scala, Java, Python, and R and an optimized engine that supports general computation graphs for data analysis.
BigData_TP1: Initiation à Hadoop et Map-ReduceLilia Sfaxi
Pour accéder aux fichiers nécessaires pour faire ce TP, visitez: https://drive.google.com/folderview?id=0Bz7DokLRQvx7M2JWZEt1VHdwSE0&usp=sharing
Pour plus de contenu, Visitez http://liliasfaxi.wix.com/liliasfaxi !
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
Presented at the MLConf in Seattle, this presentation offers a quick introduction to Apache Spark, followed by an overview of two novel features for data science
Ce support explique les concepts de base de Big Data Processing. Elle aborde les parties suivantes :
Série de vidéos : https://www.youtube.com/watch?v=1JAljjxpm-Q
- Introduction au Big Data
- Système de stockage en Big Data
- Batch Processing et Stream Processing en Big Data
- Aperçu bref de l’écosystème de Hadoop
- Aperçu de l’écosystème des outils du Bid Gata
- Big data stream processing avec Kafka écosystème
- Architecture de Kafka (Brokers, Zookeeper, Procuder, Consumer, Kafka Streams, Connecteurs)
- Comment démarrer un cluster de brokers KAFKA
- Création et configuration des Topics
- Création d’un Java Kafka consumer
- Création d’un Java Kafka Produder
- Kafka Producer et Kafka Consumer dans une application basée sur Spring
- Kafka Streams
- Intégration de Kafka dans Spring Cloud.
Mot clés : Big data, Big Data Processing, Stream Processing, Kafka, Kafka Streams, Java, Spring
Bon apprentissage
This introductory workshop is aimed at data analysts & data engineers new to Apache Spark and exposes them how to analyze big data with Spark SQL and DataFrames.
In this partly instructor-led and self-paced labs, we will cover Spark concepts and you’ll do labs for Spark SQL and DataFrames
in Databricks Community Edition.
Toward the end, you’ll get a glimpse into newly minted Databricks Developer Certification for Apache Spark: what to expect & how to prepare for it.
* Apache Spark Basics & Architecture
* Spark SQL
* DataFrames
* Brief Overview of Databricks Certified Developer for Apache Spark
The document provides an overview of Apache Spark internals and Resilient Distributed Datasets (RDDs). It discusses:
- RDDs are Spark's fundamental data structure - they are immutable distributed collections that allow transformations like map and filter to be applied.
- RDDs track their lineage or dependency graph to support fault tolerance. Transformations create new RDDs while actions trigger computation.
- Operations on RDDs include narrow transformations like map that don't require data shuffling, and wide transformations like join that do require shuffling.
- The RDD abstraction allows Spark's scheduler to optimize execution through techniques like pipelining and cache reuse.
Ce support correspond à une conférence qui s'intéresse à la mise en œuvre des Framework de Machines et Deep learning pour les applications web et mobiles. Principalement les Framwork TensorFlow.JS et DeepLeanring4J.
Je l'ai présentée au début dans mon établissement auquel j’appartiens, l’ENSET Mohammedia puis dans la conférence Carrefour des informaticiens, organisée par les étudiants de l'AIAC :
Académie internationale Mohammed VI de l'aviation civile.
et le code source est publié sur mon compte GitHub. La suite de cette série sera sans doute publiées dans les prochaines conférences :
https://github.com/mohamedYoussfi/angular-tensorflowJS
https://github.com/mohamedYoussfi/angular-ml5.js-mobilenet-feature-extractor
https://github.com/mohamedYoussfi/deeplearning4j-cnn-mnist-app
Les vidéos de la conférence sont publiée dans ma chaîne vidéo : https://www.youtube.com/user/mohamedYoussfi
Le plan de la présentation est suivant :
- Quelques Concepts de base à comprendre :
- Machines er Deep Learning, Les réseaux de neurones artificiels, MLP et CNN
- Les problèmes et les contraintes posées par les algorithmes d’apprentissage basés sur les réseaux de neurones
- Principaux catalyseurs qui ont redynamisé l’intelligence artificielle :
- Calcul de hautes performances à savoir les architectures massivement parallèles et les systèmes distribués
- La Virtualisation et le cloud Computing
- Big Data, IOT et Applications Mobiles
- Framework et Algorithmes de Machines et Deep Learning
- Réseaux et Télécommunications
- Open source
- L’écosystème des Framework de Machines et Deep Learning.
- L’architecture du Framwork TensorFlow
- Comment développer des applications de machines et Deep Learning pour les applications Web et Mobile en utilisant TensorFlow.JS et ML.JS
- Comment développer des applications de machines et Deep Learning pour les applications Java JEE en utilisant le Framework DeepLearning4J
Mot Clés :
Intelligence Artificielle, Machines learning, deep learning, TensorflowJS, Deeplearning4j, java, java script, angular
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterDataWorks Summit
This document discusses Apache Spark-on-YARN, which allows Spark applications to leverage existing Hadoop clusters. Spark improves efficiency over Hadoop via in-memory computing and supports rich APIs. Spark-on-YARN provides access to HDFS data and resources on Hadoop clusters without extra deployment costs. It supports running Spark jobs in YARN cluster and client modes. The document describes Yahoo's use of Spark-on-YARN for machine learning applications on large datasets.
Spark, ou comment traiter des données à la vitesse de l'éclairAlexis Seigneurin
Spark fait partie de la nouvelle génération de frameworks de manipulation de données basés sur Hadoop. L’outil utilise agressivement la mémoire pour offrir des temps de traitement jusqu’à 100 fois plus rapides qu'Hadoop. Dans cette session, nous découvrirons les principes de traitement de données (notamment MapReduce) et les options mises à disposition pour monter un cluster (Zookeper, Mesos…). Nous ferons un point sur les différents modules proposés par le framework, et notamment sur Spark Streaming pour le traitement de données en flux continu.
Présentation jouée chez Ippon le 11 décembre 2014.
Ce Support explique quelques concepts de base de NodeJS et montre comment mettre en oeuvre la technologie NodeJS pour développer la partie Backend d'une application.
Les vidéos des démonstrations sont publiées sur les adresse suivantes :
- https://www.youtube.com/watch?v=-X_C1tS5-9Y
- https://www.youtube.com/watch?v=rE-xRH28m0s
- https://www.youtube.com/watch?v=tnxjkTvWoKA
Cette série explique les éléments suivants :
- Architecture Web
- Modèles Multi-Threads avec les entrées sorties bloquantes
- Modèles Single Thread avec les entrées sortie non bloquantes
-Technologie Node JS
- Comment créer une simple application Node JS avec java Script
- Architecture du Framwork Express
- Comment créer une application NodeJS avec Type Script
- Comment écrire des tests unitaires avec Jest
- Quelques concepts sur MongoDb
- Comment Créer une API Rest avec NodeJS, Express et MongoDb
- Comment tester l'API Rest
- Comment Créer la partie FrontEnd avec Angular.
Même si la qualité audio n'est pas bonne, ses vidéos peuvent aider ceux qui débutent dans NodeJS en attendant d'autres vidéos avec plus qualité audio et de contenu.
Bonne lecture
Cours HBase et Base de Données Orientées Colonnes (HBase, Column Oriented Dat...Hatim CHAHDI
Ce cours introduit les bases de données orientées colonnes et leurs spécificités. Il détaille par la suite l'architecture d'HBase et explique les moyens nécessaires à sa mise en place et à son exploitation.
Spark SQL Tutorial | Spark Tutorial for Beginners | Apache Spark Training | E...Edureka!
This Edureka Spark SQL Tutorial will help you to understand how Apache Spark offers SQL power in real-time. This tutorial also demonstrates an use case on Stock Market Analysis using Spark SQL. Below are the topics covered in this tutorial:
1) Limitations of Apache Hive
2) Spark SQL Advantages Over Hive
3) Spark SQL Success Story
4) Spark SQL Features
5) Architecture of Spark SQL
6) Spark SQL Libraries
7) Querying Using Spark SQL
8) Demo: Stock Market Analysis With Spark SQL
Apache Spark is a cluster computing framework designed for fast, general-purpose processing of large datasets. It uses in-memory computing to improve processing speeds. Spark operations include transformations that create new datasets and actions that return values. The Spark stack includes Resilient Distributed Datasets (RDDs) for fault-tolerant data sharing across a cluster. Spark Streaming processes live data streams using a discretized stream model.
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
Apache Spark is a fast and general engine for large-scale data processing. It provides a unified API for batch, interactive, and streaming data processing using in-memory primitives. A benchmark showed Spark was able to sort 100TB of data 3 times faster than Hadoop using 10 times fewer machines by keeping data in memory between jobs.
This document provides an overview of Apache Spark, including its core concepts, transformations and actions, persistence, parallelism, and examples. Spark is introduced as a fast and general engine for large-scale data processing, with advantages like in-memory computing, fault tolerance, and rich APIs. Key concepts covered include its resilient distributed datasets (RDDs) and lazy evaluation approach. The document also discusses Spark SQL, streaming, and integration with other tools.
The document provides guidelines for writing idiomatic Scala code. Some key points include:
- Use Option instead of null to avoid null pointer exceptions.
- Use short variable names like 'i' for loops and longer more descriptive names for methods and variables in wider scope.
- Avoid overloading reserved names and prefixing getters; use descriptive active names for methods with side effects.
- Follow conventions like importing collections qualifications and putting imports at the top of files.
- Leverage features like pattern matching, implicits, and recursion to write clear Scala code.
2014-11-26 | Creating a BitTorrent Client with Scala and Akka, Part 1 (Vienna...Dominik Gruber
This presentation includes an overview of the BitTorrent protocol and shows my current approach and progress towards implementing a client with Scala and Akka.
Lightning talk showing various aspectos of software system performance. It goes through: latency, data structures, garbage collection, troubleshooting method like workload saturation method, quick diagnostic tools, famegraph and perfview
Apache Spark is an open-source framework developed by AMPlab of University of California and, successively, donated to Apache Software Foundation. Unlike the MapReduce paradigm based on twolevel disk of Hadoop, the primitive in-memory multilayer provided by Spark allow you to have performance up to 100 times better.
The document discusses concepts for rebranding an organization called Spark Leadership.
Concept 1 focuses on using a unique rounded font to give a soft expression to the name Spark Leadership. It also discusses using color symbolism by relating the word "growth" to turning green. Other branding ideas discussed include business cards, banners, t-shirts, notebooks and coffee cups.
Concept 2 uses an asterisk symbol next to the name to represent a focus point. It discusses using color references and features examples of other organizations to reference. Additional branding concepts include cards and banners.
The document outlines the proposed website structure and sitemap, including sections for growth, Rockefeller Habits training, Go Fast Forward training, events and the company blog
Brian O'Neill from Monetate gave a presentation on Spark. He discussed Spark's history from Hadoop and MapReduce, the basics of RDDs, DataFrames, SQL and streaming in Spark. He demonstrated how to build and run Spark applications using Java and SQL with DataFrames. Finally, he covered Spark deployment architectures and ran a demo of a Spark application on Cassandra.
This talk discusses Spark (http://spark.apache.org), the Big Data computation system that is emerging as a replacement for MapReduce in Hadoop systems, while it also runs outside of Hadoop. I discuss why the issues why MapReduce needs to be replaced and how Spark addresses them with better performance and a more powerful API.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
1. The document discusses the future of data science and big data technologies. It describes the roles of data scientists and their typical skills, salaries, and job outlook.
2. It discusses technologies like Hadoop, Spark, and distributed computing that are used to handle big data. While Hadoop is good for batch processing, Spark can perform both batch and real-time processing 100x faster.
3. Going forward, data science will shift from descriptive to predictive analytics using machine learning to improve customer experience and business outcomes across industries like internet search and digital advertising.
PixieDust is an open source library that simplifies and improves Jupyter Python notebooks. It allows users to:
1. Easily install Python packages and libraries without modifying configuration files.
2. Create visualizations with a simple display() API that includes options for performance statistics, panning, and zooming.
3. Export data to cloud services or locally in CSV, JSON, HTML formats for further use or sharing.
Tiny Batches, in the wine: Shiny New Bits in Spark StreamingPaco Nathan
London Spark Meetup 2014-11-11 @Skimlinks
http://www.meetup.com/Spark-London/events/217362972/
To paraphrase the immortal crooner Don Ho: "Tiny Batches, in the wine, make me happy, make me feel fine." http://youtu.be/mlCiDEXuxxA
Apache Spark provides support for streaming use cases, such as real-time analytics on log files, by leveraging a model called discretized streams (D-Streams). These "micro batch" computations operated on small time intervals, generally from 500 milliseconds up. One major innovation of Spark Streaming is that it leverages a unified engine. In other words, the same business logic can be used across multiple uses cases: streaming, but also interactive, iterative, machine learning, etc.
This talk will compare case studies for production deployments of Spark Streaming, emerging design patterns for integration with popular complementary OSS frameworks, plus some of the more advanced features such as approximation algorithms, and take a look at what's ahead — including the new Python support for Spark Streaming that will be in the upcoming 1.2 release.
Also, let's chat a bit about the new Databricks + O'Reilly developer certification for Apache Spark…
How Spark is Enabling the New Wave of Converged ApplicationsMapR Technologies
Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single compute engine. Spark is speeding up data pipeline development, enabling richer predictive analytics, and bringing a new class of applications to market.
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
This document introduces Spark, an open-source cluster computing framework. Spark improves on Hadoop MapReduce by keeping intermediate data in memory rather than disk, speeding up iterative jobs. Spark uses resilient distributed datasets (RDDs) that can tolerate failures using lineage graphs to recompute lost data. It runs on Hadoop YARN and HDFS and is programmed using Scala, a functional programming language that supports objects, higher-order functions, and nested functions.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through HDFS and distributed processing via MapReduce. HDFS handles storage and MapReduce provides a programming model for parallel processing of large datasets across a cluster. The MapReduce framework consists of a mapper that processes input key-value pairs in parallel, and a reducer that aggregates the output of the mappers by key.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Spark is an open-source software framework for rapid calculations on in-memory datasets. It uses Resilient Distributed Datasets (RDDs) that can be recreated if lost and supports transformations and actions on RDDs. Spark is useful for batch, interactive, and real-time processing across various problem domains like SQL, streaming, and machine learning via MLlib.
Spark is a fast and general engine for large-scale data processing. It runs programs up to 100x faster than Hadoop in memory, and 10x faster on disk. Spark supports Scala, Java, Python and can run on standalone, YARN, or Mesos clusters. It provides high-level APIs for SQL, streaming, machine learning, and graph processing.
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
Processing massive amount of data with Map Reduce using Apache Hadoop - Indi...IndicThreads
This document provides an overview of MapReduce and Hadoop. It describes the Map and Reduce functions, explaining that Map applies a function to each element of a list and Reduce reduces a list to a single value. It gives examples of Map and Reduce using employee salary data. It then discusses Hadoop and its core components HDFS for distributed storage and MapReduce for distributed processing. Key aspects covered include the NameNode, DataNodes, input/output formats, and the job launch process. It also addresses some common questions around small files, large files, and accessing SQL data from Hadoop.
This document provides an overview of Apache Spark, including its architecture, usage model, and capabilities. The key points covered include Spark's use of resilient distributed datasets (RDDs) to perform parallel transformations efficiently across a cluster, its support for SQL, streaming, and machine learning workloads, and how it achieves faster performance than other frameworks like MapReduce through optimizations like caching data in memory. Examples of WordCount in Spark and MapReduce are also provided and compared.
This document provides an introduction to MapReduce and Hadoop, including an overview of computing PageRank using MapReduce. It discusses how MapReduce addresses challenges of parallel programming by hiding details of distributed systems. It also demonstrates computing PageRank on Hadoop through parallel matrix multiplication and implementing custom file formats.
Ten tools for ten big data areas 03_Apache SparkWill Du
Apache Spark is a fast and general-purpose cluster computing system for large-scale data processing. It provides functions for distributed processing of large datasets across clusters using a concept called resilient distributed datasets (RDDs). RDDs allow in-memory clustering computing to improve performance. Spark also supports streaming, SQL, machine learning, and graph processing.
Hadoop became the most common systm to store big data.
With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself.
Together they form a big ecosystem.
This presentation covers some of those systems.
While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.
This document summarizes IBM's announcement of a major commitment to advance Apache Spark. It discusses IBM's investments in Spark capabilities, including log processing, graph analytics, stream processing, machine learning, and unified data access. Key reasons for interest in Spark include its performance (up to 100x faster than Hadoop for some tasks), productivity gains, ability to leverage existing Hadoop investments, and continuous community improvements. The document also provides an overview of Spark's architecture, programming model using resilient distributed datasets (RDDs), and common use cases like interactive querying, batch processing, analytics, and stream processing.
Spark is an open-source cluster computing framework. It started as a project in 2009 at UC Berkeley and was open sourced in 2010. It has over 300 contributors from 50+ organizations. Spark uses Resilient Distributed Datasets (RDDs) that allow in-memory cluster computing across clusters. RDDs provide a programming model for distributed datasets that can be created from external storage or by transforming existing RDDs. RDDs support operations like map, filter, reduce to perform distributed computations lazily.
Unified Big Data Processing with Apache SparkC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1yNuLGF.
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code. Filmed at qconsf.com.
Matei Zaharia is an assistant professor of computer science at MIT, and CTO of Databricks, the company commercializing Apache Spark.
Apache Spark is a cluster computing framework that allows for fast, easy, and general processing of large datasets. It extends the MapReduce model to support iterative algorithms and interactive queries. Spark uses Resilient Distributed Datasets (RDDs), which allow data to be distributed across a cluster and cached in memory for faster processing. RDDs support transformations like map, filter, and reduce and actions like count and collect. This functional programming approach allows Spark to efficiently handle iterative algorithms and interactive data analysis.
The document provides an overview of data science with Python and integrating Python with Hadoop and Apache Spark frameworks. It discusses:
- Why Python should be integrated with Hadoop and the ecosystem including HDFS, MapReduce, and Spark.
- Key concepts of Hadoop including HDFS for storage, MapReduce for processing, and how Python can be integrated via APIs.
- Benefits of Apache Spark like speed, simplicity, and efficiency through its RDD abstraction and how PySpark enables Python access.
- Examples of using Hadoop Streaming and PySpark to analyze data and determine word counts from documents.
This document provides an introduction to Apache Spark presented by Vincent Poncet of IBM. It discusses how Spark is a fast, general-purpose cluster computing system for large-scale data processing. It is faster than MapReduce, supports a wide range of workloads, and is easier to use with APIs in Scala, Python, and Java. The document also provides an overview of Spark's execution model and its core API called resilient distributed datasets (RDDs).
Online music portal management system project report.pdfKamal Acharya
The iMMS is a unique application that is synchronizing both user
experience and copyrights while providing services like online music
management, legal downloads, artists’ management. There are several
other applications available in the market that either provides some
specific services or large scale integrated solutions. Our product differs
from the rest in a way that we give more power to the users remaining
within the copyrights circle.
Social media management system project report.pdfKamal Acharya
The project "Social Media Platform in Object-Oriented Modeling" aims to design
and model a robust and scalable social media platform using object-oriented
modeling principles. In the age of digital communication, social media platforms
have become indispensable for connecting people, sharing content, and fostering
online communities. However, their complex nature requires meticulous planning
and organization.This project addresses the challenge of creating a feature-rich and
user-friendly social media platform by applying key object-oriented modeling
concepts. It entails the identification and definition of essential objects such as
"User," "Post," "Comment," and "Notification," each encapsulating specific
attributes and behaviors. Relationships between these objects, such as friendships,
content interactions, and notifications, are meticulously established.The project
emphasizes encapsulation to maintain data integrity, inheritance for shared behaviors
among objects, and polymorphism for flexible content handling. Use case diagrams
depict user interactions, while sequence diagrams showcase the flow of interactions
during critical scenarios. Class diagrams provide an overarching view of the system's
architecture, including classes, attributes, and methods .By undertaking this project,
we aim to create a modular, maintainable, and user-centric social media platform that
adheres to best practices in object-oriented modeling. Such a platform will offer users
a seamless and secure online social experience while facilitating future enhancements
and adaptability to changing user needs.
20CDE09- INFORMATION DESIGN
UNIT I INCEPTION OF INFORMATION DESIGN
Introduction and Definition
History of Information Design
Need of Information Design
Types of Information Design
Identifying audience
Defining the audience and their needs
Inclusivity and Visual impairment
Case study.
Conservation of Taksar through Economic RegenerationPriyankaKarn3
This was our 9th Sem Design Studio Project, introduced as Conservation of Taksar Bazar, Bhojpur, an ancient city famous for Taksar- Making Coins. Taksar Bazaar has a civilization of Newars shifted from Patan, with huge socio-economic and cultural significance having a settlement of about 300 years. But in the present scenario, Taksar Bazar has lost its charm and importance, due to various reasons like, migration, unemployment, shift of economic activities to Bhojpur and many more. The scenario was so pityful that when we went to make inventories, take survey and study the site, the people and the context, we barely found any youth of our age! Many houses were vacant, the earthquake devasted and ruined heritages.
Conservation of those heritages, ancient marvels,a nd history was in dire need, so we proposed the Conservation of Taksar through economic regeneration because the lack of economy was the main reason for the people to leave the settlement and the reason for the overall declination.
A vernier caliper is a precision instrument used to measure dimensions with high accuracy. It can measure internal and external dimensions, as well as depths.
Here is a detailed description of its parts and how to use it.
Development of Chatbot Using AI/ML Technologiesmaisnampibarel
The rapid advancements in artificial intelligence and natural language processing have significantly transformed human-computer interactions. This thesis presents the design, development, and evaluation of an intelligent chatbot capable of engaging in natural and meaningful conversations with users. The chatbot leverages state-of-the-art deep learning techniques, including transformer-based architectures, to understand and generate human-like responses.
Key contributions of this research include the implementation of a context- aware conversational model that can maintain coherent dialogue over extended interactions. The chatbot's performance is evaluated through both automated metrics and user studies, demonstrating its effectiveness in various applications such as customer service, mental health support, and educational assistance. Additionally, ethical considerations and potential biases in chatbot responses are examined to ensure the responsible deployment of this technology.
The findings of this thesis highlight the potential of intelligent chatbots to enhance user experience and provide valuable insights for future developments in conversational AI.
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!
2. Fernando Rodriguez Olivera
Twitter: @frodriguez
Professor at Universidad Austral (Distributed Systems, Compiler
Design, Operating Systems, …)
Creator of mvnrepository.com
Organizer at Buenos Aires High Scalability Group, Professor at
nosqlessentials.com
3. Apache Spark
Apache Spark is a Fast and General Engine
for Large-Scale data processing
Supports for Batch, Interactive and Stream
processing with Unified API
In-Memory computing primitives
4. Hadoop MR Limits
Job Job Job
Hadoop HDFS
- Communication between jobs through FS
- Fault-Tolerance (between jobs) by Persistence to FS
- Memory not managed (relies on OS caches)
MapReduce designed for Batch Processing:
Compensated with: Storm, Samza, Giraph, Impala, Presto, etc
5. Daytona Gray Sort 100TB Benchmark
source: http://databricks.com/blog/2014/11/05/spark-officially-sets-a-new-record-in-large-scale-sorting.html
Data Size Time Nodes Cores
Hadoop MR
(2013)
102.5 TB 72 min 2,100
50,400
physical
Apache
Spark
(2014)
100 TB 23 min 206
6,592
virtualized
6. Daytona Gray Sort 100TB Benchmark
source: http://databricks.com/blog/2014/11/05/spark-officially-sets-a-new-record-in-large-scale-sorting.html
Data Size Time Nodes Cores
Hadoop MR
(2013)
102.5 TB 72 min 2,100
50,400
physical
Apache
Spark
(2014)
100 TB 23 min 206
6,592
virtualized
3X faster using 10X fewer machines
7. Hadoop vs Spark for Iterative Proc
source: https://spark.apache.org/
Logistic regression in Hadoop and Spark
9. Resilient Distributed Datasets (RDD)
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
Immutable Collection of Objects
10. Resilient Distributed Datasets (RDD)
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
Immutable Collection of Objects
Partitioned and Distributed
11. Resilient Distributed Datasets (RDD)
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
Immutable Collection of Objects
Partitioned and Distributed
Stored in Memory
12. Resilient Distributed Datasets (RDD)
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
Immutable Collection of Objects
Partitioned and Distributed
Stored in Memory
Partitions Recomputed on Failure
13. RDD Transformations and Actions
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
14. RDD Transformations and Actions
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
e.g: apply
function
to count
chars
Compute
Function
(transformation)
15. RDD Transformations and Actions
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
11
...
...
...
...
10
5
7
...
RDD of Ints
e.g: apply
function
to count
chars
Compute
Function
(transformation)
16. RDD Transformations and Actions
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
11
...
...
...
...
10
5
7
...
RDD of Ints
e.g: apply
function
to count
chars
Compute
Function
(transformation)
depends on
17. RDD Transformations and Actions
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
11
...
...
...
...
10
5
7
...
RDD of Ints
e.g: apply
function
to count
chars
Compute
Function
(transformation)
depends on
N
Int
Action
18. RDD Transformations and Actions
Hello World
...
...
...
...
A New Line
hello
The End
...
RDD of Strings
11
...
...
...
...
10
5
7
...
RDD of Ints
e.g: apply
function
to count
chars
Compute
Function
(transformation)
Partitions
Compute Function
Dependencies
Preferred Compute
Location
(for each partition)
RDD Implementation
Partitioner
depends on
N
Int
Action
19. Spark API
val spark = new SparkContext()
val lines = spark.textFile(“hdfs://docs/”) // RDD[String]
val nonEmpty = lines.filter(l => l.nonEmpty()) // RDD[String]
val count = nonEmpty.count
Scala
SparkContext spark = new SparkContext();
JavaRDD<String> lines = spark.textFile(“hdfs://docs/”)
JavaRDD<String> nonEmpty = lines.filter(l -> l.length() > 0);
long count = nonEmpty.count();
Java8Python
spark = SparkContext()
lines = spark.textFile(“hdfs://docs/”)
nonEmpty = lines.filter(lambda line: len(line) > 0)
count = nonEmpty.count()
22. Create RDD from External Data
// Step 1 - Create RDD from Hadoop Text File
val docs = spark.textFile(“/docs/”)
Hadoop FileSystem,
I/O Formats, Codecs
HBaseS3HDFS MongoDB
Cassandra
…
Apache Spark
Spark can read/write from any data source supported by Hadoop
I/O via Hadoop is optional (e.g: Cassandra connector bypass Hadoop)
ElasticSearch
23. Function map
Hello World
A New Line
hello
...
The end
.map(line => line.toLowerCase)
RDD[String] RDD[String]
hello world
a new line
hello
...
the end
.map(_.toLowerCase)
// Step 2 - Convert lines to lower case
val lower = docs.map(line => line.toLowerCase)
=
24. Functions map and flatMap
hello world
a new line
hello
...
the end
RDD[String]
25. Functions map and flatMap
hello world
a new line
hello
...
the end
RDD[String]
.map( … )
_.split(“s+”)
a
hello
hello
...
the
world
new line
end
RDD[Array[String]]
26. Functions map and flatMap
hello world
a new line
hello
...
the end
RDD[String]
.map( … )
_.split(“s+”)
a
hello
hello
...
the
world
new line
end
RDD[Array[String]]
.flatten
hello
a
...
world
new
line
RDD[String]
*
27. Functions map and flatMap
hello world
a new line
hello
...
the end
.flatMap(line => line.split(“s+“))
RDD[String]
.map( … )
_.split(“s+”)
a
hello
hello
...
the
world
new line
end
RDD[Array[String]]
.flatten
hello
a
...
world
new
line
RDD[String]
*
28. Functions map and flatMap
hello world
a new line
hello
...
the end
.flatMap(line => line.split(“s+“))
Note: flatten() not available in spark, only flatMap
RDD[String]
.map( … )
_.split(“s+”)
a
hello
hello
...
the
world
new line
end
RDD[Array[String]]
// Step 3 - Split lines into words
val words = lower.flatMap(line => line.split(“s+“))
.flatten
hello
a
...
world
new
line
RDD[String]
*
34. Shuffling
hello
a
world
new
line
hello
1
1
1
1
1
1
.reduceByKey((a, b) => a + b)
// Step 5 - Count all words
val freq = counts.reduceByKey(_ + _)
world
a
1
1
new 1
line
hello
1
1
.groupByKey
RDD[(String, Iterator[Int])]
1
RDD[(String, Int)]
world
a
1
1
new 1
line
hello
1
2
.mapValues
_.reduce(…)
(a,b) => a+b
RDD[(String, Int)]
35. Top N (Prepare data)
world
a
1
1
new 1
line
hello
1
2
// Step 6 - Swap tuples (partial code)
freq.map(_.swap)
.map(_.swap)
world
a
1
1
new1
line
hello
1
2
RDD[(String, Int)] RDD[(Int, String)]
36. Top N (First Attempt)
world
a
1
1
new1
line
hello
1
2
RDD[(Int, String)]
37. Top N (First Attempt)
world
a
1
1
new1
line
hello
1
2
RDD[(Int, String)]
.sortByKey
RDD[(Int, String)]
hello
world
2
1
a1
new
line
1
1
(sortByKey(false) for descending)
38. Top N (First Attempt)
world
a
1
1
new1
line
hello
1
2
RDD[(Int, String)] Array[(Int, String)]
hello
world
2
1
.take(N).sortByKey
RDD[(Int, String)]
hello
world
2
1
a1
new
line
1
1
(sortByKey(false) for descending)
39. Top N
Array[(Int, String)]
world
a
1
1
new1
line
hello
1
2
RDD[(Int, String)]
world
a
1
1
.top(N)
hello
line
2
1
hello
line
2
1
local top N *
local top N *
reduction
// Step 6 - Swap tuples (complete code)
val top = freq.map(_.swap).top(N)
* local top N implemented by bounded priority queues
40. val spark = new SparkContext()
// RDD creation from external data source
val docs = spark.textFile(“hdfs://docs/”)
// Split lines into words
val lower = docs.map(line => line.toLowerCase)
val words = lower.flatMap(line => line.split(“s+“))
val counts = words.map(word => (word, 1))
// Count all words (automatic combination)
val freq = counts.reduceByKey(_ + _)
// Swap tuples and get top results
val top = freq.map(_.swap).top(N)
top.foreach(println)
Top Words by Frequency (Full Code)
43. SchemaRDD
Row
...
...
...
...
Row
Row
Row
...
topWords
case class Word(text: String, n: Int)
val wordsFreq = freq.map {
case (text, count) => Word(text, count)
} // RDD[Word]
wordsFreq.registerTempTable("wordsFreq")
val topWords = sql("select text, n
from wordsFreq
order by n desc
limit 20”) // RDD[Row]
topWords.collect().foreach(println)
44. nums = words.filter(_.matches(“[0-9]+”))
RDD Lineage
HadoopRDDwords = sc.textFile(“hdfs://large/file/”)
.map(_.toLowerCase)
alpha.count()
MappedRDD
alpha = words.filter(_.matches(“[a-z]+”))
FlatMappedRDD.flatMap(_.split(“ “))
FilteredRDD
Lineage
(built on the driver
by the transformations)
FilteredRDD
Action (run job on the cluster)
RDD Transformations
45. Deployment with Hadoop
A
B
C
D
/large/file
Data
Node 1
Data
Node 3
Data
Node 4
Data
Node 2
A A AB BBCC
CD DDRF 3
Name
Node
Spark
Master
Spark
Worker
Spark
Worker
Spark
Worker
Spark
Worker
Client
Submit App
(mode=cluster)
Driver Executors Executors Executors
allocates resources
(cores and memory)
Application
DN + Spark
HDFSSpark