Apache spark vs flink

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Flink Vs Spark | Difference between Flink & Spark - Apache Flink Tutorial

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03.10.2018

Link : 🤍 FLINK vs SPARK - In this video we are going to learn the difference between Apache Flink and Spark. Actually there is a very thin line of difference between Flink and Spark as both are stream processing engines But Unlike Spark, Fink is a True Stream processing Engine. This lecture is a taken out from the full Apache Flink Tutorial course. Learn Apache Flink vs Apache Spark from this video and if you want learn more about Flink then you can click on the link given below to get the full course on Apache Flink Tutorial. 🤍

Spark vs Flink

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07.02.2022

There are various Big Data Ingestion tools available in the market. It is always confusing which one to choose and when? So let's explore the difference between Spark and Flink and clear this confusion! Let’s come together in Joining our strong 3500+ 𝐦𝐞𝐦𝐛𝐞𝐫𝐬 community where we impart our knowledge regularly on Data, ML, AI, and many more technologies: 🤍 𝐒𝐭𝐚𝐲 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐮𝐬! 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐨𝐮𝐫 𝐲𝐨𝐮𝐭𝐮𝐛𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐰𝐞𝐛𝐢𝐧𝐚𝐫𝐬: 🤍 Comment, Like, Share and Subscribe to our YouTube Channel! #Spark #Flink #SparkvsFlink #Differences #BigData #Technology #DataCouch

Comparing Apache Flink and Apache Spark frameworks

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26.04.2020

Comparing Apache Spark and Apache Flink frameworks on parameters like speed, abstraction, streaming model, real time processing,language.

Scalable Stream Processing: A Survey of Storm, Samza, Spark and Flink by Felix Gessert

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17.05.2017

Batch-oriented systems have done the heavy lifting in data-intensive applications for decades, but they do not reflect the unbounded and continuous nature of data as it is produced in many real-world applications. Stream-oriented systems, on the other hand, process data as it arrives and thus are oftentimes the more natural fit. A great number of stream processors have emerged over the last years and all are advertised as highly available, fault-tolerant and horizontally scalable. But where do these systems differ and which is the right one for a given use case? In this talk, we give an overview of the state of the art of stream processors for low-latency Big Data analytics and conduct a qualitative comparison of the most popular contenders, namely Storm and its abstraction layer Trident, Samza, Flink and Spark Streaming. We first cover how stream processing frameworks differ from batch-oriented systems (e.g. Hadoop and Spark) and how they are typically employed (Lambda & Kappa Architecture). We then go into detail on each system and inspect their respective rationales, guarantees, and trade-offs. As an illustrative example we will cover real-time machine learning use-cases. Felix Gessert is CEO and co-founder of Baqend. Baqend develops a cloud backend to help programmers build instantly-loading websites with a novel caching algorithm. Felix received his master of computer science from the University of Hamburg and founded Baqend in 2014 with fellow students. His PhD thesis is concerned with the technical foundations of Baqend. His major interests are scalable database systems, transactions, web technologies for cloud data management and steaks. Felix is passionate about leveraging and improving NoSQL systems for web applications. He frequently talks and writes about the related challenges and organizes a conference series on cloud databases.

The different Streams Processing

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26.02.2019

Learn the differences between Kafka Streams, Spark Streaming, NiFi, Flink and the one to use! If you want to learn more: 🤍 Get the Kafka Streams for Data Processing course at a special price! Don’t forget to subscribe to get more content about Apache Kafka and AWS! I'm Stephane Maarek, a consultant and software developer, and I have a particular interest in everything related to Big Data, Cloud and API. I sat on the 2019 Program Committee organizing the Kafka Summit. I'm also an AWS Certified Solutions Architect, Developer, SysOps Administrator, and DevOps Engineer. My other courses are available here: 🤍 Follow me on social media: LinkedIn - 🤍 Twitter - 🤍 Medium - 🤍

Streaming frameworks (3/3): Comparison Frameworks

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01.03.2018

In this third video on Streaming Frameworks, Bram Steurtewagen, Data Scientist at Klarrio and PhD-student, explains the benchmark of streaming frameworks that the PhD students are doing at Klarrio. In the benchmark they are comparing four streaming frameworks (Kafka Stream, Storm, Spark & Flink) in order to find out which framework is best used for which kind of use case.

13.10 Apache Spark and Apache Flink

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01.03.2022

This is the Section 13 which is going to describe Section 13:Data Engineering in details. Currently you are in Section 13: and Briefly describe sections are:- Data : 🤍 Section 1: Introduction Section 2: Machine Learning 101 (End to End) Section 3: Machine Learning and Data Science Framework Section 4: The 2 Paths Section 5: Data Science Environment Setup Section 6: Pandas Data Analysis Section 7: Numpy Section 8: Matplotlib Plotting and Data Visualization Section 9: Scikit-learn Creating Machine Learning Model Section 10: Supervised Learning Classification + Regression Section 11: Project 1 Supervised Learning ( Classification ) Section 12: Project 2 Supervised Learning ( Time Series Data - Regression) Section 13: Data Engineering Section 14: Neural Network, Deep Learning, Transfer Learning, TensorFlow Section 15: Story Telling + Communication How to present your work Section 16: Career Advice + Extra Bits Section 17: Learn Python Section 18: Learn Python Part 2 Section 19: Bonus Learn Advance Statistics and Mathematics Section 20: Where to Go from Here Section 21: Bonus Section Get Excited, Let's Start... Diffrence Between OLTP and OLAP : 🤍 ACID Transaction : 🤍 Feature Scaling for machine learning : 🤍 Feature Scaling with scikit-learn : 🤍 Feature Scaling why it is required : 🤍 Filling Missing Data with scikit-learn colab: 🤍 ROC and AUC Curve : 🤍 Recieve Operating Characterstics (ROC) : 🤍 Google Machine Learning (RAC and AUC) : 🤍 Bulldozer Price Prediction github : 🤍 Kaggle Problem : 🤍 Dog Breed Identification : 🤍 Bulldozer Data : 🤍 kaggle-dataset-download-example : 🤍 Pandas Catagorical : 🤍 Bulldozer Price Prediction : 🤍

Apache Flink vs. Apache Spark para Processamento de Dados em Tempo-Real | Live #68

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26.05.2022

Nesta Live, você vai descobrir as melhores práticas para desenvolver pipeline de dados em tempo-real, utilizando o Apache Kafka como fonte de processamento de dados. O Apache Spark oferece o Structured Streaming como API para se conectar com algumas fontes de dados em tempo-real e realizar o processamento de forma unificada, utilizando o conceito de DataFrames. O Apache Flink disponibiliza o Table API [Python], para a realização do processamento de dados em tempo-real e consumir esses dados do Apache Kafka. Teremos demonstração sobre essas APIs, para que você entenda os lados positivos e negativos de cada um deles. Mantenha-se sempre informado: em nosso Podcast, Engenharia de Dados Cast, compartilhamos todo o nosso conhecimento na área e ainda trazemos profissionais de renome mundial para falar dos temas mais atuais. Inscreva-se pelo link: 🎙️ 🤍 #ApacheFlick #ApacheSpark #EngenhariaDeDadosAcademy

Flink vs Kafka Streams/ksqlDB: Comparing Stream Processing Tools

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26.05.2022

🤍 | Stream processing can be hard or easy depending on the approach you take, and the tools you choose. This sentiment is at the heart of the discussion with Matthias J. Sax (Apache Kafka® PMC member; Software Engineer, ksqlDB and Kafka Streams, Confluent) and Jeff Bean (Sr. Technical Marketing Manager, Confluent). With immense collective experience in Kafka, ksqlDB, Kafka Streams, and Apache Flink®, they delve into the types of stream processing operations and explain the different ways of solving for their respective issues. The best stream processing tools they consider are Flink along with the options from the Kafka ecosystem: Java-based Kafka Streams and its SQL-wrapped variant—ksqlDB. Flink and ksqlDB tend to be used by divergent types of teams, since they differ in terms of both design and philosophy. Why Use Apache Flink? The teams using Flink are often highly specialized, with deep expertise, and with an absolute focus on stream processing. They tend to be responsible for unusually large, industry-outlying amounts of both state and scale, and they usually require complex aggregations. Flink can excel in these use cases, which potentially makes the difficulty of its learning curve and implementation worthwhile. Why use ksqlDB/Kafka Streams? Conversely, teams employing ksqlDB/Kafka Streams require less expertise to get started and also less expertise and time to manage their solutions. Jeff notes that the skills of a developer may not even be needed in some cases—those of a data analyst may suffice. ksqlDB and Kafka Streams seamlessly integrate with Kafka itself, as well as with external systems through the use of Kafka Connect. In addition to being easy to adopt, ksqlDB is also deployed on production stream processing applications requiring large scale and state. There are also other considerations beyond the strictly architectural. Local support availability, the administrative overhead of using a library versus a separate framework, and the availability of stream processing as a fully managed service all matter. Choosing a stream processing tool is a fraught decision partially because switching between them isn't trivial: the frameworks are different, the APIs are different, and the interfaces are different. In addition to the high-level discussion, Jeff and Matthias also share lots of details you can use to understand the options, covering employment models, transactions, batching, and parallelism, as well as a few interesting tangential topics along the way such as the tyranny of state and the Turing completeness of SQL. EPISODE LINKS ► The Future of SQL: Databases Meet Stream Processing: 🤍 ► Building Real-Time Event Streams in the Cloud, On Premises: 🤍 ► Kafka Streams 101 course: 🤍 ► ksqlDB 101 course: 🤍 ► Kris Jenkins’ Twitter: 🤍 ► Join the Confluent Community: 🤍 ► Learn more with Kafka tutorials, resources, and guides: 🤍 ► Live demo: Intro to Event-Driven Microservices with Confluent: 🤍 ► Use PODCAST100 to get $100 of free Confluent Cloud usage: 🤍 ► Promo code details: 🤍 TIMESTAMPS 0:00 - Intro 2:06 - The world of stream processing 6:26 - Flink vs ksqlDB 18:34 - Example use case 20:03 - SQL was built for static data 25:51 - Concept of event time 29:30 - Session-based window joins 35:47 - Processing streaming data with SQL 39:47 - Scaling Kafka Streams/ksqlDB 45:39 - Exactly-once semantics 48:15 - Choosing stream processing tools 53:52 - It's a wrap CONNECT Subscribe: 🤍 Site: 🤍 GitHub: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Instagram: 🤍 ABOUT CONFLUENT Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations. To learn more, please visit 🤍confluent.io. #streamprocessing #ksqldb #apachekafka #kafka #confluent

How to start learning Apache Kafka and Flink!

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How to start learning Apache Kafka and Flink? Check out this video to find out! ►Learn Data Engineering with my Data Engineering Academy: 🤍 ►LEARN MORE ABOUT DATA ENGINEERING: -Check out my free 100+ page data engineering cookbook on GitHub: 🤍 ►PLEASE SUPPORT WHAT YOU LIKE: Just use this link whenever you buy something from Amazon: 🤍 (As an Amazon Associate I earn from qualifying purchases from Amazon) This is free of charge for you but super helpful for supporting this channel. #coding #dataengineer #dataeningeering

Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training

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This Apache Flink Tutorial will bring out the strength of Flink for real-time streaming. This training video will give you an understanding on how Apache Flink stands out among the other real-time streaming tools like Apache Spark and Apache Storm. You will look into two use-cases and explore the true streaming capability of Apache Flink. You will also understand Apache Flink Architecture which is based on Kappa Architecture. Below are the list of topics that are covered in this Apache Flink Tutorial: 1) Batch Vs Real Time Analytics 2) How Industry is Leveraging Analytics? 3) Spark - Most Popular Tool for Real Time Analytics 4) Why Apache Flink? 5) Use Case I - Bouygues Telecom 6) Use Case II - Extended Yahoo Streaming Benchmark 7) Who is Using Apache Flink? 8) Lambda vs Kappa Architecture Subscribe to our channel to get video updates. Hit the subscribe button above. #ApacheFlink #FlinkTutorial #FlinkTraining Please write back to us at sales🤍edureka.co or call us at +91 88808 62004 for more information. Website: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Customer Review: Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favorite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! Edureka lets you go back later, when your boss says "I want this ASAP!" ~ This is the killer education app... I've take two courses, and I'm taking two more.”

07.05 Spark versus flink

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Big Data for Architects

PREVIEW: Stateful Stream Processing with Kafka & Flink (S. Ewen, data Artisans) Kafka Summit 2018

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03.05.2018

NOTE: This is a preview. All Kafka Summit videos available in full at: 🤍 | Come learn how the combination of Apache Kafka and Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable. The new world of applications and fast data architectures has broken up the database: Raw data persistence comes in the form of event logs, and the state of the world is computed by a stream processor. Apache Kafka provides a strong solution for the event log, while Apache Flink forms a powerful foundation for the computation over the event streams. In this talk we discuss how Flink’s abstraction and management of application state have evolved over time and how Flink’s snapshot persistence model and Kafka’s log work together to form a base to build ‘versioned applications’. We will also show how end-to-end exactly-once processing works through a smart integration of Kafka’s transactions and Flink’s checkpointing mechanism. CONNECT Subscribe: 🤍 Site: 🤍 GitHub: 🤍 Facebook: 🤍 Twitter: 🤍 Linkedin: 🤍 Instagram: 🤍 ABOUT CONFLUENT Confluent, founded by the creators of Apache Kafka®, enables organizations to harness business value of live data. The Confluent Platform manages the barrage of stream data and makes it available throughout an organization. It provides various industries, from retail, logistics and manufacturing, to financial services and online social networking, a scalable, unified, real-time data pipeline that enables applications ranging from large volume data integration to big data analysis with Hadoop to real-time stream processing. To learn more, please visit 🤍

Big Data Spark And Flink Streaming

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30.09.2015

This course is designed for beginners, no Meaning Programming experience is required. Will You start by learning what Big Data is and how to Process it with MapReduce and Hadoop. From there, Vladimir will teach you several ways to program Big Data applications, as well as introduce you to the Hadoop ecosystem. This video tutorial also covers NoSQL stores and their best uses. Finally, you Will learn About Big Data and NoSQL in the Enterprise. Once you have completed this computer based Training course, you Will have developed A Solid Understanding of Big Data and NoSQL Technologies. The explanation of the concept "Big Data."

Beyond Classic Hadoop Spark And Flink

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30.09.2015

Watch all my videos for full course.... Please Like Share and Subscribe for more new training videos. This course is designed for beginners, no Meaning Programming experience is required. Will You start by learning what Big Data is and how to Process it with MapReduce and Hadoop. From there, Vladimir will teach you several ways to program Big Data applications, as well as introduce you to the Hadoop ecosystem. This video tutorial also covers NoSQL stores and their best uses. Finally, you Will learn About Big Data and NoSQL in the Enterprise. Once you have completed this computer based Training course, you Will have developed A Solid Understanding of Big Data and NoSQL Technologies. The explanation of the concept "Big Data."

21.04.2016 Spark vs. Flink - Rumble in the Big Data Jungle Meetup

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26.04.2016

Mehr Infos auf unserer Website: 🤍 Nachdem sich Apache Spark im letzten Jahr als ernsthafte Alternative unter den Big Data Frameworks etablieren konnte und Hadoop MapReduce den Rang abläuft, kommt nun aus Berlin unerwartet Konkurrenz in Form von Apache Flink. Was für eine Konkurrenz das ist, findet ihr in diesem Meetup heraus! Meinungen zu diesem Meetup: "Thanks for the really interesting talk!" Cindy L. "Wieder ein super Event! Vielen Dank an die beiden Redner und an den tollen Gastgeber!" Wolf K. "Nice talks, awesome location, friendly people and delicious food + drinks" Frank R.

Wednesday Webinar: Big Data Analysis Using Cosmos With Spark or Flink

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09.07.2020

FIWARE Wednesday Webinars - Performing Big Data Analysis Using Cosmos With Spark or Flink - 8 July 2020 The corresponding slide decks can be found here: 🤍 Check out the #WednesdayWebinars playlist for more FIWARE webinars: 🤍 Chapter: Core Difficulty: 4 Audience: Any Technical Speakers: Joaquín Salvachúa Rodríguez (UPM), Andrés Muñoz Arcentales (UPM), Sonsoles López Pernas (UPM)

GoldenGate Stream Analytics vs. Apache Flink, Confluent KSQL, and Apache Spark

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15.04.2022

Check out our sessions from the 2022 GoldenGate Customer Summit for North America! We were thrilled to have nearly 1,000 customers across the USA register to join us for the live event in March, 2022. For those who couldn't make it, or others who missed it altogether, we are happy to share some of the curated sessions from the event. On this playlist you'll find: - Keynote opener about GoldenGate, Data Fabric and Data Mesh - Customer discussion with Lowe's and Deloitte - GoldenGate Mesh vs. Hub - which is right for you? - GG in the Cloud (OCI, AWS, Azure, GCP) and at the Edge (with demo) - GG Stream Analytics vs. Apache Flink, Confluent KSQL, and Apache Spark - DM Migrations the easy way, with OCI DB Migration service (with demo) - GoldenGate for Oracle - Microservices and High Availability updates - Demo of migration utility (Classic Architecture to Microservices) - GoldenGate for Big Data - Demonstration of GoldenGate for Snowflake - GoldenGate for Non Oracle (SQL Server, Postgres, DB2, NonStop etc) - GoldenGate Foundation Suite We hope these videos help you discover new uses for GoldenGate and answer any questions you may have!

BigPetStore on Spark and Flink Implementing use cases on unified Big Data engines

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06.03.2018

by Marton Balassi At: FOSDEM 2017 Implementing use cases on unified data platforms. Having a unified dataprocessing engine empowers Big Data application developers as it makesconnections between seemingly unrelated use cases natural. This talk discussesthe implementation of the so-called BigPetStore project (which is a part ofApache Bigtop) in Apache Spark and Apache Flink. The aim BigPetStore is toprovide a common suite to test and benchmark Big Data installations. The talkfeatures best practices and implementation with the batch, streaming, SQL,DataFrames and machine learning APIs of Apache Spark and Apache Flink side byside. A range of use cases are outlined in both systems from data generation,through ETL, recommender systems to online prediction. Session type Lecture Session length 30 min + 5 min discussion Expected prior knowledge / intended audience Basic exposure to Big DataSystems Speaker bio Márton Balassi is a Solution Architect at Cloudera and a PMCmember at Apache Flink. He focuses on Big Data application development,especially in the streaming space. Marton is a regular contributor to opensource and has been a speaker of a number of open source Big Data relatedconferences including Hadoop Summit and Apache Big Data and meetups recently. Room: H.2213 Scheduled start: 2017-02-04 15:30:00

Apache Kafka and Flink: Stateful Streaming Data Pipelines made easy with SQL

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24.10.2021

A stateful streaming data pipeline needs both a solid base and an engine to drive the data. Apache Kafka is an excellent choice for storing and transmitting high throughput and low latency messages. Apache Flink adds the cherry on top with a distributed stateful compute engine available in a variety of languages, including SQL. In this session we'll explore how Apache Flink operates in conjunction with Apache Kafka to build stateful streaming data pipelines, and the problems we can solve with this combination. We will explore Flink's SQL client, showing how to define connections and transformations with the most known and beloved language in the data industry. This session is aimed at data professionals who want to reduce the barrier to streaming data pipelines by making them configurable as a set of simple SQL commands. Francesco Tisiot, Aiven

Spark Structured Streaming vs Spark Streaming Differences

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05.09.2021

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Streaming Concepts & Introduction to Flink Series - What is Stream Processing & Apache Flink

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09.07.2020

Series: Streaming Concepts & Introduction to Flink Part 1: What is Stream Processing & Apache Flink This series of videos introduces the Apache Flink stream processing framework and covers core concepts of the technology. This is the first video of the series discussing what is stateful stream processing, how it relates to batch processing and where the need for stateful stream processing comes from. For more information or questions: - Part 2 | Apache Flink Dataflow & Snapshots: 🤍 - Part 3 | Use Case: Event-Driven Applications: 🤍 - Part 4 | Flink’s Runtime Architecture & Deployment Options: 🤍 -Part 5 | Apache Flink Event Time and Watermarks: 🤍 - BONUS part | Exactly Once Fault Tolerance Guarantees: 🤍 - What is Stream Processing: 🤍 - Ververica Contact: 🤍

Introduction to Apache Flink | Stream processing framework for big data | Hadoop Full Course

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17.12.2022

This lecture is all about Introduction to Apache Flink which is nothing but a data stream processing framework for big data where we have seen what is Apache Flink, its main features, Flink ecosystem and Flink architecture in detail. In the previous lecture we have seen Apache Spark Streaming hands-on exercise where we have processed web logs ingested using Apache Flume using spark streaming code written in Python. For Flume configuration, we have used spooldir as a source and Avro as a sink and then processed the data using PySpark Streaming code. Below are the required commands for this lecture: wget 🤍 wget 🤍 wget 🤍 mkdir checkpoint export SPARK_MAJOR_VERSION=24 spark-submit packages org.apache.spark:spark-streaming-flume_2.11:2.3.0 SparkFlume.py cd /usr/hdp/current/flume-server/ bin/flume-ng agent conf conf conf-file /home/maria_dev/sparkstreamingflume.conf name a1 cp access_log.txt spool/log1.txt HDP Sandbox Installation links: Oracle VM Virtualbox: 🤍 HDP Sandbox link: 🤍 HDP Sandbox installation guide: 🤍 - Also check out similar informative videos in the field of cloud computing: What is Big Data: 🤍 How Cloud Computing changed the world: 🤍 What is Cloud? 🤍 Top 10 facts about Cloud Computing that will blow your mind! 🤍 Audience This tutorial is made for professionals who are willing to learn the basics of Big Data Analytics using Hadoop Ecosystem and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course. Prerequisites Before you start proceeding with this course, I am assuming that you have some basic knowledge to Core Java, database concepts, and any of the Linux operating system flavors. - Check out our full course topic wise playlist on some of the most popular technologies: SQL Full Course Playlist- 🤍 PYTHON Full Course Playlist- 🤍 Data Warehouse Playlist- 🤍 Unix Shell Scripting Full Course Playlist- 🤍 -Don't forget to like and follow us on our social media accounts: Facebook- 🤍 Instagram- 🤍 Twitter- 🤍 Tumblr- ampcode.tumblr.com - Channel Description- AmpCode provides you e-learning platform with a mission of making education accessible to every student. AmpCode will provide you tutorials, full courses of some of the best technologies in the world today.By subscribing to this channel, you will never miss out on high quality videos on trending topics in the areas of Big Data & Hadoop, DevOps, Machine Learning, Artificial Intelligence, Angular, Data Science, Apache Spark, Python, Selenium, Tableau, AWS , Digital Marketing and many more. #bigdata #datascience #dataanalytics #datascientist #hadoop #hdfs #hdp #mongodb #cassandra #hbase #nosqldatabase #nosql #pyspark #spark #presto #hadooptutorial #hadooptraining

Hadoop vs Spark | Hadoop And Spark Difference | Hadoop And Spark Training | Simplilearn

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00:10:01
06.12.2019

🔥Professional Certificate Program In Data Engineering: 🤍 Hadoop and Spark are the two most popular big data technologies used for solving significant big data challenges. In this video, you will learn which of them is faster based on performance. You will know how expensive they are and which among them is fault-tolerant. You will get an idea about how Hadoop and Spark process data, and how easy they are for usage. You will look at the different languages they support and what's their scalability. Finally, you will understand their security features, which of them has the edge over machine learning. Now, let's get started with learning Hadoop vs. Spark. We will differentiate based on below categories 1. Performance 00:52 2. Cost 01:40 3. Fault Tolerance 02:31 4. Data Processing 03:06 5. Ease of Use 04:03 6. Language Support 04:52 7. Scalability 05:55 8. Security 06:38 9. Machine Learning 08:02 10. Scheduler 08:56 🔥 Enroll for FREE Big Data Hadoop Spark Course & Get your Completion Certificate: 🤍 To learn more about Hadoop, subscribe to our YouTube channel: 🤍 To access the slides, click here: 🤍 Watch more videos on HadoopTraining: 🤍 #HadoopvsSpark #HadoopAndSpark #HadoopAndSparkDifference #DifferenceBetweenHadoopAndSpark #WhatIsHadoop #WhatIsSpark #LearnHadoop #HadoopTraining #SparkTraining #HadoopCertification #SimplilearnHadoop #Simplilearn ➡️ Professional Certificate Program In Data Engineering This Data Engineering course is ideal for professionals, covering critical topics like the Hadoop framework, Data Processing using Spark, Data Pipelines with Kafka, Big Data on AWS, and Azure cloud infrastructures. This program is delivered via live sessions, industry projects, masterclasses, IBM hackathons, and Ask Me Anything sessions. ✅ Key Features - Professional Certificate Program Certificate and Alumni Association membership - Exclusive Master Classes and Ask me Anything sessions by IBM - 8X higher live interaction in live Data Engineering online classes by industry experts - Capstone from 3 domains and 14+ Projects with Industry datasets from YouTube, Glassdoor, Facebook etc. - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Skills Covered - Real Time Data Processing - Data Pipelining - Big Data Analytics - Data Visualization - Provisioning data storage services - Apache Hadoop - Ingesting Streaming and Batch Data - Transforming Data - Implementing Security Requirements - Data Protection - Encryption Techniques - Data Governance and Compliance Controls 👉Learn More at: 🤍 For more information about Simplilearn courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 Get the Android app: 🤍 Get the iOS app: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

Beam on Flink: How does it actually work? - Maximilian Michels

5200
43
2
00:38:10
17.10.2019

Apache Beam is a data processing model built with focus on portability. Beam jobs can be written in the language of your choice: Java, Python, Go, or SQL. Once written, they can be executed using various execution engines including Apache Flink, Apache Spark, Google Cloud Dataflow, and many more. In order for Beam to support multiple execution engines, the Beam API needs to be translated to the API of the execution engine (e.g. Flink's). In Beam, this is the responsibility of the ""Runner"". The Flink Runner has come a long way from an early stage Runner to a fully-featured Runner. Its latest addition is the integration of Beam's language portability layer which enabled to run jobs written in other languages than Java. In this talk, we will dissect the Flink Runner and show how Beam's components tie together with Flink. If you have ever wondered how the Flink Runner or Beam works, this is your chance to find out.

SF Spark: Denis Magda, Apache Spark, Apache Flink, and Apache Ignite: Where Fast Data Meets the IoT

232
1
0
00:46:08
27.09.2017

Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: 🤍. Join us! - Apache Spark and Apache Flink are thoroughly covered at the upcoming Scale By the Bay conference: scale.bythebay.io. Stephan Ewen, a founder of Flink, keynotes the conference this year. Early Bird ends on 8/31, get your tickets soon! GridGain is an in-memory computing pioneer with products for high-performance environments such as Wall St. It runs an In-Memory Computing Summit in October. Speaker: Denis Magda, GridGain It is not enough to build a mesh of sensors or embedded devices to obtain more insights about the surrounding environment and optimize your production systems. Usually, your IoT solution needs to be capable of transferring enormous amounts of data to storage or the cloud where the data have to be processed further. Quite often, the processing of the endless streams of data has to be done in real-time so that you can react on the IoT subsystem's state accordingly. This session will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources. In particular, attendees will learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Flink. Denis Magda, GridGain Systems product manager and Apache® Ignite™ PMC Chair, is an expert in distributed systems and platforms. Before joining GridGain and becoming a part of Apache® Ignite™ community, he worked for Oracle where he led the Java ME Embedded Porting Team helping Java cross new into new territory by entering the IoT market.

Why Spark is Faster Than Hadoop MapReduce

7509
87
10
00:07:34
01.08.2019

In this video I talk about why Apache Spark's in memory processing. That's why Spark is so much faster than Mapreduce or other analytics frameworks. it's simple but awesome for stream processing and batch processing. That's why I explain first what stream and batch processing is. ►Learn Data Engineering with my Data Engineering Academy: 🤍 Check out my free 100+ pages data engineering cookbook on GitHub: 🤍 Please SUPPORT WHAT YOU LIKE: - As an Amazon Associate I earn from qualifying purchases from Amazon. Just use this link: 🤍 #ApacheSpark #DataEngineering #PlumbersofDataScience #bigdata

Map Reduce einfach erklärt - Was ist die Idee von Apache Spark, Flink & Hadoop?

2213
58
7
00:06:45
17.06.2022

#DataScience Werkzeuge wie zum Beispiel Apache Hadoop, Spark oder Flink sind mächtig, basieren aber auf einer einfachen Grundidee: das Aufteilen großer Datenmengen und die parallele Verarbeitung dieser Teile. Das Video hat nicht den Anspruch, #MapReduce einschließlich Shuffle-Phase im Detail zu besprechen. Es soll vielmehr die generelle Idee vermittelt werden um in das Thema einzusteigen. Inhalt: 00:00 Einleitung 00:17 Vorbereitung 00:34 Übungen 01:18 Alte for-Schleife 02:03 Map 03:17 add-Funktion 03:43 Reduce 04:40 Parallele Verarbeitung 06:23 Fazit Big Data & Data Science Kurse bei predic8: #BigData & NoSQL mit Open Source Werkzeugen 🤍 Einführung in Apache Kafka für Entwickler und Administratoren 🤍 Apache Cassandra 🤍 Über Thomas Bayer Twitter: 🤍thomasub Xing: 🤍

86 Flink An Overview

24
1
0
00:06:54
27.07.2022

Hadoop In 5 Minutes | What Is Hadoop? | Introduction To Hadoop | Hadoop Explained |Simplilearn

1006823
27595
1450
00:06:21
21.01.2021

🔥Professional Certificate Program In Data Engineering: 🤍 Hadoop is a famous Big Data framework; this video on Hadoop will acquaint you with the term Big Data and help you understand the importance of Hadoop. Here, you will also learn about the three main components of Hadoop, namely, HDFS, MapReduce, and YARN. In the end, we will have a quiz on Hadoop. Hadoop is a framework that manages Big Data storage in a distributed way and processes it parallelly. Now, let's get started and learn all about Hadoop. Don't forget to take the quiz at 05:11! To learn more about Hadoop, subscribe to our YouTube channel: 🤍 Watch more videos on HadoopTraining: 🤍 #WhatIsHadoop #Hadoop #HadoopExplained #IntroductionToHadoop #HadoopTutorial #Simplilearn Big Data #SimplilearnHadoop #simplilearn 🔥Free Big Data Hadoop and Spark Developer course: 🤍 ➡️ Professional Certificate Program In Data Engineering This Data Engineering course is ideal for professionals, covering critical topics like the Hadoop framework, Data Processing using Spark, Data Pipelines with Kafka, Big Data on AWS, and Azure cloud infrastructures. This program is delivered via live sessions, industry projects, masterclasses, IBM hackathons, and Ask Me Anything sessions. ✅ Key Features - Professional Certificate Program Certificate and Alumni Association membership - Exclusive Master Classes and Ask me Anything sessions by IBM - 8X higher live interaction in live Data Engineering online classes by industry experts - Capstone from 3 domains and 14+ Projects with Industry datasets from YouTube, Glassdoor, Facebook etc. - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Skills Covered - Real Time Data Processing - Data Pipelining - Big Data Analytics - Data Visualization - Provisioning data storage services - Apache Hadoop - Ingesting Streaming and Batch Data - Transforming Data - Implementing Security Requirements - Data Protection - Encryption Techniques - Data Governance and Compliance Controls 👉Learn More at: 🤍 For more information about Simplilearn courses, visit: - Facebook: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 - Website: 🤍 Get the Android app: 🤍 Get the iOS app: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

Why Apache Flink is better than Spark by Rubén Casado

1533
13
0
00:46:18
12.12.2016

🤍 Abstract: 🤍 Slides: 🤍 Session presented at Big Data Spain 2016 Conference 18th Nov 2016 Kinépolis Madrid Event promoted by: 🤍

Flink - A Serious Alternative to Spark (DUGTalks)

383
5
0
01:09:17
29.10.2018

Learn about Apache Flink, an Apache Software Foundation open-source framework that's more mature and superior to Spark for distributed stream and batch processing.​ Flink Overview – 1:56 Flink Components and Programming Model – 12:30 Flink Batch Processing – 16:14 Flink Streaming – 26:33 Flink SQL – 51:15 Flink Fault Tolerance – 55:05 View big data courses: 🤍 *SLI no longer offers the NEXT product. For current on-demand offerings, please visit: 🤍

Introduction to Apache Flink and Flink SQL

1419
32
7
00:10:48
01.02.2023

Join Gunnar Morling for a ten minute introduction to Flink and FlinkSQL, as you see him build a Flink pipeline to process data from one Kafka topic to another Kafka topic. In this example, he'll be using RedPanda's Kafka API compatible offering to stream data into and from Flink.

Berlin Buzzwords 2018: Frank Conrad – Spark and Flink Running Scalable in Kubernetes #bbuzz

353
3
0
00:36:55
18.06.2018

The challenges to run Apache Spark and Flink at scale in a Kubernetes cluster. The needed multi-tenant environment at a larger scale provide additional challenges on top. The jobs profiles have big range in terms of of size and runtime. The talk will show same faced problems on Apache Flink, Spark and Kubernetes side, discussed alternatives and used solutions to that. Topics are around - deployment, tuning - runtime variance, congestion - shuffle - cluster resilience / update behaviour - monitoring, logs Read more: 🤍 About Frank Conrad: 🤍 Website: 🤍 Twitter: 🤍 LinkedIn: 🤍 Reddit: 🤍

Migrate Spark and Flink pipelines to the cloud

78
0
0
00:00:16
18.04.2022

Learn how to migrate your Spark and Flink data pipelines from on premises to the cloud with Apache Beam. Live lessons start on May 10. Enroll now at 🤍

Flink Forward 2015: Slim Baltagi – Flink and Spark Similarities and Differences

10709
39
0
00:51:30
12.11.2015

Flink Forward Conference on Apache Flink, October 12 & 13 at Kulturbrauerei Berlin

38 2021大数据面试宝典 Flink 与Spark对比

250
0
0
00:08:10
21.03.2021

海哥持续输出,为大家带来了最新版的大数据面试宝典。尚硅谷总结了2021年最新的大厂大数据面试题,涵盖Hadoop, Hive, Flume, Kafka,Flink,实际生产经验等等,掰开揉碎送给大家,适用于全球的大数据开发岗位。需要转行、面试、或者了解大厂大数据岗位要求的同学,一定不要错过! 【尚硅谷】2021大厂大数据高薪面试宝典:🤍 【尚硅谷】大数据项目之电商数仓3.0:🤍 【尚硅谷】大数据技术之Hadoop3.x:🤍 【尚硅谷】大数据技术之Hive(2021版):🤍 【尚硅谷】【大数据】2020版最新版Spark3.0教程:🤍 【尚硅谷】电商项目_大数据实时处理【SparkStreaming版】:🤍

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