This document discusses SQL-on-Hadoop using Apache Tajo. It provides an overview of Hadoop and MapReduce, which are frameworks for distributed processing of large datasets. It then describes SQL-on-Hadoop and Apache Tajo, an open source SQL-on-Hadoop implementation. The document outlines the contents which include sections on Hadoop and MapReduce, SQL-on-Hadoop, and Apache Tajo.
MongoDB is an open-source, document-oriented database that provides scalability and high performance. It uses a dynamic schema and allows for embedding of documents. MongoDB can be deployed in a standalone, replica set, or sharded cluster configuration. A replica set provides redundancy and automatic failover through replication, while sharding allows for horizontal scalability by partitioning data across multiple servers. Key features include indexing, queries, text search, and geospatial support.
This document provides an overview and introduction to Hadoop, an open-source framework for storing and processing large datasets in a distributed computing environment. It discusses what Hadoop is, common use cases like ETL and analysis, key architectural components like HDFS and MapReduce, and why Hadoop is useful for solving problems involving "big data" through parallel processing across commodity hardware.
This document compares the Google File System (GFS) and the Hadoop Distributed File System (HDFS). It discusses their motivations, architectures, performance measurements, and role in larger systems. GFS was designed for Google's data processing needs, while HDFS was created as an open-source framework for Hadoop applications. Both divide files into blocks and replicate data across multiple servers for reliability. The document provides details on their file structures, data flow models, consistency approaches, and benchmark results. It also explores how systems like MapReduce/Hadoop utilize these underlying storage systems.
A Basic Introduction to the Hadoop eco system - no animationSameer Tiwari
The document provides a basic introduction to the Hadoop ecosystem. It describes the key components which include HDFS for raw storage, HBase for columnar storage, Hive and Pig as query engines, MapReduce and YARN as schedulers, Flume for streaming, Mahout for machine learning, Oozie for workflows, and Zookeeper for distributed locking. Each component is briefly explained including their goals, architecture, and how they relate to and build upon each other.
July 2010 Triangle Hadoop Users Group - Chad Vawter Slidesryancox
This document provides an overview of setting up a Hadoop cluster, including installing the Apache Hadoop distribution, configuring SSH keys for passwordless login between nodes, configuring environment variables and Hadoop configuration files, and starting and stopping the HDFS and MapReduce services. It also briefly discusses alternative Hadoop distributions from Cloudera and Yahoo, as well as using cloud platforms like Amazon EC2 for Hadoop clusters.
More about Hadoop
www.beinghadoop.com
https://www.facebook.com/hadoopinfo
This PPT Gives information about
Complete Hadoop Architecture and
information about
how user request is processed in Hadoop?
About Namenode
Datanode
jobtracker
tasktracker
Hadoop installation Post Configurations
Hadoop has a master/slave architecture. The master node runs the NameNode, JobTracker, and optionally SecondaryNameNode. The NameNode stores metadata about data locations. DataNodes on slave nodes store the actual data blocks. The JobTracker schedules jobs, assigning tasks to TaskTrackers on slaves which perform the work. The SecondaryNameNode assists the NameNode in the event of failures. MapReduce jobs split files into blocks, map tasks process the blocks in parallel on slaves, and reduce tasks consolidate the results.
MongoDB is an open-source, document-oriented database that provides scalability and high performance. It uses a dynamic schema and allows for embedding of documents. MongoDB can be deployed in a standalone, replica set, or sharded cluster configuration. A replica set provides redundancy and automatic failover through replication, while sharding allows for horizontal scalability by partitioning data across multiple servers. Key features include indexing, queries, text search, and geospatial support.
This document provides an overview and introduction to Hadoop, an open-source framework for storing and processing large datasets in a distributed computing environment. It discusses what Hadoop is, common use cases like ETL and analysis, key architectural components like HDFS and MapReduce, and why Hadoop is useful for solving problems involving "big data" through parallel processing across commodity hardware.
This document compares the Google File System (GFS) and the Hadoop Distributed File System (HDFS). It discusses their motivations, architectures, performance measurements, and role in larger systems. GFS was designed for Google's data processing needs, while HDFS was created as an open-source framework for Hadoop applications. Both divide files into blocks and replicate data across multiple servers for reliability. The document provides details on their file structures, data flow models, consistency approaches, and benchmark results. It also explores how systems like MapReduce/Hadoop utilize these underlying storage systems.
A Basic Introduction to the Hadoop eco system - no animationSameer Tiwari
The document provides a basic introduction to the Hadoop ecosystem. It describes the key components which include HDFS for raw storage, HBase for columnar storage, Hive and Pig as query engines, MapReduce and YARN as schedulers, Flume for streaming, Mahout for machine learning, Oozie for workflows, and Zookeeper for distributed locking. Each component is briefly explained including their goals, architecture, and how they relate to and build upon each other.
July 2010 Triangle Hadoop Users Group - Chad Vawter Slidesryancox
This document provides an overview of setting up a Hadoop cluster, including installing the Apache Hadoop distribution, configuring SSH keys for passwordless login between nodes, configuring environment variables and Hadoop configuration files, and starting and stopping the HDFS and MapReduce services. It also briefly discusses alternative Hadoop distributions from Cloudera and Yahoo, as well as using cloud platforms like Amazon EC2 for Hadoop clusters.
More about Hadoop
www.beinghadoop.com
https://www.facebook.com/hadoopinfo
This PPT Gives information about
Complete Hadoop Architecture and
information about
how user request is processed in Hadoop?
About Namenode
Datanode
jobtracker
tasktracker
Hadoop installation Post Configurations
Hadoop has a master/slave architecture. The master node runs the NameNode, JobTracker, and optionally SecondaryNameNode. The NameNode stores metadata about data locations. DataNodes on slave nodes store the actual data blocks. The JobTracker schedules jobs, assigning tasks to TaskTrackers on slaves which perform the work. The SecondaryNameNode assists the NameNode in the event of failures. MapReduce jobs split files into blocks, map tasks process the blocks in parallel on slaves, and reduce tasks consolidate the results.
This document provides an overview of HDFS and MapReduce. It discusses the core components of Hadoop including HDFS, the namenode, datanodes, and MapReduce components like the JobTracker and TaskTracker. It then covers HDFS topics such as the storage hierarchy, file reads and writes, blocks, and basic filesystem operations. It also summarizes MapReduce concepts like the inspiration from functional programming, the basic MapReduce flow, and example code for a word count problem.
This document provides an overview of Hadoop, including its architecture, installation, configuration, and commands. It describes the challenges of large-scale data that Hadoop addresses through distributed processing and storage across clusters. The key components of Hadoop are HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and provides fault tolerance through replication, while MapReduce allows parallel processing of large datasets through a map and reduce programming model. The document also outlines how to install and configure Hadoop in pseudo-distributed and fully distributed modes.
Treasure Data on The YARN - Hadoop Conference Japan 2014Ryu Kobayashi
Ryu Kobayashi from Treasure Data gave a presentation on using YARN (Yet Another Resource Negotiator) with Hadoop. Some key points:
- YARN was introduced to improve Hadoop resource management by separating processing from scheduling.
- Configuration changes are required when moving from MRv1 to YARN, including properties for memory allocation and scheduler configuration.
- Container execution, directories, and other components were adapted in the transition from JobTracker to the ResourceManager and NodeManager architecture in YARN.
- Proper configuration of YARN is important to avoid bugs, and tools from distributions can help with configuration.
This document introduces Apache Spark. It discusses MapReduce and its limitations in processing large datasets. Spark was developed to address these limitations by enabling fast sharing of data across clusters using resilient distributed datasets (RDDs). RDDs allow transformations like map and filter to be applied lazily and support operations like join and groupByKey. This provides benefits for iterative and interactive queries compared to MapReduce.
This document summarizes key Hadoop configuration parameters that affect MapReduce job performance and provides suggestions for optimizing these parameters under different conditions. It describes the MapReduce workflow and phases, defines important parameters like dfs.block.size, mapred.compress.map.output, and mapred.tasktracker.map/reduce.tasks.maximum. It explains how to configure these parameters based on factors like cluster size, data and task complexity, and available resources. The document also discusses other performance aspects like temporary space, JVM tuning, and reducing reducer initialization overhead.
Hadoop is an open source framework for running large-scale data processing jobs across clusters of computers. It has two main components: HDFS for reliable storage and Hadoop MapReduce for distributed processing. HDFS stores large files across nodes through replication and uses a master-slave architecture. MapReduce allows users to write map and reduce functions to process large datasets in parallel and generate results. Hadoop has seen widespread adoption for processing massive datasets due to its scalability, reliability and ease of use.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
This document outlines the key tasks and responsibilities of a Hadoop administrator. It discusses five top Hadoop admin tasks: 1) cluster planning which involves sizing hardware requirements, 2) setting up a fully distributed Hadoop cluster, 3) adding or removing nodes from the cluster, 4) upgrading Hadoop versions, and 5) providing high availability to the cluster. It provides guidance on hardware sizing, installing and configuring Hadoop daemons, and demos of setting up a cluster, adding nodes, and enabling high availability using NameNode redundancy. The goal is to help administrators understand how to plan, deploy, and manage Hadoop clusters effectively.
This document provides an overview of Hadoop and its core components HDFS and MapReduce. It describes how HDFS uses a master/slave architecture with a single NameNode master and multiple DataNode slaves to store and retrieve data in a fault-tolerant manner. The NameNode manages the filesystem namespace and monitors data replication, while DataNodes store data blocks and perform read/write operations. It also discusses high availability techniques for the NameNode and core functions like block placement, garbage collection and stale replica detection in HDFS.
The document summarizes key components of Hadoop including:
1) The NameNode, located on the master node, stores metadata for HDFS such as file locations and attributes.
2) DataNodes, located on slave nodes, store and retrieve data blocks.
3) The JobTracker, located on the master node, schedules jobs and assigns tasks to TaskTrackers on slave nodes.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
Big data interview questions and answersKalyan Hadoop
This document provides an overview of the Hadoop Distributed File System (HDFS), including its goals, design, daemons, and processes for reading and writing files. HDFS is designed for storing very large files across commodity servers, and provides high throughput and reliability through replication. The key components are the NameNode, which manages metadata, and DataNodes, which store data blocks. The Secondary NameNode assists the NameNode in checkpointing filesystem state periodically.
Hadoop is a distributed processing framework for large datasets. It stores data across clusters of commodity hardware in a Hadoop Distributed File System (HDFS) and provides tools for distributed processing using MapReduce. HDFS uses a master-slave architecture with a namenode managing metadata and datanodes storing data blocks. Data is replicated across nodes for reliability. MapReduce allows distributed processing of large datasets in parallel across clusters.
This document provides tips for tuning Hadoop clusters and jobs. It recommends:
1) Choosing optimal numbers of mappers and reducers per node and oversubscribing CPUs slightly.
2) Adjusting memory allocations for tasks and ensuring they do not exceed total memory available.
3) Increasing buffers for sorting and shuffling, compressing intermediate data, and using combiners to reduce data sent to reducers.
Apache Drill (http://incubator.apache.org/drill/) is a distributed system for interactive analysis of large-scale datasets, inspired by Google’s Dremel technology. It is designed to scale to thousands of servers and able to process Petabytes of data in seconds. Since its inception in mid 2012, Apache Drill has gained widespread interest in the community, attracting hundreds of interested individuals and companies. In the talk we discuss how Apache Drill enables ad-hoc interactive query at scale, walking through typical use cases and delve into Drill's architecture, the data flow and query languages as well as data sources supported.
The document discusses developing a comprehensive monitoring approach for Hadoop clusters. It recommends starting with basic monitoring of nodes using Nagios and Cacti for metrics like CPU usage, disk usage, and network traffic. It then suggests adding Hadoop-specific checks like monitoring DataNodes and graphing NameNode operations using JMX. Finally, it proposes setting alarms based on JMX metrics and regularly reviewing filesystem growth and utilization.
Nesta apresentação é demonstrado alguns recursos disponíveis num cluster Hadoop, bem como os principais componentes do ecossistema utilizado no Magazine Luiza. Além disso, temos uma comparação com grandes nomes do mercado que também utilizam esta tecnologia.
At Spotify we collect huge volumes of data for many purposes. Reporting to labels, powering our product features, and analyzing user growth are some of our most common ones. Additionally, we collect many operational metrics related to the responsiveness, utilization and capacity of our servers. To store and process this data, we use scalable and fault-tolerant multi-system infrastructure, and Apache Hadoop is a key part of it. Surprisingly or not, Apache Hadoop generates large amounts of data in the form of logs and metrics that describe its behaviour and performance. To process this data in a scalable and performant manner we use … also Hadoop! During this presentation, I will talk about how we analyze various logs generated by Apache Hadoop using custom scripts (written in Pig or Java/Python MapReduce) and available open-source tools to get data-driven answers to many questions related to the behaviour of our 690-node Hadoop cluster. At Spotify we frequently leverage these tools to learn how fast we are growing, when to buy new nodes, how to calculate the empirical retention policy for each dataset, optimize the scheduler, benchmark the cluster, find its biggest offenders (both people and datasets) and more.
Accessing external hadoop data sources using pivotal e xtension framework (px...Sameer Tiwari
The Pivotal eXtension Framework (PXF) allows SQL queries to access data stored in various data stores like HDFS, HBase, Hive, and others. PXF uses a pluggable architecture with components like a fragmenter, accessor, resolver, and analyzer that can be extended to connect to new data sources. It addresses the divide between SQL and MapReduce/Hive by enabling SQL queries to retrieve data without needing to copy it to the database first. PXF provides a single hop access to external data and fully parallel processing for high throughput queries across data stores.
This document provides an overview of HDFS and MapReduce. It discusses the core components of Hadoop including HDFS, the namenode, datanodes, and MapReduce components like the JobTracker and TaskTracker. It then covers HDFS topics such as the storage hierarchy, file reads and writes, blocks, and basic filesystem operations. It also summarizes MapReduce concepts like the inspiration from functional programming, the basic MapReduce flow, and example code for a word count problem.
This document provides an overview of Hadoop, including its architecture, installation, configuration, and commands. It describes the challenges of large-scale data that Hadoop addresses through distributed processing and storage across clusters. The key components of Hadoop are HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and provides fault tolerance through replication, while MapReduce allows parallel processing of large datasets through a map and reduce programming model. The document also outlines how to install and configure Hadoop in pseudo-distributed and fully distributed modes.
Treasure Data on The YARN - Hadoop Conference Japan 2014Ryu Kobayashi
Ryu Kobayashi from Treasure Data gave a presentation on using YARN (Yet Another Resource Negotiator) with Hadoop. Some key points:
- YARN was introduced to improve Hadoop resource management by separating processing from scheduling.
- Configuration changes are required when moving from MRv1 to YARN, including properties for memory allocation and scheduler configuration.
- Container execution, directories, and other components were adapted in the transition from JobTracker to the ResourceManager and NodeManager architecture in YARN.
- Proper configuration of YARN is important to avoid bugs, and tools from distributions can help with configuration.
This document introduces Apache Spark. It discusses MapReduce and its limitations in processing large datasets. Spark was developed to address these limitations by enabling fast sharing of data across clusters using resilient distributed datasets (RDDs). RDDs allow transformations like map and filter to be applied lazily and support operations like join and groupByKey. This provides benefits for iterative and interactive queries compared to MapReduce.
This document summarizes key Hadoop configuration parameters that affect MapReduce job performance and provides suggestions for optimizing these parameters under different conditions. It describes the MapReduce workflow and phases, defines important parameters like dfs.block.size, mapred.compress.map.output, and mapred.tasktracker.map/reduce.tasks.maximum. It explains how to configure these parameters based on factors like cluster size, data and task complexity, and available resources. The document also discusses other performance aspects like temporary space, JVM tuning, and reducing reducer initialization overhead.
Hadoop is an open source framework for running large-scale data processing jobs across clusters of computers. It has two main components: HDFS for reliable storage and Hadoop MapReduce for distributed processing. HDFS stores large files across nodes through replication and uses a master-slave architecture. MapReduce allows users to write map and reduce functions to process large datasets in parallel and generate results. Hadoop has seen widespread adoption for processing massive datasets due to its scalability, reliability and ease of use.
This document provides an agenda and overview for a presentation on Hadoop 2.x configuration and MapReduce performance tuning. The presentation covers hardware selection and capacity planning for Hadoop clusters, key configuration parameters for operating systems, HDFS, and YARN, and performance tuning techniques for MapReduce applications. It also demonstrates the Hadoop Vaidya performance diagnostic tool.
This document outlines the key tasks and responsibilities of a Hadoop administrator. It discusses five top Hadoop admin tasks: 1) cluster planning which involves sizing hardware requirements, 2) setting up a fully distributed Hadoop cluster, 3) adding or removing nodes from the cluster, 4) upgrading Hadoop versions, and 5) providing high availability to the cluster. It provides guidance on hardware sizing, installing and configuring Hadoop daemons, and demos of setting up a cluster, adding nodes, and enabling high availability using NameNode redundancy. The goal is to help administrators understand how to plan, deploy, and manage Hadoop clusters effectively.
This document provides an overview of Hadoop and its core components HDFS and MapReduce. It describes how HDFS uses a master/slave architecture with a single NameNode master and multiple DataNode slaves to store and retrieve data in a fault-tolerant manner. The NameNode manages the filesystem namespace and monitors data replication, while DataNodes store data blocks and perform read/write operations. It also discusses high availability techniques for the NameNode and core functions like block placement, garbage collection and stale replica detection in HDFS.
The document summarizes key components of Hadoop including:
1) The NameNode, located on the master node, stores metadata for HDFS such as file locations and attributes.
2) DataNodes, located on slave nodes, store and retrieve data blocks.
3) The JobTracker, located on the master node, schedules jobs and assigns tasks to TaskTrackers on slave nodes.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
This document provides an overview of Hadoop and its core components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its programming model and the Hadoop Distributed File System (HDFS) for storage. HDFS stores data redundantly across nodes for reliability. The core subprojects of Hadoop include MapReduce, HDFS, Hive, HBase, and others.
Big data interview questions and answersKalyan Hadoop
This document provides an overview of the Hadoop Distributed File System (HDFS), including its goals, design, daemons, and processes for reading and writing files. HDFS is designed for storing very large files across commodity servers, and provides high throughput and reliability through replication. The key components are the NameNode, which manages metadata, and DataNodes, which store data blocks. The Secondary NameNode assists the NameNode in checkpointing filesystem state periodically.
Hadoop is a distributed processing framework for large datasets. It stores data across clusters of commodity hardware in a Hadoop Distributed File System (HDFS) and provides tools for distributed processing using MapReduce. HDFS uses a master-slave architecture with a namenode managing metadata and datanodes storing data blocks. Data is replicated across nodes for reliability. MapReduce allows distributed processing of large datasets in parallel across clusters.
This document provides tips for tuning Hadoop clusters and jobs. It recommends:
1) Choosing optimal numbers of mappers and reducers per node and oversubscribing CPUs slightly.
2) Adjusting memory allocations for tasks and ensuring they do not exceed total memory available.
3) Increasing buffers for sorting and shuffling, compressing intermediate data, and using combiners to reduce data sent to reducers.
Apache Drill (http://incubator.apache.org/drill/) is a distributed system for interactive analysis of large-scale datasets, inspired by Google’s Dremel technology. It is designed to scale to thousands of servers and able to process Petabytes of data in seconds. Since its inception in mid 2012, Apache Drill has gained widespread interest in the community, attracting hundreds of interested individuals and companies. In the talk we discuss how Apache Drill enables ad-hoc interactive query at scale, walking through typical use cases and delve into Drill's architecture, the data flow and query languages as well as data sources supported.
The document discusses developing a comprehensive monitoring approach for Hadoop clusters. It recommends starting with basic monitoring of nodes using Nagios and Cacti for metrics like CPU usage, disk usage, and network traffic. It then suggests adding Hadoop-specific checks like monitoring DataNodes and graphing NameNode operations using JMX. Finally, it proposes setting alarms based on JMX metrics and regularly reviewing filesystem growth and utilization.
Nesta apresentação é demonstrado alguns recursos disponíveis num cluster Hadoop, bem como os principais componentes do ecossistema utilizado no Magazine Luiza. Além disso, temos uma comparação com grandes nomes do mercado que também utilizam esta tecnologia.
At Spotify we collect huge volumes of data for many purposes. Reporting to labels, powering our product features, and analyzing user growth are some of our most common ones. Additionally, we collect many operational metrics related to the responsiveness, utilization and capacity of our servers. To store and process this data, we use scalable and fault-tolerant multi-system infrastructure, and Apache Hadoop is a key part of it. Surprisingly or not, Apache Hadoop generates large amounts of data in the form of logs and metrics that describe its behaviour and performance. To process this data in a scalable and performant manner we use … also Hadoop! During this presentation, I will talk about how we analyze various logs generated by Apache Hadoop using custom scripts (written in Pig or Java/Python MapReduce) and available open-source tools to get data-driven answers to many questions related to the behaviour of our 690-node Hadoop cluster. At Spotify we frequently leverage these tools to learn how fast we are growing, when to buy new nodes, how to calculate the empirical retention policy for each dataset, optimize the scheduler, benchmark the cluster, find its biggest offenders (both people and datasets) and more.
Accessing external hadoop data sources using pivotal e xtension framework (px...Sameer Tiwari
The Pivotal eXtension Framework (PXF) allows SQL queries to access data stored in various data stores like HDFS, HBase, Hive, and others. PXF uses a pluggable architecture with components like a fragmenter, accessor, resolver, and analyzer that can be extended to connect to new data sources. It addresses the divide between SQL and MapReduce/Hive by enabling SQL queries to retrieve data without needing to copy it to the database first. PXF provides a single hop access to external data and fully parallel processing for high throughput queries across data stores.
Zbrush 4R6 introduces several new features including Zremesher, Curve Bridge Brush, and Trim Brush. Zremesher allows for automatic topology generation independent of form. Curve Bridge Brush simplifies the workflow for bridging meshes to 3 steps instead of 4. Trim Brush allows for directly trimming and closing holes in meshes.
사람을 위한 발명-사용자경험(UX) @조광수 연세대학교 정보대학원 UX Lab 교수cbs15min
상품의 진정한 가치는 그것을 만든 사람이 아니라 그것을 사용하는 사람들에 의해 부여되기도 합니다. 장인이 아무리 열심히 만든 물건이라도 막상 사람들이 사지도 않고 관심조차 두지 않는다면 그 상품은 시장에서 곧 사라지고 맙니다. 사람들이 무엇을 필요로 하고 무엇을 욕망하며, 새로운 기술에 어떻게 반응하는지를 분석해야 하는 것도 이러한 까닭입니다. 사용자경험은 인간을 위한 발명, 그 시작을 가능하게 합니다. 사용자경험(UX) 을 이해하는 똑똑한 방법. 여러분께 알려드립니다.
Start Your Career as a Big Data Expert in Top MNC's. Join today Big Data and Hadoop Training in Chandigarh at BigBoxx Academy and get 100% Placement Assistance.
Overview of stinger interactive query for hiveDavid Kaiser
This document provides an overview of the Stinger initiative to improve the performance of Hive interactive queries. The Stinger project worked to optimize Hive so that queries return results in seconds instead of minutes or hours by implementing features like Hive on Tez, vectorized processing, predicate pushdown, the ORC file format, and a cost-based optimizer. These optimizations improved Hive performance by over 100 times, allowing interactive use of Hive for the first time on large datasets.
The HP Hadoop Platform provides high performance and scalability for big data workloads. It offers several components for high throughput processing with MapReduce and TEZ, as well as lower latency querying with Presto. The platform also includes Spark for in-memory computation and machine learning, OpenTSDB for time series data, and Solr for scalable search capabilities.
This document discusses the Stinger initiative to improve the performance of Apache Hive. Stinger aims to speed up Hive queries by 100x, scale queries from terabytes to petabytes of data, and expand SQL support. Key developments include optimizing Hive to run on Apache Tez, the vectorized query execution engine, cost-based optimization using Optiq, and performance improvements from the ORC file format. The goals of Stinger Phase 3 are to deliver interactive query performance for Hive by integrating these technologies.
This document provides an introduction to Hadoop and big data. It discusses the new kinds of large, diverse data being generated and the need for platforms like Hadoop to process and analyze this data. It describes the core components of Hadoop, including HDFS for distributed storage and MapReduce for distributed processing. It also discusses some of the common applications of Hadoop and other projects in the Hadoop ecosystem like Hive, Pig, and HBase that build on the core Hadoop framework.
Big Data Meets HPC - Exploiting HPC Technologies for Accelerating Big Data Pr...inside-BigData.com
In this deck from the Stanford HPC Conference, DK Panda from Ohio State University presents: Big Data Meets HPC - Exploiting HPC Technologies for Accelerating Big Data Processing.
"This talk will provide an overview of challenges in accelerating Hadoop, Spark and Memcached on modern HPC clusters. An overview of RDMA-based designs for Hadoop (HDFS, MapReduce, RPC and HBase), Spark, Memcached, Swift, and Kafka using native RDMA support for InfiniBand and RoCE will be presented. Enhanced designs for these components to exploit NVM-based in-memory technology and parallel file systems (such as Lustre) will also be presented. Benefits of these designs on various cluster configurations using the publicly available RDMA-enabled packages from the OSU HiBD project (http://hibd.cse.ohio-state.edu) will be shown."
Watch the video: https://youtu.be/iLTYkTandEA
Learn more: http://web.cse.ohio-state.edu/~panda.2/
and
http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Scaling Storage and Computation with Hadoopyaevents
Hadoop provides a distributed storage and a framework for the analysis and transformation of very large data sets using the MapReduce paradigm. Hadoop is partitioning data and computation across thousands of hosts, and executes application computations in parallel close to their data. A Hadoop cluster scales computation capacity, storage capacity and IO bandwidth by simply adding commodity servers. Hadoop is an Apache Software Foundation project; it unites hundreds of developers, and hundreds of organizations worldwide report using Hadoop. This presentation will give an overview of the Hadoop family projects with a focus on its distributed storage solutions
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop includes a storage part called HDFS for reliable data storage, and a processing part called MapReduce that processes data in parallel on a large cluster. Hadoop also includes additional projects like Hive, Pig, HBase, Zookeeper, Oozie, and Sqoop that together form a powerful data processing ecosystem.
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)VMware Tanzu
Recorded at SpringOne2GX 2013 in Santa Clara, CA
Speaker: Adam Shook
This session assumes absolutely no knowledge of Apache Hadoop and will provide a complete introduction to all the major aspects of the Hadoop ecosystem of projects and tools. If you are looking to get up to speed on Hadoop, trying to work out what all the Big Data fuss is about, or just interested in brushing up your understanding of MapReduce, then this is the session for you. We will cover all the basics with detailed discussion about HDFS, MapReduce, YARN (MRv2), and a broad overview of the Hadoop ecosystem including Hive, Pig, HBase, ZooKeeper and more.
Learn More about Spring XD at: http://projects.spring.io/spring-xd
Learn More about Gemfire XD at:
http://www.gopivotal.com/big-data/pivotal-hd
An Open Source Incremental Processing Framework called Hoodie is summarized. Key points:
- Hoodie provides upsert and incremental processing capabilities on top of a Hadoop data lake to enable near real-time queries while avoiding costly full scans.
- It introduces primitives like upsert and incremental pull to apply mutations and consume only changed data.
- Hoodie stores data on HDFS and provides different views like read optimized, real-time, and log views to balance query performance and data latency for analytical workloads.
- The framework is open source and built on Spark, providing horizontal scalability and leveraging existing Hadoop SQL query engines like Hive and Presto.
Cheetah is a custom data warehouse system built on top of Hadoop that provides high performance for storing and querying large datasets. It uses a virtual view abstraction over star and snowflake schemas to provide a simple yet powerful SQL-like query language. The system architecture utilizes MapReduce to parallelize query execution across many nodes. Cheetah employs columnar data storage and compression, multi-query optimization, and materialized views to improve query performance. Based on evaluations, Cheetah can efficiently handle both small and large queries and outperforms single-query execution when processing batches of queries together.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of petabytes of data. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. Many large companies use Hadoop for applications such as log analysis, web indexing, and data mining of large datasets.
CDH is a popular distribution of Apache Hadoop and related projects that delivers scalable storage and distributed computing through Apache-licensed open source software. It addresses challenges in storing and analyzing large datasets known as Big Data. Hadoop is a framework for distributed processing of large datasets across computer clusters using simple programming models. Its core components are HDFS for storage, MapReduce for processing, and YARN for resource management. The Hadoop ecosystem also includes tools like Kafka, Sqoop, Hive, Pig, Impala, HBase, Spark, Mahout, Solr, Kudu, and Sentry that provide functionality like messaging, data transfer, querying, machine learning, search, and authorization.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and additional tools like Hive, Pig, HBase, Zookeeper, Flume, Sqoop and Oozie that make up its ecosystem. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
This document discusses cloud and big data technologies. It provides an overview of Hadoop and its ecosystem, which includes components like HDFS, MapReduce, HBase, Zookeeper, Pig and Hive. It also describes how data is stored in HDFS and HBase, and how MapReduce can be used for parallel processing across large datasets. Finally, it gives examples of using MapReduce to implement algorithms for word counting, building inverted indexes and performing joins.
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The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like LSTM, bi-LSTM, and convolutional neural networks. Finally, it presents results from evaluating different chatbot models on various metrics like validation score.
The document discusses challenges with using reinforcement learning for robotics. While simulations allow fast training of agents, there is often a "reality gap" when transferring learning to real robots. Other approaches like imitation learning and self-supervised learning can be safer alternatives that don't require trial-and-error. To better apply reinforcement learning, robots may need model-based approaches that learn forward models of the world, as well as techniques like active localization that allow robots to gather targeted information through interactive perception. Closing the reality gap will require finding ways to better match simulations to reality or allow robots to learn from real-world experiences.
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The document discusses linear algebra concepts including:
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This document describes the steps to convert a TensorFlow model to a TensorRT engine for inference. It includes steps to parse the model, optimize it, generate a runtime engine, serialize and deserialize the engine, as well as perform inference using the engine. It also provides code snippets for a PReLU plugin implementation in C++.
The document discusses machine reading comprehension (MRC) techniques for question answering (QA) systems, comparing search-based and natural language processing (NLP)-based approaches. It covers key milestones in the development of extractive QA models using NLP, from early sentence-level models to current state-of-the-art techniques like cross-attention, self-attention, and transfer learning. It notes the speed and scalability benefits of combining search and reading methods for QA.
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