What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial | Simplilearn
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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
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What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial | Simplilearn
- 2. What’s in it for you? 1. History of Spark What’s in it for you?
- 3. What’s in it for you? 1. History of Spark 2. What is Spark? What’s in it for you?
- 4. What’s in it for you? 1. History of Spark 2. What is Spark? 3. Hadoop vs Spark What’s in it for you?
- 5. What’s in it for you? 1. History of Spark 2. What is Spark? 3. Hadoop vs Spark 4. Components of Apache Spark What’s in it for you? Spark Core Spark SQL Spark Streaming Spark MLlib GraphX
- 6. What’s in it for you? 1. History of Spark 2. What is Spark? 3. Hadoop vs Spark 4. Components of Apache Spark 5. Spark Architecture What’s in it for you?
- 7. What’s in it for you? 1. History of Spark 2. What is Spark? 3. Hadoop vs Spark 4. Components of Apache Spark 5. Spark Architecture 6. Applications of Spark What’s in it for you?
- 8. What’s in it for you? 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 Use Case What’s in it for you?
- 9. History of Apache Spark Started as a project at UC Berkley AMPLab 2009
- 10. History of Apache Spark Started as a project at UC Berkley AMPLab Open sourced under a BSD license 2009 2010
- 11. History of Apache Spark Started as a project at UC Berkley AMPLab Open sourced under a BSD license Spark became an Apache top level project 2009 2010 2013
- 12. History of Apache Spark Started as a project at UC Berkley AMPLab Open sourced under a BSD license Spark became an Apache top level project Used by Databricks to sort large-scale datasets and set a new world record 2009 2010 2013 2014
- 13. History of Apache Spark What is Apache Spark?
- 14. What is Apache Spark? Apache Spark is an open-source data processing engine to store and process data in real-time across various clusters of computers using simple programming constructs
- 15. What is Apache Spark? Support various programming languages Apache Spark is an open-source data processing engine to store and process data in real-time across various clusters of computers using simple programming constructs
- 16. What is Apache Spark? Support various programming languages Developers and data scientists incorporate Spark into their applications to rapidly query, analyze, and transform data at scale Query Analyze Transform Apache Spark is an open-source data processing engine to store and process data in real-time across various clusters of computers using simple programming constructs
- 17. History of Apache Spark Hadoop vs Spark
- 18. Hadoop vs Spark Processing data using MapReduce in Hadoop is slow Spark processes data 100 times faster than MapReduce as it is done in- memory
- 19. Hadoop vs Spark Processing data using MapReduce in Hadoop is slow Spark processes data 100 times faster than MapReduce as it is done in- memory Performs batch processing of data Performs both batch processing and real-time processing of data
- 20. Hadoop vs Spark Processing data using MapReduce in Hadoop is slow Spark processes data 100 times faster than MapReduce as it is done in- memory Performs batch processing of data Performs both batch processing and real-time processing of data Hadoop has more lines of code. Since it is written in Java, it takes more time to execute Spark has fewer lines of code as it is implemented in Scala
- 21. Hadoop vs Spark Processing data using MapReduce in Hadoop is slow Spark processes data 100 times faster than MapReduce as it is done in- memory Performs batch processing of data Performs both batch processing and real-time processing of data Hadoop has more lines of code. Since it is written in Java, it takes more time to execute Spark has fewer lines of code as it is implemented in Scala Hadoop supports Kerberos authentication, which is difficult to manage Spark supports authentication via a shared secret. It can also run on YARN leveraging the capability of Kerberos
- 22. History of Apache Spark Spark Features
- 23. Spark Features Fast processing Spark contains Resilient Distributed Datasets (RDD) which saves time taken in reading, and writing operations and hence, it runs almost ten to hundred times faster than Hadoop
- 24. Spark Features In-memory computing In Spark, data is stored in the RAM, so it can access the data quickly and accelerate the speed of analytics Fast processing
- 25. Spark Features Flexible Spark supports multiple languages and allows the developers to write applications in Java, Scala, R, or Python In-memory computingFast processing
- 26. Spark Features Fault tolerance Spark contains Resilient Distributed Datasets (RDD) that are designed to handle the failure of any worker node in the cluster. Thus, it ensures that the loss of data reduces to zero Flexible In-memory computingFast processing
- 27. Spark Features Better analytics Spark has a rich set of SQL queries, machine learning algorithms, complex analytics, etc. With all these functionalities, analytics can be performed better Fault toleranceFlexible In-memory computingFast processing
- 28. History of Apache Spark Components of Spark
- 29. Components of Apache Spark Spark Core
- 30. Components of Apache Spark Spark Core Spark SQL SQL
- 31. Components of Apache Spark Spark Streaming Spark Core Spark SQL SQL Streaming
- 32. Components of Apache Spark MLlib Spark Streaming Spark Core Spark SQL SQL Streaming MLlib
- 33. Components of Apache Spark MLlib Spark Streaming Spark Core Spark SQL GraphX SQL Streaming MLlib
- 34. History of Apache Spark Components of Spark – Spark Core
- 35. Spark Core Spark Core Spark Core is the base engine for large-scale parallel and distributed data processing
- 36. Spark Core Spark Core Spark Core is the base engine for large-scale parallel and distributed data processing It is responsible for: memory management fault recovery scheduling, distributing and monitoring jobs on a cluster interacting with storage systems
- 37. Resilient Distributed Dataset Spark Core Spark Core is embedded with RDDs (Resilient Distributed Datasets), an immutable fault-tolerant, distributed collection of objects that can be operated on in parallel RDD Transformation Action These are operations (such as reduce, first, count) that return a value after running a computation on an RDD These are operations (such as map, filter, join, union) that are performed on an RDD that yields a new RDD containing the result
- 38. History of Apache Spark Components of Spark – Spark SQL
- 39. Spark SQL Spark SQL framework component is used for structured and semi-structured data processing Spark SQL SQL
- 40. Spark SQL Spark SQL framework component is used for structured and semi-structured data processing Spark SQL SQL DataFrame DSL Spark SQL and HQL DataFrame API Data Source API CSV JSON JDBC Spark SQL Architecture
- 41. History of Apache Spark Components of Spark – Spark Streaming
- 42. Spark Streaming Spark Streaming is a lightweight API that allows developers to perform batch processing and real-time streaming of data with ease Spark Streaming Streaming Provides secure, reliable, and fast processing of live data streams
- 43. Spark Streaming Spark Streaming is a lightweight API that allows developers to perform batch processing and real-time streaming of data with ease Spark Streaming Streaming Provides secure, reliable, and fast processing of live data streams Streaming Engine Input data stream Batches of input data Batches of processed data
- 44. History of Apache Spark Components of Spark – Spark MLlib
- 45. Spark MLlib MLlib is a low-level machine learning library that is simple to use, is scalable, and compatible with various programming languages MLlib MLlib MLlib eases the deployment and development of scalable machine learning algorithms
- 46. Spark MLlib MLlib is a low-level machine learning library that is simple to use, is scalable, and compatible with various programming languages MLlib MLlib MLlib eases the deployment and development of scalable machine learning algorithms It contains machine learning libraries that have an implementation of various machine learning algorithms Clustering Classification Collaborative Filtering
- 47. History of Apache Spark Components of Spark – GraphX
- 48. GraphX GraphX is Spark’s own Graph Computation Engine and data store GraphX
- 49. GraphX GraphX is Spark’s own Graph Computation Engine and data store GraphX Provides a uniform tool for ETL Exploratory data analysis Interactive graph computations
- 50. History of Apache Spark Spark Architecture
- 51. Master Node Driver Program SparkContext • Master Node has a Driver Program • The Spark code behaves as a driver program and creates a SparkContext, which is a gateway to all the Spark functionalities Apache Spark uses a master-slave architecture that consists of a driver, that runs on a master node, and multiple executors which run across the worker nodes in the cluster Spark Architecture
- 52. Master Node Driver Program SparkContext Cluster Manager • Spark applications run as independent sets of processes on a cluster • The driver program & Spark context takes care of the job execution within the cluster Spark Architecture
- 53. Master Node Driver Program SparkContext Cluster Manager Cache Task Task Executor Worker Node Cache Task Task Executor Worker Node • A job is split into multiple tasks that are distributed over the worker node • When an RDD is created in Spark context, it can be distributed across various nodes • Worker nodes are slaves that run different tasks Spark Architecture
- 54. Master Node Driver Program SparkContext Cluster Manager Cache Task Task Executor Worker Node Cache Task Task Executor Worker Node • The Executor is responsible for the execution of these tasks • Worker nodes execute the tasks assigned by the Cluster Manager and return the results back to the SparkContext Spark Architecture
- 55. Spark Cluster Managers Standalone mode 1 By default, applications submitted to the standalone mode cluster will run in FIFO order, and each application will try to use all available nodes
- 56. Spark Cluster Managers Standalone mode 1 2 By default, applications submitted to the standalone mode cluster will run in FIFO order, and each application will try to use all available nodes Apache Mesos is an open-source project to manage computer clusters, and can also run Hadoop applications
- 57. Spark Cluster Managers Standalone mode 1 2 3 By default, applications submitted to the standalone mode cluster will run in FIFO order, and each application will try to use all available nodes Apache Mesos is an open-source project to manage computer clusters, and can also run Hadoop applications Apache YARN is the cluster resource manager of Hadoop 2. Spark can be run on YARN
- 58. Spark Cluster Managers Standalone mode 1 2 3 4 By default, applications submitted to the standalone mode cluster will run in FIFO order, and each application will try to use all available nodes Apache Mesos is an open-source project to manage computer clusters, and can also run Hadoop applications Apache YARN is the cluster resource manager of Hadoop 2. Spark can be run on YARN Kubernetes is an open- source system for automating deployment, scaling, and management of containerized applications
- 59. History of Apache Spark Applications of Spark
- 60. Applications of Spark Banking JPMorgan uses Spark to detect fraudulent transactions, analyze the business spends of an individual to suggest offers, and identify patterns to decide how much to invest and where to invest
- 61. Applications of Spark Banking E-Commerce JPMorgan uses Spark to detect fraudulent transactions, analyze the business spends of an individual to suggest offers, and identify patterns to decide how much to invest and where to invest Alibaba uses Spark to analyze large sets of data such as real-time transaction details, browsing history, etc. in the form of Spark jobs and provides recommendations to its users
- 62. Applications of Spark Banking E-Commerce Healthcare JPMorgan uses Spark to detect fraudulent transactions, analyze the business spends of an individual to suggest offers, and identify patterns to decide how much to invest and where to invest Alibaba uses Spark to analyze large sets of data such as real-time transaction details, browsing history, etc. in the form of Spark jobs and provides recommendations to its users IQVIA is a leading healthcare company that uses Spark to analyze patient’s data, identify possible health issues, and diagnose it based on their medical history
- 63. Applications of Spark Banking E-Commerce Healthcare Entertainment JPMorgan uses Spark to detect fraudulent transactions, analyze the business spends of an individual to suggest offers, and identify patterns to decide how much to invest and where to invest Alibaba uses Spark to analyze large sets of data such as real-time transaction details, browsing history, etc. in the form of Spark jobs and provides recommendations to its users IQVIA is a leading healthcare company that uses Spark to analyze patient’s data, identify possible health issues, and diagnose it based on their medical history Entertainment and gaming companies like Netflix and Riot games use Apache Spark to showcase relevant advertisements to their users based on the videos that they watch, share, and like
- 64. History of Apache Spark Spark Use Case
- 65. Spark Use Case Conviva is one of the world’s leading video streaming companies
- 66. Spark Use Case Conviva is one of the world’s leading video streaming companies Video streaming is a challenge, especially with increasing demand for high-quality streaming experiences
- 67. Spark Use Case Conviva is one of the world’s leading video streaming companies Video streaming is a challenge, especially with increasing demand for high-quality streaming experiences Conviva collects data about video streaming quality to give their customers visibility into the end- user experience they are delivering
- 68. Spark Use Case Conviva is one of the world’s leading video streaming companies Using Apache Spark, Conviva delivers a better quality of service to its customers by removing the screen buffering and learning in detail about the network conditions in real-time
- 69. Spark Use Case Conviva is one of the world’s leading video streaming companies Using Apache Spark, Conviva delivers a better quality of service to its customers by removing the screen buffering and learning in detail about the network conditions in real-time This information is stored in the video player to manage live video traffic coming from 4 billion video feeds every month, to ensure maximum retention
- 70. Spark Use Case Conviva is one of the world’s leading video streaming companies Using Apache Spark, Conviva has created an auto diagnostics alert
- 71. Spark Use Case Conviva is one of the world’s leading video streaming companies Using Apache Spark, Conviva has created an auto diagnostics alert It automatically detects anomalies along the video streaming pipeline and diagnoses the root cause of the issue
- 72. Spark Use Case Conviva is one of the world’s leading video streaming companies Using Apache Spark, Conviva has created an auto diagnostics alert It automatically detects anomalies along the video streaming pipeline and diagnoses the root cause of the issue Reduces waiting time before the video starts
- 73. Spark Use Case Conviva is one of the world’s leading video streaming companies Using Apache Spark, Conviva has created an auto diagnostics alert It automatically detects anomalies along the video streaming pipeline and diagnoses the root cause of the issue Reduces waiting time before the video starts Avoids buffering and recovers the video from a technical error
- 74. Spark Use Case Conviva is one of the world’s leading video streaming companies Using Apache Spark, Conviva has created an auto diagnostics alert It automatically detects anomalies along the video streaming pipeline and diagnoses the root cause of the issue Reduces waiting time before the video starts Avoids buffering and recovers the video from a technical error Goal is to maximize the viewer engagement