This document provides an introduction to the Pig analytics platform for Hadoop. It begins with an overview of big data and Hadoop, then discusses the basics of Pig including its data model, language called Pig Latin, and components. Key points made are that Pig provides a high-level language for expressing data analysis processes, compiles queries into MapReduce programs for execution, and allows for easier programming than lower-level systems like Java MapReduce. The document also compares Pig to SQL and Hive, and demonstrates visualizing Pig jobs with the Twitter Ambrose tool.
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
This document provides an introduction to GraphX, which is an Apache Spark component for graphs and graph-parallel computations. It describes different types of graphs like regular graphs, directed graphs, and property graphs. It shows how to create a property graph in GraphX by defining vertex and edge RDDs. It also demonstrates various graph operators that can be used to perform operations on graphs, such as finding the number of vertices/edges, degrees, longest paths, and top vertices by degree. The goal is to introduce the basics of representing and analyzing graph data with GraphX.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
This document discusses concepts related to data streams and real-time analytics. It begins with introductions to stream data models and sampling techniques. It then covers filtering, counting, and windowing queries on data streams. The document discusses challenges of stream processing like bounded memory and proposes solutions like sampling and sketching. It provides examples of applications in various domains and tools for real-time data streaming and analytics.
Simplifying Big Data Analytics with Apache SparkDatabricks
Apache Spark is a fast and general-purpose cluster computing system for large-scale data processing. It improves on MapReduce by allowing data to be kept in memory across jobs, enabling faster iterative jobs. Spark consists of a core engine along with libraries for SQL, streaming, machine learning, and graph processing. The document discusses new APIs in Spark including DataFrames, which provide a tabular interface like in R/Python, and data sources, which allow plugging external data systems into Spark. These changes aim to make Spark easier for data scientists to use at scale.
The document discusses the Hadoop ecosystem, which includes core Apache Hadoop components like HDFS, MapReduce, YARN, as well as related projects like Pig, Hive, HBase, Mahout, Sqoop, ZooKeeper, Chukwa, and HCatalog. It provides overviews and diagrams explaining the architecture and purpose of each component, positioning them as core functionality that speeds up Hadoop processing and makes Hadoop more usable and accessible.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
This document provides an introduction to GraphX, which is an Apache Spark component for graphs and graph-parallel computations. It describes different types of graphs like regular graphs, directed graphs, and property graphs. It shows how to create a property graph in GraphX by defining vertex and edge RDDs. It also demonstrates various graph operators that can be used to perform operations on graphs, such as finding the number of vertices/edges, degrees, longest paths, and top vertices by degree. The goal is to introduce the basics of representing and analyzing graph data with GraphX.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
This document discusses concepts related to data streams and real-time analytics. It begins with introductions to stream data models and sampling techniques. It then covers filtering, counting, and windowing queries on data streams. The document discusses challenges of stream processing like bounded memory and proposes solutions like sampling and sketching. It provides examples of applications in various domains and tools for real-time data streaming and analytics.
Simplifying Big Data Analytics with Apache SparkDatabricks
Apache Spark is a fast and general-purpose cluster computing system for large-scale data processing. It improves on MapReduce by allowing data to be kept in memory across jobs, enabling faster iterative jobs. Spark consists of a core engine along with libraries for SQL, streaming, machine learning, and graph processing. The document discusses new APIs in Spark including DataFrames, which provide a tabular interface like in R/Python, and data sources, which allow plugging external data systems into Spark. These changes aim to make Spark easier for data scientists to use at scale.
The document discusses the Hadoop ecosystem, which includes core Apache Hadoop components like HDFS, MapReduce, YARN, as well as related projects like Pig, Hive, HBase, Mahout, Sqoop, ZooKeeper, Chukwa, and HCatalog. It provides overviews and diagrams explaining the architecture and purpose of each component, positioning them as core functionality that speeds up Hadoop processing and makes Hadoop more usable and accessible.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
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
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
The document discusses Apache Spark, an open source cluster computing framework for real-time data processing. It notes that Spark is up to 100 times faster than Hadoop for in-memory processing and 10 times faster on disk. The main feature of Spark is its in-memory cluster computing capability, which increases processing speeds. Spark runs on a driver-executor model and uses resilient distributed datasets and directed acyclic graphs to process data in parallel across a cluster.
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
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.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Spark is an open-source cluster computing framework that allows processing of large datasets in parallel. It supports multiple languages and provides advanced analytics capabilities. Spark SQL was built to overcome limitations of Apache Hive by running on Spark and providing a unified data access layer, SQL support, and better performance on medium and small datasets. Spark SQL uses DataFrames and a SQLContext to allow SQL queries on different data sources like JSON, Hive tables, and Parquet files. It provides a scalable architecture and integrates with Spark's RDD API.
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
----------
What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
This document provides an overview of big data and Hadoop. It defines big data as high-volume, high-velocity, and high-variety data that requires new techniques to capture value. Hadoop is introduced as an open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for storage and MapReduce for parallel processing. Benefits of Hadoop are its ability to handle large amounts of structured and unstructured data quickly and cost-effectively at large scales.
This document provides an introduction to Hadoop, including its motivation and key components. It discusses the scale of cloud computing that Hadoop addresses, and describes the core Hadoop technologies - the Hadoop Distributed File System (HDFS) and MapReduce framework. It also briefly introduces the Hadoop ecosystem, including other related projects like Pig, HBase, Hive and ZooKeeper. Sample code is walked through to illustrate MapReduce programming. Key aspects of HDFS like fault tolerance, scalability and data reliability are summarized.
Hadoop is a distributed processing framework for large datasets. It utilizes HDFS for storage and MapReduce as its programming model. The Hadoop ecosystem has expanded to include many other tools. YARN was developed to address limitations in the original Hadoop architecture. It provides a common platform for various data processing engines like MapReduce, Spark, and Storm. YARN improves scalability, utilization, and supports multiple workloads by decoupling cluster resource management from application logic. It allows different applications to leverage shared Hadoop cluster resources.
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
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
This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
This document provides an overview of YARN (Yet Another Resource Negotiator), the resource management system for Hadoop. It describes the key components of YARN including the Resource Manager, Node Manager, and Application Master. The Resource Manager tracks cluster resources and schedules applications, while Node Managers monitor nodes and containers. Application Masters communicate with the Resource Manager to manage applications. YARN allows Hadoop to run multiple applications like Spark and HBase, improves on MapReduce scheduling, and transforms Hadoop into a distributed operating system for big data processing.
Pig is a platform for analyzing large datasets that uses a high-level language to express data analysis programs. It compiles programs into MapReduce jobs that can run in parallel on a Hadoop cluster. Pig provides built-in functions for common tasks and allows users to define their own custom functions (UDFs). Programs can be run locally or on a Hadoop cluster by placing commands in a script or Grunt shell.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
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
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
The document discusses Apache Spark, an open source cluster computing framework for real-time data processing. It notes that Spark is up to 100 times faster than Hadoop for in-memory processing and 10 times faster on disk. The main feature of Spark is its in-memory cluster computing capability, which increases processing speeds. Spark runs on a driver-executor model and uses resilient distributed datasets and directed acyclic graphs to process data in parallel across a cluster.
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
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.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Spark is an open-source cluster computing framework that allows processing of large datasets in parallel. It supports multiple languages and provides advanced analytics capabilities. Spark SQL was built to overcome limitations of Apache Hive by running on Spark and providing a unified data access layer, SQL support, and better performance on medium and small datasets. Spark SQL uses DataFrames and a SQLContext to allow SQL queries on different data sources like JSON, Hive tables, and Parquet files. It provides a scalable architecture and integrates with Spark's RDD API.
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
----------
What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
This document provides an overview of big data and Hadoop. It defines big data as high-volume, high-velocity, and high-variety data that requires new techniques to capture value. Hadoop is introduced as an open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for storage and MapReduce for parallel processing. Benefits of Hadoop are its ability to handle large amounts of structured and unstructured data quickly and cost-effectively at large scales.
This document provides an introduction to Hadoop, including its motivation and key components. It discusses the scale of cloud computing that Hadoop addresses, and describes the core Hadoop technologies - the Hadoop Distributed File System (HDFS) and MapReduce framework. It also briefly introduces the Hadoop ecosystem, including other related projects like Pig, HBase, Hive and ZooKeeper. Sample code is walked through to illustrate MapReduce programming. Key aspects of HDFS like fault tolerance, scalability and data reliability are summarized.
Hadoop is a distributed processing framework for large datasets. It utilizes HDFS for storage and MapReduce as its programming model. The Hadoop ecosystem has expanded to include many other tools. YARN was developed to address limitations in the original Hadoop architecture. It provides a common platform for various data processing engines like MapReduce, Spark, and Storm. YARN improves scalability, utilization, and supports multiple workloads by decoupling cluster resource management from application logic. It allows different applications to leverage shared Hadoop cluster resources.
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
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
This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
This document provides an overview of YARN (Yet Another Resource Negotiator), the resource management system for Hadoop. It describes the key components of YARN including the Resource Manager, Node Manager, and Application Master. The Resource Manager tracks cluster resources and schedules applications, while Node Managers monitor nodes and containers. Application Masters communicate with the Resource Manager to manage applications. YARN allows Hadoop to run multiple applications like Spark and HBase, improves on MapReduce scheduling, and transforms Hadoop into a distributed operating system for big data processing.
Pig is a platform for analyzing large datasets that uses a high-level language to express data analysis programs. It compiles programs into MapReduce jobs that can run in parallel on a Hadoop cluster. Pig provides built-in functions for common tasks and allows users to define their own custom functions (UDFs). Programs can be run locally or on a Hadoop cluster by placing commands in a script or Grunt shell.
Présentation générales du Big Data et zoom sur des cas d'usage dans l'industrie et les services.
Présentation réalisée à l'occasion de l'événement Big data de Niort du 20 mars 2014
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
The document discusses Hortonworks and its strategy to support Apache Hadoop. Hortonworks aims to make Hadoop easy to use and deployable at enterprise scale. It offers the Hortonworks Data Platform, training, support subscriptions, and consulting services to help organizations adopt Hadoop. Hortonworks' goal is to establish Hadoop as the next-generation data platform and help more of the world's data be processed using Apache Hadoop.
Hadoop as we know is a Java based massive scalable distributed framework for processing large data (several peta bytes) across a cluster (1000s) of commodity computers.
The Hadoop ecosystem has grown over the last few years and there is a lot of jargon in terms of tools as well as frameworks.
Many organizations are investing & innovating heavily in Hadoop to make it better and easier. The mind map on the next slide should be useful to get a high level picture of the ecosystem.
This document discusses Apache Hadoop, its current state and future direction. It provides an overview of Hadoop as an open source platform for storing and analyzing large amounts of data across distributed systems. The document outlines Hortonworks' vision of making Hadoop an enterprise-ready platform that can power data-driven businesses and unify both traditional and big data analytics methods. It also announces an upcoming Hadoop conference in June 2012 with sessions showcasing real-world Hadoop uses.
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Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
This document discusses big data analytics techniques like Hadoop MapReduce and NoSQL databases. It begins with an introduction to big data and how the exponential growth of data presents challenges that conventional databases can't handle. It then describes Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers using a simple programming model. Key aspects of Hadoop covered include MapReduce, HDFS, and various other related projects like Pig, Hive, HBase etc. The document concludes with details about how Hadoop MapReduce works, including its master-slave architecture and how it provides fault tolerance.
Cloud computing, big data, and mobile technologies are driving major changes in the IT world. Cloud computing provides scalable computing resources over the internet. Big data involves extremely large data sets that are analyzed to reveal business insights. Hadoop is an open-source software framework that allows distributed processing of big data across commodity hardware. It includes tools like HDFS for storage and MapReduce for distributed computing. The Hadoop ecosystem also includes additional tools for tasks like data integration, analytics, workflow management, and more. These emerging technologies are changing how businesses use and analyze data.
1) Hadoop is a framework for distributed processing of large datasets across clusters of computers using a simple programming model.
2) Virtualizing Hadoop enables rapid deployment, high availability, elastic scaling, and consolidation of big data workloads on a common infrastructure.
3) Serengeti is a tool that automates the deployment and management of Hadoop clusters on vSphere in under 30 minutes through simple commands.
The document discusses virtualizing Hadoop clusters on VMware vSphere. It describes how Hadoop enables parallel processing of large datasets across clusters using MapReduce. Virtualizing Hadoop provides benefits like simple operations, high availability, and elastic scaling. The document outlines challenges with using Hadoop and how virtualization addresses them. It provides examples of deploying Hadoop clusters on Serengeti and configuring different distributions. Performance results show little overhead from virtualization and benefits of local storage. Joint engineering with Hortonworks adds high availability to Hadoop master daemons using vSphere features.
1) Hadoop is a framework for distributed processing of large datasets across clusters of computers using a simple programming model.
2) Virtualizing Hadoop enables rapid deployment, high availability, elastic scaling, and consolidation of big data workloads on a common infrastructure.
3) Serengeti is a tool that automates the deployment and management of Hadoop clusters on vSphere in under 30 minutes through simple commands.
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This document contains the resume of Hassan Qureshi. He has over 9 years of experience as a Hadoop Lead Developer with expertise in technologies like Hadoop, HDFS, Hive, Pig and HBase. Currently he works as the technical lead of a data engineering team developing insights from data. He has extensive hands-on experience installing, configuring and maintaining Hadoop clusters in different environments.
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In less than an hour, you’ll learn:
-Key capabilities available in Hortonworks Data Platform for Windows
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The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
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#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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Presented by Vladimir Iglovikov:
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People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
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Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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3. Agenda
Introduction to Big Data
Basics of Hadoop
Hadoop MapReduce WordCount Demo
Hadoop Ecosystem landscape
Basics of Pig and Pig Latin
Pig WordCount Demo
Pig vs SQL and Pig vs Hive
Visualization of Pig MapReduce Jobs with Twitter Ambrose
4. Pre-requisites
Basic understanding of Hadoop, HDFS and MapReduce.
Laptop with VMware Player or Oracle VirtualBox installed.
Please copy the VMware image of 64 bit Ubuntu Server 12.04
distributed in the USB flash drive.
Uncompress the VMware image and launch the image using VMware
Player / Virtual Box.
Login to the VM with the credentials:
hduser / hduser
Check if the environment variables
HADOOP_HOME, PIG_HOME, etc are set.
5. Introduction to Big Data
…. AND FAR FAR BEYOND
WEB User generated content
Mobile Web
User Click Stream
Sentiment
Social Network
External Demographics
CRM Weblogs Business Data Feeds
Offer history HD Video
A / B Testing Speech to Text
Dynamic Pricing Product / Service Logs
Affiliate Network SMS / MMS
Search Marketing
ERP Segmentation Behavioral Targeting
Offer Details Dynamic Funnels
Purchase Details Customer Touches
Purchase Records Support Contacts
Payment Records
Megabytes Gigabytes Terabytes Petabytes
Source: http://datameer.com
6. Introduction to Big Data
Source: http://blog.softwareinsider.org/2012/02/27/mondays-musings-beyond-the-three-vs-of-big-data-viscosity-and-virality/
7. Big Data Analysis
RDBMS (scalability) Hadoop comes to
the rescue
Parallel RDBMS (expensive)
Programming Language (too
complex)
9. History of Hadoop
Scalable distributed
file system for large
distributed data-
intensive
applications
“The Google File System” by Sanjay
Ghemawat, Howard Gobioff, and Shun-Tak
Leung
http://research.google.com/archive/gfs.html
Programming model
and an associated
implementation for
processing and
generating large data
sets`
“MapReduce: Simplified Data Processing on Large
Clusters” by Jeffrey Dean and Sanjay Ghemawat
http://research.google.com/archive/mapreduce.html
10. Introduction to Hadoop
HDFS
Hadoop Distributed File System
A distributed, scalable, and portable filesystem written in Java
for the Hadoop framework
Provides high-throughput access to application data.
Runs on large clusters of commodity machines
Is used to store large datasets.
MapReduce
Distributed data processing model and execution environment
that runs on large clusters of commodity machines
Also called MR.
Programs are inherently parallel.
14. Pig
“Pig Latin: A Not-So-Foreign Language for Data Processing”
Christopher Olston, Benjamin Reed, Utkarsh Srivastava, Ravi Kumar, Andrew Tomkins
(Yahoo! Research)
http://www.sigmod08.org/program_glance.shtml#sigmod_industrial_program
http://infolab.stanford.edu/~usriv/papers/pig-latin.pdf
15. Pig
High level data flow language for exploring very large datasets.
Provides an engine for executing data flows in parallel on Hadoop.
Compiler that produces sequences of MapReduce programs
Structure is amenable to substantial parallelization
Operates on files in HDFS
Metadata not required, but used when available
Key Properties of Pig:
Ease of programming: Trivial to achieve parallel execution of simple
and parallel data analysis tasks
Optimization opportunities: Allows the user to focus on semantics
rather than efficiency
Extensibility: Users can create their own functions to do special-
purpose processing
18. Load Users Load Pages
Filter by Age
Join on Name
Group on url
Count Clicks
Order by Clicks
Take Top 5
Save results
19. Pig vs Hadoop
5% of the MR code.
5% of the MR development time.
Within 25% of the MR execution time.
Readable and reusable.
Easy to learn DSL.
Increases programmer productivity.
No Java expertise required.
Anyone [eg. BI folks] can trigger the Jobs.
Insulates against Hadoop complexity
Version upgrades
Changes in Hadoop interfaces
JobConf configuration tuning
Job Chains
20. Committers of
Pig
Source: http://pig.apache.org/whoweare.html
21. Who is using Pig?
Source: http://wiki.apache.org/pig/PoweredBy
22. Pig use cases
Processing many Data Sources
Data Analysis
Text Processing
Structured
Semi-Structured
ETL
Machine Learning
Advantage of Sampling in any use case
23. Pig in real-world
LinkedIn
Twitter
Reporting, ETL, targeted emails & recommendations, spam analysis, ML
24. Components of Pig
Pig Latin
Submit a script directly
Grunt
Pig Shell
PigServer
Java Class similar to JDBC interface
25. Pig Execution Modes
Local Mode
Need access to a single machine
All files are installed and run using your local host and file system
Is invoked by using the -x local flag
pig -x local
MapReduce Mode
Mapreduce mode is the default mode
Need access to a Hadoop cluster and HDFS installation.
Can also be invoked by using the -x mapreduce flag or just pig
pig
pig -x mapreduce
26. Pig Latin Statements
Pig Latin Statements work with relations
Field is a piece of data.
John
Tuple is an ordered set of fields.
(John,18,4.0F)
Bag is a collection of tuples.
(1,{(1,2,3)})
Relation is a bag
27. Pig Simple Datatypes
Simple Type Description Example
int Signed 32-bit integer 10
long Signed 64-bit integer Data: 10L or 10l
Display: 10L
float 32-bit floating point Data: 10.5F or 10.5f or
10.5e2f or 10.5E2F
Display: 10.5F or 1050.0F
double 64-bit floating point Data: 10.5 or 10.5e2 or
10.5E2
Display: 10.5 or 1050.0
chararray Character array (string) in Unicode hello world
UTF-8 format
bytearray Byte array (blob)
boolean boolean true/false (case insensitive)
28. Pig Complex Datatypes
Type Description Example
tuple An ordered set of fields. (19,2)
bag An collection of tuples. {(19,2), (18,1)}
map A set of key value pairs. [open#apache]
29. Pig Commands
Statement Description
Load Read data from the file system
Store Write data to the file system
Dump Write output to stdout
Foreach Apply expression to each record and generate
one or more records
Filter Apply predicate to each record and remove
records where false
Group / Cogroup Collect records with the same key from one or
more inputs
Join Join two or more inputs based on a key
Order Sort records based on a Key
Distinct Remove duplicate records
Union Merge two datasets
Limit Limit the number of records
Split Split data into 2 or more sets, based on filter
conditions
30. Pig Diagnostic Operators
Statement Description
Describe Returns the schema of the relation
Dump Dumps the results to the screen
Explain Displays execution plans.
Illustrate Displays a step-by-step execution of a
sequence of statements
35. Pig vs Hive
Feature Pig Hive
Language PigLatin SQL-like
Schemas / Types Yes (implicit) Yes (explicit)
Partitions No Yes
Server No Optional (Thrift)
User Defined Functions (UDF) Yes (Java, Python, Ruby, etc) Yes (Java)
Custom Serializer/Deserializer Yes Yes
DFS Direct Access Yes (explicit) Yes (implicit)
Join/Order/Sort Yes Yes
Shell Yes Yes
Streaming Yes Yes
Web Interface No Yes
JDBC/ODBC No Yes (limited)
Source:http://www.larsgeorge.com/2009/10/hive-vs-pig.html
36. Storage Options in Pig
HDFS
Plain Text
Binary format
Customized format (XML, JSON, Protobuf, Thrift, etc)
RDBMS (DBStorage)
Cassandra (CassandraStorage)
HBase (HBaseStorage)
Avro (AvroStorage)
37. Visualization of Pig MapReduce
Jobs
Twitter Ambrose: https://github.com/twitter/ambrose
Platform for visualization and real-time monitoring of MapReduce data workflows
Presents a global view of all the MapReduce jobs derived from the workflow after planning
and optimization
Ambrose provides the following in a web UI:
A chord diagram to visualize job dependencies and current state
A table view of all the associated jobs, along with their current state
A highlight view of the currently running jobs
An overall script progress bar
Ambrose is built using:
D3.js
Bootstrap
Supported Runtimes: Designed to support any Hadoop workflow runtime
Currently supports Pig MR Jobs
Future work would include Cascading, Scalding, Cascalog and Hive