Hadoop_EcoSystem_Pradeep_MG

This ppt is to help students to learn about big data, Hadoop, HDFS, MApReduce, architecture of HDFS,

Spark is a big data processing tool built with Scala that runs on the Java Virtual Machine (JVM). It is up to 100 times faster than Hadoop for iterative jobs because it keeps intermediate data in memory rather than writing to disk. Spark uses Resilient Distributed Datasets (RDDs) that allow data to be kept in memory across transformations and actions. RDDs also maintain lineage graphs to allow recovery from failures.

Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created in 2005 and is designed to reliably handle large volumes of data and complex computations in a distributed fashion. The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing data in parallel across large clusters of computers. It is widely adopted by companies handling big data like Yahoo, Facebook, Amazon and Netflix.

Big Data with Hadoop & Spark Training: http://bit.ly/2skCodH This CloudxLab Understanding MapReduce tutorial helps you to understand MapReduce in detail. Below are the topics covered in this tutorial: 1) Thinking in Map / Reduce 2) Understanding Unix Pipeline 3) Examples to understand MapReduce 4) Merging 5) Mappers & Reducers 6) Mapper Example 7) Input Split 8) mapper() & reducer() Code 9) Example - Count number of words in a file using MapReduce 10) Example - Compute Max Temperature using MapReduce 11) Hands-on - Count number of words in a file using MapReduce on CloudxLab

Apache Hadoop is a popular open-source framework for storing and processing large datasets across clusters of computers. It includes Apache HDFS for distributed storage, YARN for job scheduling and resource management, and MapReduce for parallel processing. The Hortonworks Data Platform is an enterprise-grade distribution of Apache Hadoop that is fully open source.

I originally gave this presentation as an internal briefing at SDSC based on my experiences in working with Spark to solve scientific problems.

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 scalable, fault-tolerant storage and MapReduce for parallel processing. The core components of Hadoop - HDFS and MapReduce - allow for distributed processing of large datasets across commodity hardware, providing capabilities for scalability, cost-effectiveness, and efficient distributed computing.

Hadoop Streaming allows any executable or script to be used as a MapReduce job. It works by launching the executable or script as a separate process and communicating with it via stdin and stdout. The executable or script receives key-value pairs in a predefined format and outputs new key-value pairs that are collected. Hadoop Streaming uses PipeMapper and PipeReducer to adapt the external processes to the MapReduce framework. It provides a simple way to run MapReduce jobs without writing Java code.

MapReduce is a programming model for processing large datasets in parallel. It works by breaking the dataset into independent chunks which are processed by the map function, and then grouping the output of the maps into partitions to be processed by the reduce function. Hadoop uses MapReduce to provide fault tolerance by restarting failed tasks and monitoring the JobTracker and TaskTrackers. MapReduce programs can be written in languages other than Java using Hadoop Streaming.

Hive provides an SQL-like interface to query data stored in Hadoop's HDFS distributed file system and processed using MapReduce. It allows users without MapReduce programming experience to write queries that Hive then compiles into a series of MapReduce jobs. The document discusses Hive's components, data model, query planning and optimization techniques, and performance compared to other frameworks like Pig.

In the ppt i have explained the basic difference between the hadoop architectures. hadoop architecture 1 and hadoop architecture 2 i have taken the reference from the website for the preperation.

Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as a programming model and HDFS for storage. MapReduce allows for massively parallel processing of large datasets by breaking jobs into smaller tasks that can be run in parallel on multiple machines. HDFS stores very large files across machines in a distributed file system for fault tolerance.

What makes a big-data platform 'cloud-optimized'. Here's our (Qubole's) shot at it. @Cloud-Asia 2014.

Hadoop is a system for processing large amounts of data using MapReduce and HDFS. HDFS is the storage component that splits files into blocks and stores multiple copies for reliability. MapReduce is the processing framework where mappers process key-value pairs in parallel and reducers aggregate the outputs. While Hadoop can process huge datasets, other systems like Pig, Hive, HBase, Accumulo, Avro, ZooKeeper, and Flume provide additional functionality for tasks like SQL queries, real-time processing, coordination, serialization, and data aggregation.

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.

Detailed documentation of the complex Map Reduce Execution Architecture with the terminology explanations and the execution of MapReduce Jar File

This document provides an introduction and overview of Apache Spark. It discusses why Spark is useful, describes some Spark basics including Resilient Distributed Datasets (RDDs) and DataFrames, and gives a quick tour of Spark Core, SQL, and Streaming functionality. It also provides some tips for using Spark and describes how to set up Spark locally. The presenter is introduced as a data engineer who uses Spark to load data from Kafka streams into Redshift and Cassandra. Ways to learn more about Spark are suggested at the end.

This document provides an overview of big data and Hadoop. It discusses the scale of big data, noting that Facebook handles 180PB per year and Twitter handles 1.2 million tweets per second. It also covers the volume, variety, and velocity challenges of big data. Hadoop and MapReduce are introduced as the leading solutions for distributed storage and processing of big data using a scale-out architecture. Key ideas of Hadoop include storing large data across multiple machines in HDFS and processing that data in parallel using MapReduce jobs.

This document summarizes Hadoop MapReduce, including its goals of distribution and reliability. It describes the roles of mappers, reducers, and other system components like the JobTracker and TaskTracker. Mappers process input splits in parallel and generate intermediate key-value pairs. Reducers sort and group the outputs by key before processing each unique key. The JobTracker coordinates jobs while the TaskTracker manages tasks on each node.

Pyshark is a wrapper around tshark comand line utility to capture a live Network packet or from a capture file. Pyshark is useful in parsing capture data for analysis.

In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.

This document provides an overview of Hadoop MapReduce scheduling algorithms. It discusses several commonly used algorithms like FIFO, fair scheduling, and capacity scheduler. It also introduces more advanced algorithms such as LATE, SAMR, ESAMR, locality-aware scheduling, and center-of-gravity scheduling that aim to improve metrics like fairness, throughput, response time, and resource utilization. The document concludes by listing references for further reading on MapReduce scheduling techniques.

The document presents an introduction to MapReduce. It discusses how MapReduce provides an easy framework for distributed computing by allowing programmers to write simple map and reduce functions without worrying about complex distributed systems issues. It outlines Google's implementation of MapReduce and how it uses the Google File System for fault tolerance. Alternative open-source implementations like Apache Hadoop are also covered. The document discusses how MapReduce has been widely adopted by companies to process massive amounts of data and analyzes some criticism of MapReduce from database experts. It concludes by noting trends in using MapReduce as a parallel database and for multi-core processing.

This document provides a summary of MapReduce algorithms. It begins with background on the author's experience blogging about MapReduce algorithms in academic papers. It then provides an overview of MapReduce concepts including the mapper and reducer functions. Several examples of recently published MapReduce algorithms are described for tasks like machine learning, finance, and software engineering. One algorithm is examined in depth for building a low-latency key-value store. Finally, recommendations are provided for designing MapReduce algorithms including patterns, performance, and cost/maintainability considerations. An appendix lists additional MapReduce algorithms from academic papers in areas such as AI, biology, machine learning, and mathematics.

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

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.

In this talk we make an introduction to data processing with big data and review the basic concepts in MapReduce programming with Hadoop. We also comment about the use of Pig to simplify the development of data processing applications YDN Tuesdays are geek meetups organized the first Tuesday of each month by YDN in London

This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.

This document provides an overview of MapReduce, a programming model developed by Google for processing and generating large datasets in a distributed computing environment. It describes how MapReduce abstracts away the complexities of parallelization, fault tolerance, and load balancing to allow developers to focus on the problem logic. Examples are given showing how MapReduce can be used for tasks like word counting in documents and joining datasets. Implementation details and usage statistics from Google demonstrate how MapReduce has scaled to process exabytes of data across thousands of machines.

Miss goodheart created a PowerPoint to test out the Slideshare tool, which was introduced to her by Sharon Tonner on January 20th, 2011.

The document provides an introduction to Hadoop. It discusses how Google developed its own infrastructure using Google File System (GFS) and MapReduce to power Google Search due to limitations with databases. Hadoop was later developed based on these Google papers to provide an open-source implementation of GFS and MapReduce. The document also provides overviews of the HDFS file system and MapReduce programming model in Hadoop.

Big Data Characteristics Contents Explosion in Quantity of Data Importance of Big Data Usage Example in Big Data Challenges in Big Data Hadoop Ecosystem

Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. The core of Hadoop consists of HDFS for storage and MapReduce for processing. Hadoop has been expanded with additional projects including YARN for job scheduling and resource management, Pig and Hive for SQL-like queries, HBase for column-oriented storage, Zookeeper for coordination, and Ambari for provisioning and managing Hadoop clusters. Hadoop provides scalable and cost-effective solutions for storing and analyzing massive amounts of data.