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🔥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. 🔥Professional Certificate Program In Data Engineering: 🤍 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 ➡️ 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
This lecture is all about Setting up Apache Kafka and simulating a real-time data streaming on our HDP Sandbox where we have set up Kafka cluster and created a topic and read/write some raw data from command line kafka consumer and kafka producer applications. Below are the commands required for this lecture. Start Kafka service using Ambari cd /usr/hdp/current/kafka-broker/bin ./kafka-topics.sh create zookeeper sandbox-hdp.hortonworks.com:2181 replication-factor 1 partitions 1 topic test_topic ./kafka-topics.sh list zookeeper sandbox-hdp.hortonworks.com:2181 ./kafka-console-producer.sh broker-list sandbox-hdp.hortonworks.com:6667 topic test_topic On next console- cd /usr/hdp/current/kafka-broker/bin ./kafka-console-consumer.sh zookeeper localhost:2181 topic test_topic from-beginning - Remember for HDP 2.5 version: in create and producer script, use the below zookeeper argument: ./kafka-topics.sh create zookeeper sandbox.hdp.hortonworks.com:2181 for consumer, run the below command: ./kafka-console-consumer.sh bootstrap-server sandbox.hdp.hortonworks.com:6667 zookeeper localhost:2181 topic test_topic from-beginning In the previous lecture we have seen Introducing Apache Kafka in Hadoop Ecosystem where we have seen what is real-time data streaming platform and rea-time data analytics. This lecture covers introductory party which will teach you what is Kafka, Kafka architecture and how it works under the hood. - 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 which are linked below. 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
Learn the principles of Apache Kafka and how it works through easy examples and diagrams! If you want to learn more: 🤍 Get the Apache Kafka Series - Learn Apache Kafka for Beginners v3 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 - 🤍
This lecture is all about processing web logs using Apache Kafka where we have built a Kafka data streaming application which takes the web logs as a input and write it on a topic as well as sink file in real time. Below are the commands required for this lecture. cd /usr/hdp/current/kafka-broker/conf cp connect-standalone.properties ~/ cp connect-file-sink.properties ~/ cp connect-file-source.properties ~/ vi connect-standalone.properties bootstrap.servesrs=sandbox-hortonworks.com:6667 vi connect-file-sink.properties file=/home/maria_dev/logout.txt topic = log-test vi connect-file-source.properties file=/home/maria_dev/access_log.txt topic = log-test wget 🤍 new console: cd /usr/hdp/current/kafka-broker/bin ./kafka-console-consumer.sh zookeeper localhost:2181 topic log-test ./kafka-console-consumer.sh bootstrap-server sandbox-hortonworks.com:6667 topic log-test zookeeper localhost:2181 new console: cd /usr/hdp/current/kafka-broker/bin ./connect-standalone.sh ~/connect-standalone.properties ~/connect-file-source.properties ~/connect-file-sink.properties Remember for HDP 2.5 version: in create and producer script, use the below zookeeper argument: ./kafka-topics.sh create zookeeper sandbox.hdp.hortonworks.com:2181 for consumer, run the below command: ./kafka-console-consumer.sh bootstrap-server sandbox.hdp.hortonworks.com:6667 zookeeper localhost:2181 topic test_topic from-beginning In the previous lecture we have seen Setting up Apache Kafka and simulating a real-time data streaming on our HDP Sandbox where we have set up Kafka cluster and created a topic and read/write some raw data from command line kafka consumer and kafka producer applications. 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
Apache Kafka is a distributed event streaming platform used to handle large amounts of realtime data. Learn the basics of Kafka in this quickstart tutorial. #programming #datascience #100SecondsOfCode 💬 Chat with Me on Discord 🤍 🔗 Resources - Kafka Homepage 🤍 - Kafka Github 🤍 - RabbitMQ in 100 Seconds 🤍 🔥 Get More Content - Upgrade to PRO Upgrade at 🤍 Use code YT25 for 25% off PRO access 🎨 My Editor Settings - Atom One Dark - vscode-icons - Fira Code Font 🔖 Topics Covered - What is Apache Kafka? - Who created Apache Kafka? - What is Kafka used for? - How do large apps handle streaming data? - Apache Kafka basic examples
This course is for basic understanding of data ingestion with kafka, it delivers how we can get data from twitter, use flume and spark streaming with kafka. It also encapsulates the customisation on consumer and producer as well as consumer groups. For more information on this course, please visit 🤍
Let us discover the distributed streaming platform Apache Kafka together. Hi my name is Ian Hillman Table of Contents: 00:00 - Introduction 00:03 - What is Apache Kafka? 00:17 - Is Kafka written in Java or Scala? 00:21 - Is Kafka associated to Big DATA? 00:26 - What is a Kafka Brokers or node? 00:47 - How does Kafka use messaging? 00:58 - What is a topic in Kafka? 01:15 - How about the Zookeeper? 01:32 - Kafka and event storing? 01:35 - Is Kafka open-source? 01:44 - What is the aim of the Kafka project? 01:54 - Requirement to run Kafka? 02:06 - How is Zookeeper used? 02:27 - Why is Apache Kafka so popular? 02:58 - Is working with Kafka easy? 03:25 - What is Offset Explorer? 03:48 - IBM MQ vs Kafka? 04:28 - Prerequisites for Kafka training? 04:54 - Kafka being open source is it Free? 05:07 - What is Kafka in Simple terms? 05:14 - Which APIs to manage Kafka platform? 05:40 - What does Kafka offer? 05:56 - What are Bootstrap Servers? 06:14 - What is Kafka Broker? 06:37 - How are host port pairs used in Kafka? 06:51 - Kafka uses high volume of real time Data 07:01 - Who originally Built Kafka? 07:05 - Where is Kafka Used? 07:11 - How much does Kafka cost? 07:16 - Any new approaches to usage? 07:31 - What does the approach require? 07:42 - What is Hadoop Zookeeper? 08:02 - What is kafka used for? 08:13 - Closing Remarks 08:43 - Thank you.
► TRY THIS YOURSELF: 🤍 Thanks to the Apache Kafka community, you don't need to write the code yourself for widely used layers of application functionality. This infrastructure already exists in ksqlDB, Kafka Streams, Confluent Schema Registry, and Kafka Connect, to name a few. ► For a COMPLETE IMMERSIVE HANDS-ON EXPERIENCE, go to 🤍 - - - ABOUT CONFLUENT Confluent, founded by the creators of Apache Kafka®, enables organizations to harness the 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 🤍 #kafka #kafkastreams #streamprocessing #apachekafka #confluent
A quick introduction to how Apache Kafka works and differs from other messaging systems using an example application. In this video I explain partitioning, consumer offsets, replication and many other concepts found in Kafka. Please support me through my Udemy courses: Pass your coding interview in Java : 🤍 Python: 🤍 Ruby: 🤍 JavaScript: 🤍 Learn Dynamic Programming in, Java: 🤍 Python: 🤍 Ruby: 🤍 Multithreading in, Go Lang: 🤍 Python: 🤍 Java: 🤍 Blog: 🤍
#Kafka #Hadoop #Flume #ByCleverStudies By watching this video, you will learn about Apache Hadoop Vs Kafka Vs Flume. Flume and Spark Integration Part-1: 🤍 Flume and Spark Integration Part-2: 🤍 Follow me on LinkedIn 🤍 - Follow this link to join 'Clever Studies' official WhatsApp groups: 🤍 Community: 🤍 Follow this link to join 'Clever Studies' official telegram channel: 🤍 (Who choose Paid Membership option will get the following benefits) Watch premium YT videos in our channel Mock Interview and Feedback Gdrive access for Bigdata Materials (Complimentary) PySpark by Naresh playlist: 🤍 PySpark Software Installation: 🤍 Realtime Interview playlist : 🤍 Apache Spark playlist : 🤍 PySpark playlist: 🤍 Apache Hadoop playlist: 🤍 Bigdata playlist: 🤍 Scala Playlist: 🤍 SQL Playlist: 🤍 Hello Viewers, We ‘Clever Studies’ YouTube Channel formed by group of experienced software professionals to fill the gap in the industry by providing free content on software tutorials, mock interviews, study materials, interview tips, knowledge sharing by Real-time working professionals and many more to help the freshers, working professionals, software aspirants to get a job. If you like our videos, please do subscribe and share within your friends circle. Contact us : shareit2904🤍gmail.com Thank you !
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► TRY THIS YOURSELF: 🤍 Learn how partitioning works in Apache Kafka. With partitioning, the effort behind storing, processing, and messaging can be split among many nodes in the cluster. ► For a COMPLETE IMMERSIVE HANDS-ON EXPERIENCE, go to 🤍 - - - ABOUT CONFLUENT Confluent, founded by the creators of Apache Kafka®, enables organizations to harness the 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 🤍 #kafka #kafkastreams #streamprocessing #apachekafka #confluent
► TRY THIS YOURSELF: 🤍 Learn about Kafka Connect, also known as Kafka's integration API, and how it works to get data from non-Kafka systems into Kafka topics. ► For a COMPLETE IMMERSIVE HANDS-ON EXPERIENCE, go to 🤍 - - - ABOUT CONFLUENT Confluent, founded by the creators of Apache Kafka®, enables organizations to harness the 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 🤍 #kafka #kafkastreams #streamprocessing #apachekafka #confluent
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Hi Friends, Good morning/evening. Do you need a FREE Apache Spark and Hadoop VM for practice? You can sign up for free and get/download it directly from here: 🤍 Happy Learning! Access Apache Kafka in datamakingvm2: - jps Checking the status of the Zookeeper service: - sudo systemctl status zookeeper Checking the status of the Kafka service: - sudo systemctl status kafka Command to start the Zookeeper service: - sudo systemctl start zookeeper Command to start the Kafka service: - sudo systemctl start kafka Command to stop the Zookeeper service: sudo systemctl stop zookeeper Command to stop the Kafka service: sudo systemctl stop kafka netstat -an | grep 2181 netstat -an | grep 9092 Command to create Kafka topic: kafka-topics.sh create topic order-events bootstrap-server localhost:9092 Describe kafka topic: - kafka-topics.sh describe topic order-events bootstrap-server localhost:9092 List the kafka topics: kafka-topics.sh list bootstrap-server localhost:9092 Commandline kafka producer: - kafka-console-producer.sh topic order-events bootstrap-server localhost:9092 Commandline kafka consumer: - kafka-console-consumer.sh topic order-events from-beginning bootstrap-server localhost:9092 Reach us through our blog website for FREE VM: 🤍 Please donate us if you like our work, which will help us a lot. Link to donate: 🤍 I have published new course called "Real Time Spark Project for Beginners: Hadoop, Spark, Docker" Course link: 🤍 Create First PySpark App on Apache Spark 2.4.4 using PyCharm | PySpark 101 |Part 1| DM | DataMaking - 🤍 End to End Project using Spark/Hadoop | Code Walkthrough | Architecture | Part 1 | DM | DataMaking - 🤍 Spark Structured Streaming with Kafka using PySpark | Use Case 2 |Hands-On|Data Making|DM|DataMaking - 🤍 Running First PySpark Application in PyCharm IDE with Apache Spark 2.3.0 | DM | DataMaking - 🤍 Access Facebook API using Python in English | Hands-On | Part 3 | DM | DataMaking - 🤍 Real-Time Spark Project |Real-Time Data Analysis|Architecture|Part 1| DM | DataMaking | Data Making - 🤍 Web Scraping using Python and Selenium | Scrape Facebook | Part 5 | Data Making | DM | DataMaking - 🤍 End to End Project using Spark/Hadoop | Code Walkthrough | Kafka Producer | Part 2 | DM | DataMaking - 🤍 Apache Zeppelin | Step-by-Step Installation Guide | Python | Notebook |DM| DataMaking | Data Making - 🤍 Create First RDD(Resilient Distributed Dataset) in PySpark | PySpark 101 | Part 2 | DM | DataMaking - 🤍 Join this channel to get access to perks: 🤍
For virtual instructor-led Kafka Official Class, please reach out to us at operations🤍datacouch.io We are an official training delivery partner of Confluent.. We conduct corporate trainings on various topics including Confluent Kafka Developer, Confluent Kafka Administration, Confluent Kafka Real Time Streaming using KSQL & KStreams and Confluent Kafka Advanced Optimization. Our instructors are well qualified and vetted by Confluent for delivering such courses. This tutorial video will help you to understand the Kafka architecture in depth. As a part of this video we are covering Kafka Architecture, brokers, topics, Kafka partitions, producers and consumers. Enjoy Learning! Let’s come together in Joining our strong 3700+ 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: 🤍 Our company site - 🤍 Find our eLearning Courses here - 🤍 Comment, Like, Share and Subscribe to our YouTube Channel! #Kafka #KafkaArchitecture #KafkaArchitectureDesign #Confluent #Partition #Topics #Producer #Consumer #KafkaSimplified #KafkaArchitecturePatterns #KafkaArchitectureDataFlow #UnderstandingKafkaArchitecture #DataCouch
🤍 | In this video we’ll lay the foundation for Apache Kafka®, starting with its architecture; ZooKeeper’s role; topics, partitions, and segments; the commit log and streams; brokers and broker replication; producers basics; and consumers, consumer groups, and offsets. After you’ve watched the video, you can take a quick quiz to check what you’ve learned and get immediate feedback here: 🤍 As always you can visit us here: 🤍 LEARN MORE ► Apache Kafka 101 course: 🤍 ► Learn about Apache Kafka on Confluent Developer: 🤍 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 🤍 #apachekafka #kafka #confluent
Explanation of how one can integrate Apache Kafka with Apache Flume
Speaker: Neha Narkhede from Confluent Big Data Applications Meetup, 06/23/2015 Palo Alto, CA More info here: 🤍 Link to slides: 🤍 About this talk: Kafka’s unique architecture allows it to be used for real time processing as well as a bus for feeding batch systems like Hadoop. Kafka is fundamentally changing the way data flows through an organization and presents new opportunities for processing data in real time that were not possible before. The biggest change this had led to is a shift in the way data is integrated across a variety of data sources and systems. In this talk, Neha Kharkhede from Confluent, will discuss how companies are using Apache Kafka and where it fits in the Big Data ecosystem.
After completing the Apache Kafka training, you will be able to build applications using Apache Kafka. You will be able to make educated decisions for building good design. This series we will learn the following: 1) Introduction to Kafka 2) What is a producer 3) What is a consumer 4) what are Topic and Partition 5) what is a consumer group 6) What is Topic replication and how to scale Kafka 7) How to Install apache Kafka 8) How to build applications using Kafka - - - - - - - - - - - - - - Who should go for this Course? This course is a must for anyone who aspires to embark into the field of big data and keep abreast of the latest developments around fast and efficient processing of ever-growing data using Spark and related projects. The course is ideal for: 1. Big Data enthusiasts 2. Software Architects, Engineers, and Developers - - - - - - - - - - - - - - Facebook: 🤍 Data Savvy: 🤍 Github: 🤍 LinkedIn: 🤍 #kafka #apachekafka #bigdata
This video is all about installing the Apache Kafka on your Windows PC. This video includes step-by-step guide to install Kafka including recommended configurations. Prerequisite: You should have Java (JDK) installed on your windows machine. Apache Kafka official website: 🤍 Required Commands: .\bin\windows\zookeeper-server-start.bat .\config\zookeeper.properties .\bin\windows\kafka-server-start.bat .\config\server.properties kafka-topics.bat create bootstrap-server localhost:9092 replication-factor 1 partition 1 topic test kafka-console-producer.bat broker-list localhost:9092 topic test - Sample Data: {"Name: "John", "Age":"31", "Gender":"Male"} {"Name: "Emma", "Age":"27", "Gender":"Female"} {"Name: "Ronald", "Age":"17", "Gender":"Male"} - kafka-console-consumer.bat topic test bootstrap-server localhost:9092 from-beginning .\bin\windows\zookeeper-server-stop.bat .\config\zookeeper.properties .\bin\windows\kafka-server-stop.bat .\config\server.properties Want to know more about Big Data? then checkout the full course dedicated to Big Data fundamentals: 🤍 - 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 which are linked below. 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 #technology #dataanalytics #datascientist #kafka #apachekafka #ampcode
Development Environment as Virtual Machine(VM): You can get it from the below Url 🤍 YouTube Playlist Link: Development Environment for Aspiring Data Engineers | DataMaking Software | DMS - 🤍 Data Engineering Case Study/POC Projects: 1. Real-Time Apache Spark Project | Real-Time Data Analysis | End to End - 🤍 2. Apache Spark Project | Meetup RSVP Stream Processing | Real-World Project - 🤍 3. Apache Spark, Hadoop Project with Kafka and Python, End to End Development | Code Walk-through - 🤍 PySpark Tutorial 1. PySpark 101 Tutorial - 🤍 2. PySpark Structured Streaming for Beginners | PySpark Tutorial | Spark Streaming | Hands-On Guide - 🤍
As Hortonworks is acquired by Cloudera, we have decided to upgrade our cluster using Open Source Stack. In place of Ambari, we are providing access to Grafana to review the cluster. You can sign up to our labs and purchase a product by going to 🤍 For quick itversity updates, subscribe to our newsletter or follow us on social platforms. * Newsletter: 🤍 * LinkedIn: 🤍 * Facebook: 🤍 * Twitter: 🤍 * Instagram: 🤍 * YouTube: 🤍 #BigData #Labs #DataEngineering #Spark #Hadoop #Kafka Join this channel to get access to perks: 🤍
🤍 | Apache Kafka® 3.4 is released! In this special episode, Danica Fine (Senior Developer Advocate, Confluent), shares highlights of the Apache Kafka 3.4 release. This release introduces new KIPs in Kafka Core, Kafka Streams, and Kafka Connect. In Kafka Core: – KIP-792 expands the metadata each group member passes to the group leader in its JoinGroup subscription to include the highest stable generation that consumer was a part of. – KIP-830 includes a new configuration setting that allows you to disable the JMX reporter for environments where it’s not being used. – KIP-854 introduces changes to clean up producer IDs more efficiently, to avoid excess memory usage. It introduces a new timeout parameter that affects the expiry of producer IDs and updates the old parameter to only affect the expiry of transaction IDs. – KIP-866 (early access) provides a bridge to migrate between existing Zookeeper clusters to new KRaft mode clusters, enabling the migration of existing metadata from Zookeeper to KRaft. – KIP-876 adds a new property that defines the maximum amount of time that the server will wait to generate a snapshot; the default is 1 hour. – KIP-881, an extension of KIP-392, makes it so that consumers can now be rack-aware when it comes to partition assignments and consumer rebalancing. In Kafka Streams: – KIP-770 updates some Kafka Streams configs and metrics related to the record cache size. – KIP-837 allows users to multicast result records to every partition of downstream sink topics and adds functionality for users to choose to drop result records without sending. And finally, for Kafka Connect: – KIP-787 allows users to run MirrorMaker2 with custom implementations for the Kafka resource manager and makes it easier to integrate with your ecosystem. Tune in to learn more about the Apache Kafka 34 release! EPISODE LINKS ► See release notes for Apache Kafka 3.4: 🤍 ► Read the blog to learn more: 🤍 ► Download Apache Kafka 3.4: 🤍 ► Get started with Apache Kafka 3.4: 🤍 ► Listen to the audio version: 🤍 TIMESTAMPS 0:00 - Intro 0:30 - KIP-866: ZooKeeper to KRaft cluster upgrade 1:06 - KIP-830: Allow disabling JMX Reporter 1:41 - KIP-881: Rack-aware Partition Assignment for Kafka Consumers 2:21 - KIP-876: Time based Cluster Metadata Snapshots 2:54 - KIP-854: Separate configuration for producer ID expiry 3:54 - KIP-837: Allow MultiCasting a Result Record 4:37 - KIP-787: MM2 manage Kafka resources with custom Admin implementations 5:14 - 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. #apachekafka #kafka #confluent
Apache Kafka is a simple, high-performance, distributed, fault-tolerant messaging system. It was initially developed at LinkedIn and is now used at many companies, including Twitter, Square, Mozilla, Foursquare, and Tumblr. This talk will cover the architecture of Kafka and how LinkedIn uses Kafka to build a distributed low-latency pipeline that handles all messaging, tracking, logging, and metrics data. This unified pipeline provides data feeds into Hadoop and a diverse set of user-facing real-time stream processing applications. We will describe the lessons learned scaling this service to thousands of data feeds and many terabytes of messages per day.
Abstract: To make critical business decisions in real time, many businesses today rely on a variety of data, which arrives in large volumes. Variety and volume together make big data applications complex operations. Big data applications require businesses to combine transactional data with structured, semi-structured, and unstructured data for deep and holistic insights. And, time is of the essence: to derive the most valuable insights and drive key decisions, large amounts of data have to be continuously ingested into Hadoop data lakes as well as other destinations. As a result, data ingestion poses the first challenge for businesses, which must be overcome before embarking on data analysis. With its various Application Templates for ingestion, DataTorrent allows users to: Ingest vast amounts of data with enterprise-grade operability and performance guarantees provided by its underlying Apache Apex framework. Those guarantees include fault tolerance, linear scalability, high throughput, low latency, and end-to-end exactly-once processing. Quickly launch template applications to ingest raw data, while also providing an easy and iterative way to add business logic and such processing logic as parse, dedupe, filter, transform, enrich, and more to ingestion pipelines. Visualize various metrics on throughput, latency and app data in real-time throughout execution. This talk will cover demo on streaming ETL application with App Templates. The streaming application would extract data from Kafka with Kafka Operator, transform with help of Transform operator and filter them and load to HDFS. Presenters: Mohit Jotwani is a Product Manager with DataTorrent with more than 10 years of experience and expertise in Big Data Solutions Deepak Narkhede is a Software Engineer at DataTorrent. He has worked on Storage systems in the past.
( Big Data with Hadoop & Spark Training: 🤍 ) This CloudxLab Introduction to Apache Kafka & Spark DataFrames tutorial helps you to understand Apache Kafka & Spark DataFrames in detail. Below are the topics covered in this tutorial: 1) Introduction to Apache Kafka 2) Apache Kafka Hands-on on CloudxLab 3) Integrating Spark Streaming & Kafka 4) Spark Streaming & Kafka Hands-on 5) Spark Streaming - updateStateByKey 6) Spark Streaming - Transform, Window & Join Operations 7) Output Operations on DStreams 8) Introduction to Spark DataFrames 9) Getting Started with DataFrames on CloudxLab Subscribe to our channel to get video updates. Hit the subscribe button above. Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Check our complete Hadoop & Spark playlist here: 🤍 - - - - - - - - - - - - - - How does it work? 1. This is a 60+ hour online instructor-led course 2. With the course, you get access to real-time distributed production cluster so that you can learn by doing hands-on 3. Cluster comes with all the tools preinstalled so that you can focus on learning than wasting time in setting up the cluster 4. Each topic consists of videos, assessments, questions and case studies to make sure you master the topic 5. The course is compatible with CCA175, HDP Certified Developer, HDP Certified Developer: Spark certifications 6. We have a 24×7 support and forum access to answer all your queries throughout your learning journey 7. At the end of the training, you will work on real-life projects on which we will provide you a grade and a verifiable certificate! 8. Optionally, Subscribe to 1:1 mentoring sessions and get guidance from industry leaders and professional - - - - - - - - - - - - - - About the Course CloudxLab's Big Data with Hadoop & Spark online training is designed to help you become a top Big Data developer. You will learn Hadoop and Spark to drive better business decisions and solve real-world problems. During this course, our expert will help you in 1. Introduction to Big Data 2. ZooKeeper 3. HDFS 4. YARN 5. MapReduce Basics 6. Write your own MapReduce programs 7. Analyzing and processing data with Pig and Hive 8. Schedule jobs using Oozie 9. NoSQL and HBase 10. Importing data with Sqoop and Flume 11. Implement best practices for Hadoop development 12. Introduction to Apache Spark 13. Introduction to Scala 14. Spark RDD and RDD operations 15. Writing and deploying Spark applications 16. Common patterns in Spark data processing 17. Data Formats - JSON, XML, AVRO, SequenceFile, Parquet, Protocol Buffers, RCFile 18. DataFrames and SparkSQL 19. Machine Learning with Spark using Spark MLlib 20. Introduction to GraphX 21. Work on real-life projects - - - - - - - - - - - - - - Who should go for this course? This course is for anyone who wants to become expert in Big Data with Hadoop and Spark and progress in the career. Ideally, this course will help professionals in the following groups 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building a successful career in Big Data - - - - - - - - - - - - - - Why Learn Hadoop and Spark? As humans, we are immersed in data in our every-day lives. As per IBM, the data doubles every two years on this planet. The value that data holds can only be understood when we can start to identify patterns and trends in the data. Normal computing principles do not work when data becomes huge. There is massive growth in the big data space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space. In this specialization, you will learn Hadoop and Spark to drive better business decisions and solve real-world problems Please write back to us at reachus🤍cloudxlab.com or call us at +1 (412) 568-3901 (US) or 080 - 4920 2224 (IN) for more information. Customer Review: Jose Manuel Ramirez Leon, Data Engineer, PwC - I signed up for CloudxLab's Big Data with Hadoop and Spark course. I went through all of the material, which includes very well narrated videos and very clear slides. I finally had to tackle a project in spark were I had to work hard to complete it and finally got my certificate. As I already said, they were very attentive and knowledgeable in their support throughout the whole course. I am going to use the site to prepare for the CCA175 exam now. I thoroughly recommend CloudxLab for anyone who needs to learn Spark, Hadoop, and the whole Big Data ecosystem
( Big Data with Hadoop & Spark Training: 🤍 ) This CloudxLab Introduction to Spark Streaming & Apache Kafka tutorial helps you to understand Spark Streaming and Kafka in detail. Below are the topics covered in this tutorial: 1) Spark Streaming - Workflow 2) Use Cases - Ecommerce, Real-time Sentiment Analysis & Real-time Fraud Detection 3) Spark Streaming - DStream 4) Word Count Hands-on using Spark Streaming 5) Spark Streaming - Running Locally Vs Running on Cluster 6) Introduction to Apache Kafka 7) Apache Kafka Hands-on on CloudxLab 8) Integrating Spark Streaming & Kafka 9) Spark Streaming & Kafka Hands-on Subscribe to our channel to get video updates. Hit the subscribe button above. Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Check our complete Hadoop & Spark playlist here: 🤍 - - - - - - - - - - - - - - How does it work? 1. This is a 60+ hour online instructor-led course 2. With the course, you get access to real-time distributed production cluster so that you can learn by doing hands-on 3. Cluster comes with all the tools preinstalled so that you can focus on learning than wasting time in setting up the cluster 4. Each topic consists of videos, assessments, questions and case studies to make sure you master the topic 5. The course is compatible with CCA175, HDP Certified Developer, HDP Certified Developer: Spark certifications 6. We have a 24×7 support and forum access to answer all your queries throughout your learning journey 7. At the end of the training, you will work on real-life projects on which we will provide you a grade and a verifiable certificate! 8. Optionally, Subscribe to 1:1 mentoring sessions and get guidance from industry leaders and professional - - - - - - - - - - - - - - About the Course CloudxLab's Big Data with Hadoop & Spark online training is designed to help you become a top Big Data developer. You will learn Hadoop and Spark to drive better business decisions and solve real-world problems. During this course, our expert will help you in 1. Introduction to Big Data 2. ZooKeeper 3. HDFS 4. YARN 5. MapReduce Basics 6. Write your own MapReduce programs 7. Analyzing and processing data with Pig and Hive 8. Schedule jobs using Oozie 9. NoSQL and HBase 10. Importing data with Sqoop and Flume 11. Implement best practices for Hadoop development 12. Introduction to Apache Spark 13. Introduction to Scala 14. Spark RDD and RDD operations 15. Writing and deploying Spark applications 16. Common patterns in Spark data processing 17. Data Formats - JSON, XML, AVRO, SequenceFile, Parquet, Protocol Buffers, RCFile 18. DataFrames and SparkSQL 19. Machine Learning with Spark using Spark MLlib 20. Introduction to GraphX 21. Work on real-life projects - - - - - - - - - - - - - - Who should go for this course? This course is for anyone who wants to become expert in Big Data with Hadoop and Spark and progress in the career. Ideally, this course will help professionals in the following groups 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building a successful career in Big Data - - - - - - - - - - - - - - Why Learn Hadoop and Spark? As humans, we are immersed in data in our every-day lives. As per IBM, the data doubles every two years on this planet. The value that data holds can only be understood when we can start to identify patterns and trends in the data. Normal computing principles do not work when data becomes huge. There is massive growth in the big data space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space. In this specialization, you will learn Hadoop and Spark to drive better business decisions and solve real-world problems Please write back to us at reachus🤍cloudxlab.com or call us at +1 (412) 568-3901 (US) or 080 - 4920 2224 (IN) for more information. Customer Review: Jose Manuel Ramirez Leon, Data Engineer, PwC - I signed up for CloudxLab's Big Data with Hadoop and Spark course. I went through all of the material, which includes very well narrated videos and very clear slides. I finally had to tackle a project in spark were I had to work hard to complete it and finally got my certificate. As I already said, they were very attentive and knowledgeable in their support throughout the whole course. I am going to use the site to prepare for the CCA175 exam now. I thoroughly recommend CloudxLab for anyone who needs to learn Spark, Hadoop, and the whole Big Data ecosystem
🤍 | Imagine if you could create a better world for future generations simply by delivering marine ingenuity. Van Oord is a Dutch family-owned company that has served as an international marine contractor for over 150 years, focusing on dredging, land infrastructure in the Netherlands, and offshore wind and oil & gas infrastructure. Real-time insights into costs spent, the progress of projects, and the performance tracking of vessels and equipment are essential for surviving as a business. Becoming a data-driven company requires that all data connected, synchronized, and visualized—in fact, truly digitized. This requires a central nervous system that supports: ► Legacy (monolith environment) as well as microservices ► ELT/ETL/streaming ETL ► All types of data, including transactional, streaming, geo, machine, and (sea) survey/bathymetry ► Master data/enterprise common data model The need for agility and speed makes it necessary to have a fully integrated DevOps-infrastructure-as-code environment, where data lineage, data governance, and enterprise architecture are holistically embedded. Thousands of topics need to be developed, updated, tested, accepted, and deployed each day. This together with different scripts for connectors requires a holistic data management solution, where data lineage, data governance and enterprise architecture are an integrated part. Thus, Marlon Hiralal (Enterprise/Data Management Architect, Van Oord) and Andreas Wombacher (Data Engineer, Van Oord) turned to Confluent for a three-month proof of concept and explored the pre-prep stage of using Apache Kafka® on Van Oord’s vessels. Since the environment in Van Oord is dynamic with regards to the application landscape and offered services, it is essential that a stable environment with controlled continuous integration and deployment is applied. Beyond the software components itself, this also applies to configurations and infrastructure, as well as applying the concept of CI/CD with infrastructure as code. The result: using Terraform and Confluent together. Publishing information is treated as a product at Van Oord. An information product is a set of Kafka topics: topics to communicate change and topics for sharing the state of a data source (Kafka tables). The set of all information products forms the enterprise data model. Apache Atlas is used as a data dictionary and governance tool to capture the meaning of different information products. All changes in the data dictionary are available as an information product in Confluent, allowing for consumers of information products to subscribe to the information and be notified about changes. Van Oord’s enterprise architecture model must remain up to date and aligned with the current implementation. This is achieved by automatically inspecting and analyzing Confluent data flows. Fortunately, Confluent embeds homogeneously in this holistic reference architecture. The basis of the holistic reference architecture is a CDC layer and a persistent layer, which makes Confluent the core component of the Van Oord future-proof digital data management solution. EPISODE LINKS ► Confluent Community: 🤍 ► Confluent Developer: 🤍 ► Use 60PDCAST for $60 of free Confluent Cloud: 🤍 ► Promo code details: 🤍 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. Confluent 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 🤍 #apachekafka #kafka #digitaltransformation
Cambridge Technology has been helping enterprises implement Apache Kafka platform to change their culture from batch processing to real-time event based stream processing. In this webinar, you'll learn about how Apache Kafka works, how to Integrate Apache Kafka into your environment and more. Web: 🤍 #Apache #Kafka #Confluent #Cloud #AI
🔥 Professional Certificate Program In Data Engineering: 🤍 This Simplilearn Pig Tutorial will help you understand the concepts of Apache Pig in depth. Below are the topics covered in this Hadoop Pig Tutorial: 0:00 Introduction to Pig 1:10 What is Pig? 1:37 Pig example 1:59 Components of Pig 2:41 How Pig works? 3:24 Pig salient features 3:50 Data model 5:04 Nested data model 5:26 Pig execution modes 5:42 Pig interactive modes 6:04 Pig vs SQL 8:59 Pig script interpretation 🔥Free Big Data Hadoop Spark Developer Course: 🤍 Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - 🤍 Check our Big Data Training Video Playlist: 🤍 Big Data and Analytics Articles - 🤍 To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: 🤍 #PigTutorial #WhatIsPigInHadoop #ApachePigTutorial #Hadoop #PigScript #HadoopTutorialForBeginners #Simplilearn - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: 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. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - Get the android app: 🤍 Get the iOS app: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
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