Apache Spark beyond Hadoop MapReduce
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This presentation discusses Apache Spark and how it overcomes limitations of MapReduce. It begins by describing strengths of MapReduce like scalability and fault tolerance. It then covers limitations of MapReduce like lack of support for real-time and iterative processing. The presentation explains how Spark addresses these limitations by keeping data in-memory and supporting features like machine learning libraries. It highlights other Spark features such as DataFrames and SparkSQL and concludes by advertising an Apache Spark training course from Edureka.
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Démystifions l'API-culture!
En cette ère digitale, les usages changent : les IHM sont multiples, accessibles n'importe où et n'importe quand, mais surtout de plus en plus éphémères. Nos systèmes d'informations doivent évoluer afin de gérer cette accélération.
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Afterwork Blockchain : la prochaine technologie disruptive ?
Plongez au cœur des enjeux technologiques, économiques et sociétaux du 21ème siècle !
En participant à cet Afterwork, vous y verrez plus clair sur la technologie considérée comme «The Next Disruptive Technology», comme l'a été le web dans les années 90.
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Utilisée et testée depuis plus de sept ans, la Blockchain n’est plus réservée aux transferts financiers mais à un nombre de secteurs bien plus important, aussi bien privés que publics.
Dans le cadre de cet Afterwork, nous aborderons 3 enjeux :
Comprendre la technologie Blockchain, son fonctionnement, ses contraintes et limites
Où en est-on ? Quelle est la maturité des technologies ?
Avez-vous besoin de la technologie Blockchain dans votre secteur d’activité ? Quels sont les «use cases» ?
Cet Afterwork s’adresse à :
Tous ceux qui veulent saisir au plus tôt les enjeux d’une technologie qui pourrait avoir autant d’impact que le web
Aux directions Marketing / Digital / Innovation pour anticiper les enjeux de la Blockchain
Aux directions Digital & Systèmes d’Information pour soulever le capot et voir ce qu’il y a derrière le «buzz word»
Aux directions des Opérations qui sont intéressées par une réduction de leurs coûts d’opération et de leurs infrastructures de transaction
A tous les curieux qui s’intéressent aux grandes innovations technologiques
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Afterwork Blockchain : la prochaine technologie disruptive ?
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This document provides an overview of Apache Spark, including its history, features, architecture and use cases. Spark started in 2009 at UC Berkeley and was later adopted by the Apache Foundation. It provides faster processing than Hadoop by keeping data in memory. Spark supports batch, streaming and interactive processing on large datasets using its core abstraction called resilient distributed datasets (RDDs).
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This Edureka Spark Tutorial will help you to understand all the basics of Apache Spark. This Spark tutorial is ideal for both beginners as well as professionals who want to learn or brush up Apache Spark concepts. Below are the topics covered in this tutorial:
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This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
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Apache Spark Introduction.pdf
The document provides an overview of Apache Spark, including what it is, its ecosystem, features, and architecture. Some key points:
- Apache Spark is an open-source cluster computing framework for large-scale data processing. It is up to 100x faster than Hadoop for iterative/interactive algorithms.
- Spark features include its RDD abstraction, lazy evaluation, and use of DAGs to optimize performance. It supports Scala, Java, Python, and R.
- The Spark ecosystem includes tools like Spark SQL, MLlib, GraphX, and Spark Streaming. It can run on Hadoop YARN, Mesos, or in standalone mode.
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spark interview questions & answers acadgild blogs
This document discusses 20 common Apache Spark interview questions and their answers. It begins by introducing Apache Spark as a cluster computing framework for data unification and faster processing compared to MapReduce. Some key questions covered include explaining the Spark architecture of driver/worker programs, defining RDDs and their role, the difference between transformations and actions, and features like caching and broadcasting variables. Advanced topics such as Spark SQL, GraphX, and Spark Streaming are also briefly described.
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This Edureka Apache Spark Interview Questions and Answers tutorial helps you in understanding how to tackle questions in a Spark interview and also gives you an idea of the questions that can be asked in a Spark Interview. The Spark interview questions cover a wide range of questions from various Spark components. Below are the topics covered in this tutorial:
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Spark from the Surface
Apache Spark is an open-source distributed processing engine that is up to 100 times faster than Hadoop for processing data stored in memory and 10 times faster for data stored on disk. It provides high-level APIs in Java, Scala, Python and SQL and supports batch processing, streaming, and machine learning. Spark runs on Hadoop, Mesos, Kubernetes or standalone and can access diverse data sources using its core abstraction called resilient distributed datasets (RDDs).
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https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark
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Apache Spark PDF
Spark is a cluster computing framework designed to be fast, general-purpose, and able to handle a wide range of workloads including batch processing, iterative algorithms, interactive queries, and streaming. It is faster than Hadoop for interactive queries and complex applications by running computations in-memory when possible. Spark also simplifies combining different processing types through a single engine. It offers APIs in Java, Python, Scala and SQL and integrates closely with other big data tools like Hadoop. Spark is commonly used for interactive queries on large datasets, streaming data processing, and machine learning tasks.
Introduction to Spark - DataFactZ
We are a company driven by inquisitive data scientists, having developed a pragmatic and interdisciplinary approach, which has evolved over the decades working with over 100 clients across multiple industries. Combining several Data Science techniques from statistics, machine learning, deep learning, decision science, cognitive science, and business intelligence, with our ecosystem of technology platforms, we have produced unprecedented solutions. Welcome to the Data Science Analytics team that can do it all, from architecture to algorithms.
Our practice delivers data driven solutions, including Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics. We employ a number of technologies in the area of Big Data and Advanced Analytics such as DataStax (Cassandra), Databricks (Spark), Cloudera, Hortonworks, MapR, R, SAS, Matlab, SPSS and Advanced Data Visualizations.
This presentation is designed for Spark Enthusiasts to get started and details of the course are below.
1. Introduction to Apache Spark
2. Functional Programming + Scala
3. Spark Core
4. Spark SQL + Parquet
5. Advanced Libraries
6. Tips & Tricks
7. Where do I go from here?
Introduction to apache spark
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.
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
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?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
«Почему Spark отнюдь не так хорош»
Алексей Дёмин (Java Developer в InData Labs)
Доклад: «Почему Spark отнюдь не так хорош»
О чём: Сейчас по всем каналам идёт обсуждение новой революционной технологии обработки данных Spark. Алексей предлагает взглянуть чуть глубже и узнать, действительно ли Spark так хорош, как нам рассказывает об этом маркетинг.
Cleveland Hadoop Users Group - Spark
Spark is a unified analytics engine for large-scale data processing. It provides APIs for SQL queries, streaming data, and machine learning. Spark uses RDDs (Resilient Distributed Datasets) as its fundamental data abstraction, which allows data to be operated on in parallel. RDDs track lineage information to efficiently recover lost data. Spark offers advantages over MapReduce like being faster, using less code, and supporting iterative algorithms. It can also be used for both batch and streaming workloads using the same APIs. While still maturing, Spark is gaining popularity for its ease of use and performance.
Introduction to spark
Spark was introduced by Apache Software Foundation for speeding up the Hadoop computational computing software process.
Spark Hadoop Tutorial | Spark Hadoop Example on NBA | Apache Spark Training |...
This Edureka Spark Hadoop Tutorial will help you understand how to use Spark and Hadoop together. This Spark Hadoop tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Spark Overview
2) Hadoop Overview
3) Spark vs Hadoop
4) Why Spark Hadoop?
5) Using Hadoop With Spark
6) Use Case - Sports Analytics (NBA)
Big data processing with apache spark part1
Apache Spark is an open source big data processing framework that is faster than Hadoop, easier to use, and supports more types of analytics. It provides high-level APIs, can run computations directly in memory for faster performance, and supports a variety of data processing workloads including SQL queries, streaming data, machine learning, and graph processing. Spark also has a large ecosystem of additional libraries and tools that expand its capabilities.
Bring the Spark To Your Eyes
Demi Ben-Ari is a senior software engineer at Windward Ltd. who has a BS in computer science. They previously worked as a software team leader and senior Java engineer developing missile defense and alert systems. The presentation discusses Spark, an open-source cluster computing framework, and how Windward uses Spark for data filtering, management, predictions and more through Java applications running on YARN clusters.
Spark architecture
The document discusses Apache Spark, an open source cluster computing framework for real-time data processing. It notes that Spark is up to 100 times faster than Hadoop for in-memory processing and 10 times faster on disk. The main feature of Spark is its in-memory cluster computing capability, which increases processing speeds. Spark runs on a driver-executor model and uses resilient distributed datasets and directed acyclic graphs to process data in parallel across a cluster.
Apache spark
This was a basic presentation of Apache Spark for starters who are just looking to start their journey learning spark.
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A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Installation on Ubuntu | Edureka
YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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Introduction to DevOps | Edureka
YouTube Link: https://youtu.be/GJQ36pIYbic
DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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Apache Spark beyond Hadoop MapReduce
- 1. www.edureka.co/r-for-analytics www.edureka.co/apache-spark-scala-training Apache Spark: Beyond Hadoop MapReduce Presenter: Vishal
- 2. Slide 2Slide 2Slide 2 www.edureka.co/apache-spark-scala-training What will you learn today? Strength of MapReduce Limitations of MapReduce How MapReduce limitations can be overcome How Spark fits the bill Other exciting features in Spark
- 4. Slide 4Slide 4Slide 4 www.edureka.co/apache-spark-scala-training Simple Scalable Fault Tolerant Minimal data motion Strength of MapReduce Independent of a programming language, such as Java, C++ or Python. It can process petabytes of data, stored in HDFS on one cluster MapReduce takes care of failures using the replicated copies. Process moves towards data to minimize Disk I/O
- 6. Slide 6Slide 6Slide 6 www.edureka.co/apache-spark-scala-training Real Time Complex Algorithm Re-reading and parsing Data Minimal Data Motion Graph Processing Iterative Tasks Random Access Limitations Of MR
- 7. Slide 7Slide 7Slide 7 www.edureka.co/apache-spark-scala-training Feature Comparison with Spark Fast 100x faster than MapReduce Batch Processing Batch and Real-time Processing Stores Data on Disk Stores Data in Memory Written in Java Written in Scala Hadoop MapReduce Hadoop Spark Source: Databrix
- 8. What are the MR limitations and how Spark overcomes it?
- 9. Slide 9Slide 9Slide 9 www.edureka.co/apache-spark-scala-training Overcoming MR limitations By Cutting down on the number of Reads and Writes to the disc Real time
- 10. Slide 10Slide 10Slide 10 www.edureka.co/apache-spark-scala-training Spark tries to keep things in-memory of its distributed workers, allowing for significantly faster/lower-latency computations, whereas MapReduce keeps shuffling things in and out of disk. Spark Cuts Down Read/Write I/O To Disk
- 11. Slide 11Slide 11Slide 11 www.edureka.co/apache-spark-scala-training Overcoming MR limitations Libraries for Machine Learning & Streaming Graph processing Complex algorithm
- 12. Slide 12Slide 12Slide 12 www.edureka.co/apache-spark-scala-training Libraries For ML, Graph Programming … Machine Learning Library Graph programming Spark interface For RDBMS lovers Utility for continuous ingestion of data
- 13. Slide 13Slide 13Slide 13 www.edureka.co/apache-spark-scala-training Overcoming MR limitations Cyclic data flows Random access
- 14. Slide 14Slide 14Slide 14 www.edureka.co/apache-spark-scala-training Cyclic Data Flows • All jobs in spark comprise a series of operators and run on a set of data. • All the operators in a job are used to construct a DAG (Directed Acyclic Graph). • The DAG is optimized by rearranging and combining operators where possible.
- 15. Slide 15Slide 15Slide 15 www.edureka.co/apache-spark-scala-training Spark Features makes its Architecture better than MR
- 16. Other Spark Features In Demand
- 17. Slide 17Slide 17Slide 17 www.edureka.co/apache-spark-scala-training Spark Features/Modules In Demand Source: Typesafe
- 18. Slide 18Slide 18Slide 18 www.edureka.co/apache-spark-scala-training New Features In 2015 Data Frames • Similar API to data frames in R and Pandas • Automatically optimised via Spark SQL • Released in Spark 1.3 SparkR • Released in Spark 1.4 • Exposes DataFrames, RDD’s & MLlibrary in R Machine Learning Pipelines • High Level API • Featurization • Evaluation • Model Tuning External Data Sources • Platform API to plug Data-Sources into Spark • Pushes logic into sources Source: Databrix
- 19. Slide 19Slide 19Slide 19 www.edureka.co/apache-spark-scala-training Get Certified in Spark from Edureka Edureka's Spark and Scala course: • Learn large-scale data processing by mastering the concepts of Scala, RDD, Traits, OOPS and Spark SQL • Online Live Courses: 24 hours • Assignments: 32 hours • Project: 20 hours • Lifetime Access + 24 X 7 Support Go to www.edureka.co/apache-spark-scala-training Batch starts from 10th October (Weekend Batch)
- 20. Thank You Questions/Queries/Feedback/Survey Recording and presentation will be made available to you within 24 hours