This is an introductory tutorial to Apache Spark at the Lagos Scala Meetup II. We discussed the basics of processing engine, Spark, how it relates to Hadoop MapReduce. Little handson at the end of the session.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Spark provides tools for distributed processing of large datasets across clusters. It includes APIs for distributed datasets called RDDs (Resilient Distributed Datasets) and transformations and actions that can be performed on those datasets in parallel. Key features of Spark include the Spark Shell for interactive use, DataFrames for structured data processing, and Spark Streaming for real-time data analysis.
This document summarizes Viadeo's use of Apache Spark. It discusses how Spark is used to build models for job offer click prediction and member segmentation. Spark jobs process event log data from HDFS and HBase to cluster job titles, build relationship graphs, compute input variables for regression models, and evaluate segments. The models improve click-through rates and allow flexible, fast member targeting. Future work includes indexing segmentations and exposing them for analytics and online campaign building.
This document provides an overview of Apache Spark, including how it compares to Hadoop, the Spark ecosystem, Resilient Distributed Datasets (RDDs), transformations and actions on RDDs, the directed acyclic graph (DAG) scheduler, Spark Streaming, and the DataFrames API. Key points covered include Spark's faster performance versus Hadoop through its use of memory instead of disk, the RDD abstraction for distributed collections, common RDD operations, and Spark's capabilities for real-time streaming data processing and SQL queries on structured data.
This document outlines a project to capture user location data and send it to a database for real-time analysis using Kafka and Spark streaming. It describes starting Zookeeper and Kafka servers, creating Kafka topics, producing and consuming messages with Java producers and consumers, using the Spark CLI, integrating Kafka and Spark for streaming, creating DataFrames and SQL queries, and saving data to PostgreSQL tables for further processing and analysis. The goal is to demonstrate real-time data streaming and analytics on user location data.
An over-ambitious introduction to Spark programming, test and deployment. This slide tries to cover most core technologies and design patterns used in SpookyStuff, the fastest query engine for data collection/mashup from the deep web.
For more information please follow: https://github.com/tribbloid/spookystuff
A bug in PowerPoint used to cause transparent background color not being rendered properly. This has been fixed in a recent upload.
Apache Spark is a fast distributed data processing engine that runs in memory. It can be used with Java, Scala, Python and R. Spark uses resilient distributed datasets (RDDs) as its main data structure. RDDs are immutable and partitioned collections of elements that allow transformations like map and filter. Spark is 10-100x faster than Hadoop for iterative algorithms and can be used for tasks like ETL, machine learning, and streaming.
Productionizing Spark and the REST Job Server- Evan ChanSpark Summit
The document discusses productionizing Apache Spark and using the Spark REST Job Server. It provides an overview of Spark deployment options like YARN, Mesos, and Spark Standalone mode. It also covers Spark configuration topics like jars management, classpath configuration, and tuning garbage collection. The document then discusses running Spark applications in a cluster using tools like spark-submit and the Spark Job Server. It highlights features of the Spark Job Server like enabling low-latency Spark queries and sharing cached RDDs across jobs. Finally, it provides examples of using the Spark Job Server in production environments.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
Spark provides tools for distributed processing of large datasets across clusters. It includes APIs for distributed datasets called RDDs (Resilient Distributed Datasets) and transformations and actions that can be performed on those datasets in parallel. Key features of Spark include the Spark Shell for interactive use, DataFrames for structured data processing, and Spark Streaming for real-time data analysis.
This document summarizes Viadeo's use of Apache Spark. It discusses how Spark is used to build models for job offer click prediction and member segmentation. Spark jobs process event log data from HDFS and HBase to cluster job titles, build relationship graphs, compute input variables for regression models, and evaluate segments. The models improve click-through rates and allow flexible, fast member targeting. Future work includes indexing segmentations and exposing them for analytics and online campaign building.
This document provides an overview of Apache Spark, including how it compares to Hadoop, the Spark ecosystem, Resilient Distributed Datasets (RDDs), transformations and actions on RDDs, the directed acyclic graph (DAG) scheduler, Spark Streaming, and the DataFrames API. Key points covered include Spark's faster performance versus Hadoop through its use of memory instead of disk, the RDD abstraction for distributed collections, common RDD operations, and Spark's capabilities for real-time streaming data processing and SQL queries on structured data.
This document outlines a project to capture user location data and send it to a database for real-time analysis using Kafka and Spark streaming. It describes starting Zookeeper and Kafka servers, creating Kafka topics, producing and consuming messages with Java producers and consumers, using the Spark CLI, integrating Kafka and Spark for streaming, creating DataFrames and SQL queries, and saving data to PostgreSQL tables for further processing and analysis. The goal is to demonstrate real-time data streaming and analytics on user location data.
An over-ambitious introduction to Spark programming, test and deployment. This slide tries to cover most core technologies and design patterns used in SpookyStuff, the fastest query engine for data collection/mashup from the deep web.
For more information please follow: https://github.com/tribbloid/spookystuff
A bug in PowerPoint used to cause transparent background color not being rendered properly. This has been fixed in a recent upload.
Apache Spark is a fast distributed data processing engine that runs in memory. It can be used with Java, Scala, Python and R. Spark uses resilient distributed datasets (RDDs) as its main data structure. RDDs are immutable and partitioned collections of elements that allow transformations like map and filter. Spark is 10-100x faster than Hadoop for iterative algorithms and can be used for tasks like ETL, machine learning, and streaming.
Productionizing Spark and the REST Job Server- Evan ChanSpark Summit
The document discusses productionizing Apache Spark and using the Spark REST Job Server. It provides an overview of Spark deployment options like YARN, Mesos, and Spark Standalone mode. It also covers Spark configuration topics like jars management, classpath configuration, and tuning garbage collection. The document then discusses running Spark applications in a cluster using tools like spark-submit and the Spark Job Server. It highlights features of the Spark Job Server like enabling low-latency Spark queries and sharing cached RDDs across jobs. Finally, it provides examples of using the Spark Job Server in production environments.
Beneath RDD in Apache Spark by Jacek LaskowskiSpark Summit
This document provides an overview of SparkContext and Resilient Distributed Datasets (RDDs) in Apache Spark. It discusses how to create RDDs using SparkContext functions like parallelize(), range(), and textFile(). It also covers DataFrames and converting between RDDs and DataFrames. The document discusses partitions and the level of parallelism in Spark, as well as the execution environment involving DAGScheduler, TaskScheduler, and SchedulerBackend. It provides examples of RDD lineage and describes Spark clusters like Spark Standalone and the Spark web UI.
Spark supports four cluster managers: Local, Standalone, YARN, and Mesos. YARN is highly recommended for production use. When running Spark on YARN, careful tuning of configuration settings like the number of executors, executor memory and cores, and dynamic allocation is important to optimize performance and resource utilization. Configuring queues also allows separating different applications by priority and resource needs.
This document discusses Spark Job Server, an open source project that allows Spark jobs to be submitted and run via a REST API. It provides features like job monitoring, context sharing between jobs to reuse cached data, and asynchronous APIs. The document outlines motivations for the project, how to use it including submitting and monitoring jobs, and future plans like high availability and hot failover support.
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
Securing Spark Applications by Kostas Sakellis and Marcelo VanzinSpark Summit
This document discusses securing Spark applications. It covers encryption, authentication, and authorization. Encryption protects data in transit using SASL or SSL. Authentication uses Kerberos to identify users. Authorization controls data access using Apache Sentry and the Sentry HDFS plugin, which synchronizes HDFS permissions with higher-level abstractions like tables. A future RecordService aims to provide a unified authorization system at the record level for Spark SQL.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
This document provides an overview of Apache Spark, including its core concepts, transformations and actions, persistence, parallelism, and examples. Spark is introduced as a fast and general engine for large-scale data processing, with advantages like in-memory computing, fault tolerance, and rich APIs. Key concepts covered include its resilient distributed datasets (RDDs) and lazy evaluation approach. The document also discusses Spark SQL, streaming, and integration with other tools.
Spark is a general-purpose cluster computing framework that provides high-level APIs and is faster than Hadoop for iterative jobs and interactive queries. It leverages cached data in cluster memory across nodes for faster performance. Spark supports various higher-level tools including SQL, machine learning, graph processing, and streaming.
Spark is a distributed data processing framework that uses RDDs (Resilient Distributed Datasets) to represent data distributed across a cluster. RDDs support transformations like map, filter, and actions like reduce to operate on the distributed data in a parallel and fault-tolerant manner. Key concepts include lazy evaluation of transformations, caching of RDDs, and use of broadcast variables and accumulators for sharing data across nodes.
This document introduces Apache Spark, an open-source cluster computing system that provides fast, general execution engines for large-scale data processing. It summarizes key Spark concepts including resilient distributed datasets (RDDs) that let users spread data across a cluster, transformations that operate on RDDs, and actions that return values to the driver program. Examples demonstrate how to load data from files, filter and transform it using RDDs, and run Spark programs on a local or cluster environment.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
This document summarizes improvements to sorting and joining in Spark 2.0. Benchmarking shows Spark 2.0 performed joins and sorts faster than Spark 1.6 using fewer cores and less memory. The shuffle manager, which distributes data between partitions, was optimized in 2.0. Compression and limiting remote requests during shuffles reduced small files and improved performance. Garbage collection settings were also tuned.
Lessons Learned from Dockerizing Spark Workloads: Spark Summit East talk by T...Spark Summit
Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale production environments poses interesting challenges, especially when deploying distributed Big Data applications like Apache Spark.
Some of these challenges include container lifecycle management, smart scheduling for optimal resource utilization, network configuration and security, and performance. At BlueData, we’re “all in” on Docker containers – with a specific focus on Spark applications. We’ve learned first-hand how to address these challenges for Fortune 500 enterprises and government organizations that want to deploy Big Data workloads using Docker.
In this session, you’ll learn about networking Docker containers across multiple hosts securely. We’ll discuss ways to achieve high availability across distributed Big Data applications and hosts in your data center. And since we’re talking about very large volumes of data, performance is a key factor. So we’ll discuss some of the storage options we explored and implemented at BlueData to achieve near bare-metal I/O performance for Spark using Docker. We’ll share our lessons learned as well as some tips and tricks on how to Dockerize your Big Data applications in a reliable, scalable, and high-performance environment.
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterDataWorks Summit
This document discusses Apache Spark-on-YARN, which allows Spark applications to leverage existing Hadoop clusters. Spark improves efficiency over Hadoop via in-memory computing and supports rich APIs. Spark-on-YARN provides access to HDFS data and resources on Hadoop clusters without extra deployment costs. It supports running Spark jobs in YARN cluster and client modes. The document describes Yahoo's use of Spark-on-YARN for machine learning applications on large datasets.
This document provides a history and market overview of Apache Spark. It discusses the motivation for distributed data processing due to increasing data volumes, velocities and varieties. It then covers brief histories of Google File System, MapReduce, BigTable, and other technologies. Hadoop and MapReduce are explained. Apache Spark is introduced as a faster alternative to MapReduce that keeps data in memory. Competitors like Flink, Tez and Storm are also mentioned.
Top 5 mistakes when writing Spark applicationshadooparchbook
This document discusses common mistakes people make when writing Spark applications and provides recommendations to address them. It covers issues related to executor configuration, application failures due to shuffle block sizes exceeding limits, slow jobs caused by data skew, and managing the DAG to avoid excessive shuffles and stages. Recommendations include using smaller executors, increasing the number of partitions, addressing skew through techniques like salting, and preferring ReduceByKey over GroupByKey and TreeReduce over Reduce to improve performance and resource usage.
This document provides an overview of SolrCloud on Hadoop. It discusses how SolrCloud allows for distributed, highly scalable search capabilities on Hadoop clusters. Key components that work with SolrCloud are also summarized, including HDFS for storage, MapReduce for processing, and ZooKeeper for coordination services. The document demonstrates how SolrCloud can index and query large datasets stored in Hadoop.
This document summarizes a presentation about Tachyon, an open source memory-centric distributed storage system. It introduces Tachyon and how it can be used with Spark to resolve issues around slow data sharing, in-memory data loss during crashes, and data duplication. The presentation outlines new features in Tachyon 0.8.0 like tiered storage, pluggable data management policies, and a unified namespace across storage systems. It concludes by inviting users and collaborators to try, develop, and get involved with the Tachyon community.
Introduction to NoSQL | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
1) NoSQL databases are non-relational and schema-free, providing alternatives to SQL databases for big data and high availability applications.
2) Common NoSQL database models include key-value stores, column-oriented databases, document databases, and graph databases.
3) The CAP theorem states that a distributed data store can only provide two out of three guarantees around consistency, availability, and partition tolerance.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Beneath RDD in Apache Spark by Jacek LaskowskiSpark Summit
This document provides an overview of SparkContext and Resilient Distributed Datasets (RDDs) in Apache Spark. It discusses how to create RDDs using SparkContext functions like parallelize(), range(), and textFile(). It also covers DataFrames and converting between RDDs and DataFrames. The document discusses partitions and the level of parallelism in Spark, as well as the execution environment involving DAGScheduler, TaskScheduler, and SchedulerBackend. It provides examples of RDD lineage and describes Spark clusters like Spark Standalone and the Spark web UI.
Spark supports four cluster managers: Local, Standalone, YARN, and Mesos. YARN is highly recommended for production use. When running Spark on YARN, careful tuning of configuration settings like the number of executors, executor memory and cores, and dynamic allocation is important to optimize performance and resource utilization. Configuring queues also allows separating different applications by priority and resource needs.
This document discusses Spark Job Server, an open source project that allows Spark jobs to be submitted and run via a REST API. It provides features like job monitoring, context sharing between jobs to reuse cached data, and asynchronous APIs. The document outlines motivations for the project, how to use it including submitting and monitoring jobs, and future plans like high availability and hot failover support.
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
Securing Spark Applications by Kostas Sakellis and Marcelo VanzinSpark Summit
This document discusses securing Spark applications. It covers encryption, authentication, and authorization. Encryption protects data in transit using SASL or SSL. Authentication uses Kerberos to identify users. Authorization controls data access using Apache Sentry and the Sentry HDFS plugin, which synchronizes HDFS permissions with higher-level abstractions like tables. A future RecordService aims to provide a unified authorization system at the record level for Spark SQL.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
This document provides an overview of Apache Spark, including its core concepts, transformations and actions, persistence, parallelism, and examples. Spark is introduced as a fast and general engine for large-scale data processing, with advantages like in-memory computing, fault tolerance, and rich APIs. Key concepts covered include its resilient distributed datasets (RDDs) and lazy evaluation approach. The document also discusses Spark SQL, streaming, and integration with other tools.
Spark is a general-purpose cluster computing framework that provides high-level APIs and is faster than Hadoop for iterative jobs and interactive queries. It leverages cached data in cluster memory across nodes for faster performance. Spark supports various higher-level tools including SQL, machine learning, graph processing, and streaming.
Spark is a distributed data processing framework that uses RDDs (Resilient Distributed Datasets) to represent data distributed across a cluster. RDDs support transformations like map, filter, and actions like reduce to operate on the distributed data in a parallel and fault-tolerant manner. Key concepts include lazy evaluation of transformations, caching of RDDs, and use of broadcast variables and accumulators for sharing data across nodes.
This document introduces Apache Spark, an open-source cluster computing system that provides fast, general execution engines for large-scale data processing. It summarizes key Spark concepts including resilient distributed datasets (RDDs) that let users spread data across a cluster, transformations that operate on RDDs, and actions that return values to the driver program. Examples demonstrate how to load data from files, filter and transform it using RDDs, and run Spark programs on a local or cluster environment.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
This document summarizes improvements to sorting and joining in Spark 2.0. Benchmarking shows Spark 2.0 performed joins and sorts faster than Spark 1.6 using fewer cores and less memory. The shuffle manager, which distributes data between partitions, was optimized in 2.0. Compression and limiting remote requests during shuffles reduced small files and improved performance. Garbage collection settings were also tuned.
Lessons Learned from Dockerizing Spark Workloads: Spark Summit East talk by T...Spark Summit
Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale production environments poses interesting challenges, especially when deploying distributed Big Data applications like Apache Spark.
Some of these challenges include container lifecycle management, smart scheduling for optimal resource utilization, network configuration and security, and performance. At BlueData, we’re “all in” on Docker containers – with a specific focus on Spark applications. We’ve learned first-hand how to address these challenges for Fortune 500 enterprises and government organizations that want to deploy Big Data workloads using Docker.
In this session, you’ll learn about networking Docker containers across multiple hosts securely. We’ll discuss ways to achieve high availability across distributed Big Data applications and hosts in your data center. And since we’re talking about very large volumes of data, performance is a key factor. So we’ll discuss some of the storage options we explored and implemented at BlueData to achieve near bare-metal I/O performance for Spark using Docker. We’ll share our lessons learned as well as some tips and tricks on how to Dockerize your Big Data applications in a reliable, scalable, and high-performance environment.
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterDataWorks Summit
This document discusses Apache Spark-on-YARN, which allows Spark applications to leverage existing Hadoop clusters. Spark improves efficiency over Hadoop via in-memory computing and supports rich APIs. Spark-on-YARN provides access to HDFS data and resources on Hadoop clusters without extra deployment costs. It supports running Spark jobs in YARN cluster and client modes. The document describes Yahoo's use of Spark-on-YARN for machine learning applications on large datasets.
This document provides a history and market overview of Apache Spark. It discusses the motivation for distributed data processing due to increasing data volumes, velocities and varieties. It then covers brief histories of Google File System, MapReduce, BigTable, and other technologies. Hadoop and MapReduce are explained. Apache Spark is introduced as a faster alternative to MapReduce that keeps data in memory. Competitors like Flink, Tez and Storm are also mentioned.
Top 5 mistakes when writing Spark applicationshadooparchbook
This document discusses common mistakes people make when writing Spark applications and provides recommendations to address them. It covers issues related to executor configuration, application failures due to shuffle block sizes exceeding limits, slow jobs caused by data skew, and managing the DAG to avoid excessive shuffles and stages. Recommendations include using smaller executors, increasing the number of partitions, addressing skew through techniques like salting, and preferring ReduceByKey over GroupByKey and TreeReduce over Reduce to improve performance and resource usage.
This document provides an overview of SolrCloud on Hadoop. It discusses how SolrCloud allows for distributed, highly scalable search capabilities on Hadoop clusters. Key components that work with SolrCloud are also summarized, including HDFS for storage, MapReduce for processing, and ZooKeeper for coordination services. The document demonstrates how SolrCloud can index and query large datasets stored in Hadoop.
This document summarizes a presentation about Tachyon, an open source memory-centric distributed storage system. It introduces Tachyon and how it can be used with Spark to resolve issues around slow data sharing, in-memory data loss during crashes, and data duplication. The presentation outlines new features in Tachyon 0.8.0 like tiered storage, pluggable data management policies, and a unified namespace across storage systems. It concludes by inviting users and collaborators to try, develop, and get involved with the Tachyon community.
Introduction to NoSQL | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
1) NoSQL databases are non-relational and schema-free, providing alternatives to SQL databases for big data and high availability applications.
2) Common NoSQL database models include key-value stores, column-oriented databases, document databases, and graph databases.
3) The CAP theorem states that a distributed data store can only provide two out of three guarantees around consistency, availability, and partition tolerance.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
This document provides an overview of the Apache Spark framework. It covers Spark fundamentals including the Spark execution model using Resilient Distributed Datasets (RDDs), basic Spark programming, and common Spark libraries and use cases. Key topics include how Spark improves on MapReduce by operating in-memory and supporting general graphs through its directed acyclic graph execution model. The document also reviews Spark installation and provides examples of basic Spark programs in Scala.
Unit II Real Time Data Processing tools.pptxRahul Borate
Apache Spark is a lightning-fast cluster computing framework designed for real-time processing. It overcomes limitations of Hadoop by running 100 times faster in memory and 10 times faster on disk. Spark uses resilient distributed datasets (RDDs) that allow data to be partitioned across clusters and cached in memory for faster processing.
Your data is getting bigger while your boss is getting anxious to have insights! This tutorial covers Apache Spark that makes data analytics fast to write and fast to run. Tackle big datasets quickly through a simple API in Python, and learn one programming paradigm in order to deploy interactive, batch, and streaming applications while connecting to data sources incl. HDFS, Hive, JSON, and S3.
Productionizing Spark and the Spark Job ServerEvan Chan
You won't find this in many places - an overview of deploying, configuring, and running Apache Spark, including Mesos vs YARN vs Standalone clustering modes, useful config tuning parameters, and other tips from years of using Spark in production. Also, learn about the Spark Job Server and how it can help your organization deploy Spark as a RESTful service, track Spark jobs, and enable fast queries (including SQL!) of cached RDDs.
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
This document summarizes Lightning-Fast Cluster Computing with Spark and Shark, a presentation about the Spark and Shark frameworks. Spark is an open-source cluster computing system that aims to provide fast, fault-tolerant processing of large datasets. It uses resilient distributed datasets (RDDs) and supports diverse workloads with sub-second latency. Shark is a system built on Spark that exposes the HiveQL query language and compiles queries down to Spark programs for faster, interactive analysis of large datasets.
This slide introduces Hadoop Spark.
Just to help you construct an idea of Spark regarding its architecture, data flow, job scheduling, and programming.
Not all technical details are included.
spark example spark example spark examplespark examplespark examplespark exampleShidrokhGoudarzi1
Spark is a fast general-purpose engine for large-scale data processing. It has advantages over MapReduce like speed, ease of use, and running everywhere. Spark supports SQL querying, streaming, machine learning, and graph processing. It can run on Scala, Java, Python. Spark applications have drivers, executors, tasks and run RDDs and shared variables. The Spark shell provides an interactive way to learn the API and analyze data.
Spark is a fast, general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R for distributed tasks including SQL, streaming, and machine learning. Spark improves on MapReduce by keeping data in-memory, allowing iterative algorithms to run faster than disk-based approaches. Resilient Distributed Datasets (RDDs) are Spark's fundamental data structure, acting as a fault-tolerant collection of elements that can be operated on in parallel.
http://bit.ly/1BTaXZP – As organizations look for even faster ways to derive value from big data, they are turning to Apache Spark is an in-memory processing framework that offers lightning-fast big data analytics, providing speed, developer productivity, and real-time processing advantages. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Spark Streaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis. This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop. By the end of the session, you’ll come away with a deeper understanding of how you can unlock deeper insights from your data, faster, with Spark.
Real time Analytics with Apache Kafka and Apache SparkRahul Jain
A presentation cum workshop on Real time Analytics with Apache Kafka and Apache Spark. Apache Kafka is a distributed publish-subscribe messaging while other side Spark Streaming brings Spark's language-integrated API to stream processing, allows to write streaming applications very quickly and easily. It supports both Java and Scala. In this workshop we are going to explore Apache Kafka, Zookeeper and Spark with a Web click streaming example using Spark Streaming. A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing.
Presentation detailed about capabilities of In memory Analytic using Apache Spark. Apache Spark overview with programming mode, cluster mode with Mosos, supported operations and comparison with Hadoop Map Reduce. Elaborating Apache Spark Stack expansion like Shark, Streaming, MLib, GraphX
This document provides an overview of installing and programming with Apache Spark on Hortonworks Data Platform (HDP). It introduces Spark and its components, benefits over other frameworks, and Hortonworks' commitment to Spark. The document outlines an example Spark programming workflow using Resilient Distributed Datasets (RDDs) in Scala, and covers common RDD transformations, actions, and persistence methods. It also discusses Spark deployment modes like standalone and on YARN, and reference HDP architectures using Spark.
Spark is an open-source cluster computing framework that can run analytics applications much faster than Hadoop by keeping data in memory rather than on disk. While Spark can access Hadoop's HDFS storage system and is often used as a replacement for Hadoop's MapReduce, Hadoop remains useful for batch processing and Spark is not expected to fully replace it. Spark provides speed, ease of use, and integration of SQL, streaming, and machine learning through its APIs in multiple languages.
Spark is an open-source cluster computing framework that uses in-memory processing to allow data sharing across jobs for faster iterative queries and interactive analytics, it uses Resilient Distributed Datasets (RDDs) that can survive failures through lineage tracking and supports programming in Scala, Java, and Python for batch, streaming, and machine learning workloads.
Spark adds some abstractions and generalizations and performance optimizations to achieve much better efficiency especially in iterative workloads. Yet, spark does not concern itself with being a data file system while Hadoop has what is called HDFS.
Spark can leverage existing distributed files systems (like HDFS), a distributed data base (like HBase), traditional databases through its JDBC or ODBC adaptors, and flat files in local file systems or on a file store like S3 in Amazon cloud.
Hadoop MapReduce framework is similar to Spark in that it uses master slave-like paradigm. It has one Master node (which consists of a job tracker, name node, and RAM) and Worker Nodes (each worker node consists of a task tracker, data node, and a RAM). The task tracker in a worker node is analogues to an executor in Spark environment.
This document provides an overview of installing and programming with Apache Spark on the Hortonworks Data Platform (HDP). It discusses how Spark fits within HDP and can be used for batch processing, streaming, SQL queries and machine learning. The document outlines how to install Spark on HDP using Ambari and describes Spark programming with Resilient Distributed Datasets (RDDs), transformations, actions and caching/persistence. It provides examples of Spark APIs and programming patterns.
Similar to Introduction to Apache Spark :: Lagos Scala Meetup session 2 (20)
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