The document provides an overview of big data and Hadoop fundamentals. It discusses what big data is, the characteristics of big data, and how it differs from traditional data processing approaches. It then describes the key components of Hadoop including HDFS for distributed storage, MapReduce for distributed processing, and YARN for resource management. HDFS architecture and features are explained in more detail. MapReduce tasks, stages, and an example word count job are also covered. The document concludes with a discussion of Hive, including its use as a data warehouse infrastructure on Hadoop and its query language HiveQL.
Top Hadoop Big Data Interview Questions and Answers for FresherJanBask Training
Top Hadoop Big Data Interview Questions and Answers for Fresher , Hadoop, Hadoop Big Data, Hadoop Training, Hadoop Interview Question, Hadoop Interview Answers, Hadoop Big Data Interview Question
Data Pipelines in Hadoop - SAP Meetup in Tel Aviv larsgeorge
This talk is about showing the complexity in building a data pipeline in Hadoop, starting with the technology aspect, and the correlating to the skillsets of current Hadoop adopters.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
The document provides an agenda and slides for a presentation on architectural considerations for data warehousing with Hadoop. The presentation discusses typical data warehouse architectures and challenges, how Hadoop can complement existing architectures, and provides an example use case of implementing a data warehouse with Hadoop using the Movielens dataset. Key aspects covered include ingestion of data from various sources using tools like Flume and Sqoop, data modeling and storage formats in Hadoop, processing the data using tools like Hive and Spark, and exporting results to a data warehouse.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
This document discusses modern data architecture and Apache Hadoop's role within it. It presents WANdisco and its Non-Stop Hadoop solution, which extends HDFS across multiple data centers to provide 100% uptime for Hadoop deployments. Non-Stop Hadoop uses WANdisco's patented distributed coordination engine to synchronize HDFS metadata across sites separated by wide area networks, enabling continuous availability of HDFS data and global HDFS deployments.
This document summarizes Andrew Brust's presentation on using the Microsoft platform for big data. It discusses Hadoop and HDInsight, MapReduce, using Hive with ODBC and the BI stack. It also covers Hekaton, NoSQL, SQL Server Parallel Data Warehouse, and PolyBase. The presentation includes demos of HDInsight, MapReduce, and using Hive with the BI stack.
Top Hadoop Big Data Interview Questions and Answers for FresherJanBask Training
Top Hadoop Big Data Interview Questions and Answers for Fresher , Hadoop, Hadoop Big Data, Hadoop Training, Hadoop Interview Question, Hadoop Interview Answers, Hadoop Big Data Interview Question
Data Pipelines in Hadoop - SAP Meetup in Tel Aviv larsgeorge
This talk is about showing the complexity in building a data pipeline in Hadoop, starting with the technology aspect, and the correlating to the skillsets of current Hadoop adopters.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
The document provides an agenda and slides for a presentation on architectural considerations for data warehousing with Hadoop. The presentation discusses typical data warehouse architectures and challenges, how Hadoop can complement existing architectures, and provides an example use case of implementing a data warehouse with Hadoop using the Movielens dataset. Key aspects covered include ingestion of data from various sources using tools like Flume and Sqoop, data modeling and storage formats in Hadoop, processing the data using tools like Hive and Spark, and exporting results to a data warehouse.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
This document discusses modern data architecture and Apache Hadoop's role within it. It presents WANdisco and its Non-Stop Hadoop solution, which extends HDFS across multiple data centers to provide 100% uptime for Hadoop deployments. Non-Stop Hadoop uses WANdisco's patented distributed coordination engine to synchronize HDFS metadata across sites separated by wide area networks, enabling continuous availability of HDFS data and global HDFS deployments.
This document summarizes Andrew Brust's presentation on using the Microsoft platform for big data. It discusses Hadoop and HDInsight, MapReduce, using Hive with ODBC and the BI stack. It also covers Hekaton, NoSQL, SQL Server Parallel Data Warehouse, and PolyBase. The presentation includes demos of HDInsight, MapReduce, and using Hive with the BI stack.
The document discusses big data and Hadoop. It describes the three V's of big data - variety, volume, and velocity. It also discusses Hadoop components like HDFS, MapReduce, Pig, Hive, and YARN. Hadoop is a framework for storing and processing large datasets in a distributed computing environment. It allows for the ability to store and use all types of data at scale using commodity hardware.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
The document proposes an Agile Big Data model to address perceived issues with traditional Hadoop implementations. It discusses the motivation for change and outlines an Agile model with self-organized roles including data stewards, data scientists, project teams, and an architecture board. Key aspects of the proposed model include independent and self-managed project teams, a domain-driven data model, and emphasis on data quality and governance through the involvement of data stewards across domains.
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Proc...Agile Testing Alliance
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Processing by "Sampat Kumar" from "Harman". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
Introduction to Hadoop - The EssentialsFadi Yousuf
This document provides an introduction to Hadoop, including:
- A brief history of Hadoop and how it was created to address limitations of relational databases for big data.
- An overview of core Hadoop concepts like its shared-nothing architecture and using computation near storage.
- Descriptions of HDFS for distributed storage and MapReduce as the original programming framework.
- How the Hadoop ecosystem has grown to include additional frameworks like Hive, Pig, HBase and tools like Sqoop and Zookeeper.
- A discussion of YARN which separates resource management from job scheduling in Hadoop.
Hadoop is being used across organizations for a variety of purposes like data staging, analytics, security monitoring, and manufacturing quality assurance. However, most organizations still have separate systems optimized for specific workloads. Hadoop has the potential to relieve pressure on these systems by handling data staging, archives, transformations, and exploration. Going forward, Hadoop will need to provide enterprise-grade capabilities like high performance, security, data protection, and support for both analytical and operational workloads to fully replace specialized systems and become the main enterprise data platform.
This document discusses leveraging major market opportunities with Microsoft Azure. It notes that worldwide cloud software revenue is expected to grow significantly between 2010-2017. By 2017, nearly $1 of every $5 spent on applications will be consumed via the cloud. It also notes that hybrid cloud deployments will be common for large enterprises by the end of 2017. The document then outlines several major enterprise workloads that can be moved to Azure, including test/development, SharePoint, SQL/business intelligence, application migration, SAP, and identity/Office 365. It provides examples of how partners can help customers with these types of migrations.
Architectural considerations for Hadoop Applicationshadooparchbook
The document discusses architectural considerations for Hadoop applications using a case study on clickstream analysis. It covers requirements for data ingestion, storage, processing, and orchestration. For data storage, it considers HDFS vs HBase, file formats, and compression formats. SequenceFiles are identified as a good choice for raw data storage as they allow for splittable compression.
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
The document discusses integrating Hadoop with relational databases. It describes scenarios where reference data is stored in an RDBMS and used in Hadoop, Hadoop is used for offline analytics on data stored in an RDBMS, and exporting MapReduce outputs to an RDBMS. It then presents a case study on extending SQOOP for optimized Oracle integration and compares performance with and without the extension. Other tools for Hadoop-RDBMS integration are also briefly outlined.
Realtime Analytics with Hadoop and HBaselarsgeorge
The document discusses realtime analytics using Hadoop and HBase. It begins by introducing the speaker and their experience. It then discusses moving from batch processing with Hadoop to more realtime needs, and how systems like HBase can help bridge that gap. Several designs are presented for using HBase and Hadoop together to enable both realtime and batch analytics on large datasets.
This document summarizes Hortonworks' Hadoop distribution called Hortonworks Data Platform (HDP). It discusses how HDP provides a comprehensive data management platform built around Apache Hadoop and YARN. HDP includes tools for storage, processing, security, operations and accessing data through batch, interactive and real-time methods. The document also outlines new capabilities in HDP 2.2 like improved engines for SQL, Spark and streaming and expanded deployment options.
Deep learning has become widespread as frameworks such as TensorFlow and PyTorch have made it easy to onboard machine learning applications. However, while it is easy to start developing with these frameworks on your local developer machine, scaling up a model to run on a cluster and train on huge datasets is still challenging. Code and dependencies have to be copied to every machine and defining the cluster configurations is tedious and error-prone. In addition, troubleshooting errors and aggregating logs is difficult. Ad-hoc solutions also lack resource guarantees, isolation from other jobs, and fault tolerance.
To solve these problems and make scaling deep learning easy, we have made several enhancements to Hadoop and built an open-source deep learning platform called TonY. In this talk, Anthony and Keqiu will discuss new Hadoop features useful for deep learning, such as GPU resource support, and deep dive into TonY, which lets you run deep learning programs natively on Hadoop. We will discuss TonY's architecture and how it allows users to manage their deep learning jobs, acting as a portal from which to launch notebooks, monitor jobs, and visualize training results.
Building a Big Data platform with the Hadoop ecosystemGregg Barrett
This presentation provides a brief insight into a Big Data platform using the Hadoop ecosystem.
To this end the presentation will touch on:
-views of the Big Data ecosystem and it’s components
-an example of a Hadoop cluster
-considerations when selecting a Hadoop distribution
-some of the Hadoop distributions available
-a recommended Hadoop distribution
The document discusses when to use Hadoop instead of a relational database management system (RDBMS) for advanced analytics. It provides examples of when queries like count distinct, cursors, and alter table statements become problematic in an RDBMS. It contrasts analyzing simple, transactional data like invoices versus complex, evolving data like customers or website visitors. Hadoop is better suited for problems involving complex objects, self-joins on large datasets, and matching large datasets. The document encourages structuring data in HDFS in a flexible way that fits the problem and use cases like simple counts on complex objects, self-self-self joins, and matching problems.
2015 nov 27_thug_paytm_rt_ingest_brief_finalAdam Muise
The document discusses Paytm Labs' transition from batch data ingestion to real-time data ingestion using Apache Kafka and Confluent. It outlines their current batch-driven pipeline and some of its limitations. Their new approach, called DFAI (Direct-From-App-Ingest), will have applications directly write data to Kafka using provided SDKs. This data will then be streamed and aggregated in real-time using their Fabrica framework to generate views for different use cases. The benefits of real-time ingestion include having fresher data available and a more flexible schema.
Hadoop or Spark: is it an either-or proposition? By Slim BaltagiSlim Baltagi
Hadoop or Spark: is it an either-or proposition? An exodus away from Hadoop to Spark is picking up steam in the news headlines and talks! Away from marketing fluff and politics, this talk analyzes such news and claims from a technical perspective.
In practical ways, while referring to components and tools from both Hadoop and Spark ecosystems, this talk will show that the relationship between Hadoop and Spark is not of an either-or type but can take different forms such as: evolution, transition, integration, alternation and complementarity.
The document discusses big data and Hadoop. It describes the three V's of big data - variety, volume, and velocity. It also discusses Hadoop components like HDFS, MapReduce, Pig, Hive, and YARN. Hadoop is a framework for storing and processing large datasets in a distributed computing environment. It allows for the ability to store and use all types of data at scale using commodity hardware.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
The document proposes an Agile Big Data model to address perceived issues with traditional Hadoop implementations. It discusses the motivation for change and outlines an Agile model with self-organized roles including data stewards, data scientists, project teams, and an architecture board. Key aspects of the proposed model include independent and self-managed project teams, a domain-driven data model, and emphasis on data quality and governance through the involvement of data stewards across domains.
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Proc...Agile Testing Alliance
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Processing by "Sampat Kumar" from "Harman". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
Introduction to Hadoop - The EssentialsFadi Yousuf
This document provides an introduction to Hadoop, including:
- A brief history of Hadoop and how it was created to address limitations of relational databases for big data.
- An overview of core Hadoop concepts like its shared-nothing architecture and using computation near storage.
- Descriptions of HDFS for distributed storage and MapReduce as the original programming framework.
- How the Hadoop ecosystem has grown to include additional frameworks like Hive, Pig, HBase and tools like Sqoop and Zookeeper.
- A discussion of YARN which separates resource management from job scheduling in Hadoop.
Hadoop is being used across organizations for a variety of purposes like data staging, analytics, security monitoring, and manufacturing quality assurance. However, most organizations still have separate systems optimized for specific workloads. Hadoop has the potential to relieve pressure on these systems by handling data staging, archives, transformations, and exploration. Going forward, Hadoop will need to provide enterprise-grade capabilities like high performance, security, data protection, and support for both analytical and operational workloads to fully replace specialized systems and become the main enterprise data platform.
This document discusses leveraging major market opportunities with Microsoft Azure. It notes that worldwide cloud software revenue is expected to grow significantly between 2010-2017. By 2017, nearly $1 of every $5 spent on applications will be consumed via the cloud. It also notes that hybrid cloud deployments will be common for large enterprises by the end of 2017. The document then outlines several major enterprise workloads that can be moved to Azure, including test/development, SharePoint, SQL/business intelligence, application migration, SAP, and identity/Office 365. It provides examples of how partners can help customers with these types of migrations.
Architectural considerations for Hadoop Applicationshadooparchbook
The document discusses architectural considerations for Hadoop applications using a case study on clickstream analysis. It covers requirements for data ingestion, storage, processing, and orchestration. For data storage, it considers HDFS vs HBase, file formats, and compression formats. SequenceFiles are identified as a good choice for raw data storage as they allow for splittable compression.
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
The document discusses integrating Hadoop with relational databases. It describes scenarios where reference data is stored in an RDBMS and used in Hadoop, Hadoop is used for offline analytics on data stored in an RDBMS, and exporting MapReduce outputs to an RDBMS. It then presents a case study on extending SQOOP for optimized Oracle integration and compares performance with and without the extension. Other tools for Hadoop-RDBMS integration are also briefly outlined.
Realtime Analytics with Hadoop and HBaselarsgeorge
The document discusses realtime analytics using Hadoop and HBase. It begins by introducing the speaker and their experience. It then discusses moving from batch processing with Hadoop to more realtime needs, and how systems like HBase can help bridge that gap. Several designs are presented for using HBase and Hadoop together to enable both realtime and batch analytics on large datasets.
This document summarizes Hortonworks' Hadoop distribution called Hortonworks Data Platform (HDP). It discusses how HDP provides a comprehensive data management platform built around Apache Hadoop and YARN. HDP includes tools for storage, processing, security, operations and accessing data through batch, interactive and real-time methods. The document also outlines new capabilities in HDP 2.2 like improved engines for SQL, Spark and streaming and expanded deployment options.
Deep learning has become widespread as frameworks such as TensorFlow and PyTorch have made it easy to onboard machine learning applications. However, while it is easy to start developing with these frameworks on your local developer machine, scaling up a model to run on a cluster and train on huge datasets is still challenging. Code and dependencies have to be copied to every machine and defining the cluster configurations is tedious and error-prone. In addition, troubleshooting errors and aggregating logs is difficult. Ad-hoc solutions also lack resource guarantees, isolation from other jobs, and fault tolerance.
To solve these problems and make scaling deep learning easy, we have made several enhancements to Hadoop and built an open-source deep learning platform called TonY. In this talk, Anthony and Keqiu will discuss new Hadoop features useful for deep learning, such as GPU resource support, and deep dive into TonY, which lets you run deep learning programs natively on Hadoop. We will discuss TonY's architecture and how it allows users to manage their deep learning jobs, acting as a portal from which to launch notebooks, monitor jobs, and visualize training results.
Building a Big Data platform with the Hadoop ecosystemGregg Barrett
This presentation provides a brief insight into a Big Data platform using the Hadoop ecosystem.
To this end the presentation will touch on:
-views of the Big Data ecosystem and it’s components
-an example of a Hadoop cluster
-considerations when selecting a Hadoop distribution
-some of the Hadoop distributions available
-a recommended Hadoop distribution
The document discusses when to use Hadoop instead of a relational database management system (RDBMS) for advanced analytics. It provides examples of when queries like count distinct, cursors, and alter table statements become problematic in an RDBMS. It contrasts analyzing simple, transactional data like invoices versus complex, evolving data like customers or website visitors. Hadoop is better suited for problems involving complex objects, self-joins on large datasets, and matching large datasets. The document encourages structuring data in HDFS in a flexible way that fits the problem and use cases like simple counts on complex objects, self-self-self joins, and matching problems.
2015 nov 27_thug_paytm_rt_ingest_brief_finalAdam Muise
The document discusses Paytm Labs' transition from batch data ingestion to real-time data ingestion using Apache Kafka and Confluent. It outlines their current batch-driven pipeline and some of its limitations. Their new approach, called DFAI (Direct-From-App-Ingest), will have applications directly write data to Kafka using provided SDKs. This data will then be streamed and aggregated in real-time using their Fabrica framework to generate views for different use cases. The benefits of real-time ingestion include having fresher data available and a more flexible schema.
Hadoop or Spark: is it an either-or proposition? By Slim BaltagiSlim Baltagi
Hadoop or Spark: is it an either-or proposition? An exodus away from Hadoop to Spark is picking up steam in the news headlines and talks! Away from marketing fluff and politics, this talk analyzes such news and claims from a technical perspective.
In practical ways, while referring to components and tools from both Hadoop and Spark ecosystems, this talk will show that the relationship between Hadoop and Spark is not of an either-or type but can take different forms such as: evolution, transition, integration, alternation and complementarity.
Hoodie (Hadoop Upsert Delete and Incremental) is an analytical, scan-optimized data storage abstraction which enables applying mutations to data in HDFS on the order of few minutes and chaining of incremental processing in hadoop
Hire Hadoop Developer at 0.99* $ per hour for first 5 hours. visit http://geeksperhour.com/hire-hadoop-developer/ to post your job.
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This document provides an overview of microservice architecture (MSA). It describes the characteristics of MSA, including small, independent services focused on a single business capability. It covers service interaction styles, service discovery, data management challenges in MSA, deployment strategies, and migration from monolithic to MSA. It also discusses event-driven architecture, API gateways, common design patterns, and challenges with MSA.
This document outlines Seth Familian's presentation on working with big data. It discusses key concepts like what constitutes big data, popular tools for working with big data like Splunk and Segment, and techniques for building dashboards and inferring customer segments from large datasets. Specific examples are provided of automated data flows that extract, load, transform and analyze big data from various sources to generate insights and populate customized dashboards.
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.
According to Gartner, big data will drive $232 billion in IT spending through 2016. The benefits to organizations for adding big data to their information management and analytics infrastructure will force a more rapid cycle of replacing existing solutions.
Learn more about:
• Provisioning a Data-intensive Application Cluster (Hadoop or Spark) on top of OpenStack.
• Building an Architecture combining the Hadoop and OpenStack Ecosystems.
• Build OpenStack Cloud and implement Big Data Architectures with comparative benefits of other Architectures.
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.
Hadoop is an open-source framework for storing and processing large datasets across commodity hardware. It is well-suited for big data applications involving high volume, velocity, and unstructured data. Hadoop can be used for business intelligence by extracting, transforming, and loading operational data into data warehouses and performing analytics using tools like Hive, HBase, Sqoop, R, SAS and Matlab. Many BI tools now support Hadoop by allowing users to connect to, import/export data from, and perform predictive analytics on Hadoop clusters. This allows organizations to leverage Hadoop for BI applications involving large, diverse datasets.
El Valor de construir First Party Data Orgánico a través del Ecosistema Digit...Esther Checa
Principales retos de una estrategia de posicionamiento natural (SEO): estar en las diferente etapas del viaje del consumidor y aprovechar las audiencias que se generan dentro de los activos digitales #Innobi2017
Lecture given at the University of Catania on December 2nd, 2014.
Start from Big Data definitions, continue with real life examples of successful Big Data Projects, go a little bit deeper with Sentiment Analysis, and conclude with a brief overview of Big Data tools and Big Data with Microsoft.
Summary:
1. What is Big Data? (includes the 5Vs of Big Data)
2. Big Data Examples (includes 6 Real Life Examples and comments on Privacy concerns)
3. How to Tackle a Big Data Problem (my 4 Universal Steps to follow)
4. Sentiment Analysis (what is sentiment analysis? Why do we care? A Technique and a plan)
5. Big Data tools (Hadoop, Hadoop Ecosystem, Hive, Pig, Sqoop, Oozie; Azure HDInsight, Excel Power Query, Power Pivot, Power View, Power Map)
Part 1 - Data Warehousing Lecture at BW Cooperative State University (DHBW)Andreas Buckenhofer
The document provides an introduction to a lecture on data warehousing and data warehouse architecture given by Andreas Buckenhofer from Daimler TSS, including information about the lecturer, the structure and topics to be covered in the lecture, as well as employment opportunities in data warehousing. The lecture aims to help participants understand data warehousing concepts like architectures, data modeling, ETL processes, and trends in the industry.
Accelerating Hadoop, Spark, and Memcached with HPC Technologiesinside-BigData.com
DK Panda from Ohio State University presented this deck at the OpenFabrics Workshop.
"Modern HPC clusters are having many advanced features, such as multi-/many-core architectures, highperformance RDMA-enabled interconnects, SSD-based storage devices, burst-buffers and parallel file systems. However, current generation Big Data processing middleware (such as Hadoop, Spark, and Memcached) have not fully exploited the benefits of the advanced features on modern HPC clusters. This talk will present RDMA-based designs using OpenFabrics Verbs and heterogeneous storage architectures to accelerate multiple components of Hadoop (HDFS, MapReduce, RPC, and HBase), Spark and Memcached. An overview of the associated RDMA-enabled software libraries (being designed and publicly distributed as a part of the HiBD project for Apache Hadoop (integrated and plug-ins for Apache, HDP, and Cloudera distributions), Apache Spark and Memcached will be presented. The talk will also address the need for designing benchmarks using a multi-layered and systematic approach, which can be used to evaluate the performance of these Big Data processing middleware."
Watch the video presentation: http://wp.me/p3RLHQ-gzg
Learn more: http://hibd.cse.ohio-state.edu/
and
https://www.openfabrics.org/index.php/abstracts-agenda.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Working with thousands, millions, or billions of data records in high dimensions is increasingly becoming the reality for scientific research. What are some techniques to make this kind of data volume tractable? How can parallel computing help? In this talk I'll review data management tools and infrastructures, languages, and paradigms that help in this regard. In particular, I'll discuss Hadoop, MapReduce, Python, NumPy, and Globus Online to provide a survey of ways in which researchers can manage their data and process it in parallel.
Part 1: Cloudera’s Analytic Database: BI & SQL Analytics in a Hybrid Cloud WorldCloudera, Inc.
3 Things to Learn About:
* On-premises versus the cloud: What’s the same and what’s different?
* Design and benefits of analytics in the cloud
* Best practices and architectural considerations
This document provides an overview of big data and Hadoop. It discusses what big data is, why it has become important recently, and common use cases. It then describes how Hadoop addresses challenges of processing large datasets by distributing data and computation across clusters. The core Hadoop components of HDFS for storage and MapReduce for processing are explained. Example MapReduce jobs like wordcount are shown. Finally, higher-level tools like Hive and Pig that provide SQL-like interfaces are introduced.
This document discusses big data and Hadoop. It provides an overview of Hadoop, including what it is, how it works, and its core components like HDFS and MapReduce. It also discusses what Hadoop is good for, such as processing large datasets, and what it is not as good for, like low-latency queries or transactional systems. Finally, it covers some best practices for implementing Hadoop, such as infrastructure design and performance considerations.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It has four main modules - Hadoop Common, HDFS, YARN and MapReduce. HDFS provides a distributed file system that stores data reliably across commodity hardware. MapReduce is a programming model used to process large amounts of data in parallel. Hadoop architecture uses a master-slave model, with a NameNode master and DataNode slaves. It provides fault tolerance, high throughput access to application data and scales to thousands of machines.
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.
- Data is a precious resource that can last longer than the systems themselves (Tim Berners-Lee)
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It provides reliability, scalability and flexibility.
- Hadoop consists of HDFS for storage and MapReduce for processing. The main nodes include NameNode, DataNodes, JobTracker and TaskTrackers. Tools like Hive, Pig, HBase extend its capabilities for SQL-like queries, data flows and NoSQL access.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
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
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.
Apache hadoop, hdfs and map reduce OverviewNisanth Simon
This document provides an overview of Apache Hadoop, HDFS, and MapReduce. It describes how Hadoop uses a distributed file system (HDFS) to store large amounts of data across commodity hardware. It also explains how MapReduce allows distributed processing of that data by allocating map and reduce tasks across nodes. Key components discussed include the HDFS architecture with NameNodes and DataNodes, data replication for fault tolerance, and how the MapReduce engine works with a JobTracker and TaskTrackers to parallelize jobs.
The document provides an introduction to Hadoop and big data concepts. It discusses key topics like what big data is characterized by the three V's of volume, velocity and variety. It then defines Hadoop as a framework for distributed storage and processing of large datasets using commodity hardware. The rest of the document outlines the main components of the Hadoop ecosystem including HDFS, YARN, MapReduce, Hive, Pig, Zookeeper, Flume and Sqoop and provides brief descriptions of each.
This document provides an overview of Hadoop, its core components HDFS and MapReduce, and how they work. It discusses that Hadoop is an open-source framework used for storing and processing huge datasets across clusters of commodity hardware. The two core concepts of Hadoop are HDFS for distributed storage and MapReduce for distributed processing. HDFS provides reliable storage with replication and MapReduce allows processing of large datasets in parallel by dividing work across nodes and integrating results.
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Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses problems with traditional systems like data growth, network/server failures, and high costs by allowing data to be stored in a distributed manner and processed in parallel. Hadoop has two main components - the Hadoop Distributed File System (HDFS) which provides high-throughput access to application data across servers, and the MapReduce programming model which processes large amounts of data in parallel by splitting work into map and reduce tasks.
The document provides an overview of Apache Hadoop and how it addresses challenges related to big data. It discusses how Hadoop uses HDFS to distribute and store large datasets across clusters of commodity servers and uses MapReduce as a programming model to process and analyze the data in parallel. The core components of Hadoop - HDFS for storage and MapReduce for processing - allow it to efficiently handle large volumes and varieties of data across distributed systems in a fault-tolerant manner. Major companies have adopted Hadoop to derive insights from their big data.
HDFS allows storing large amounts of data across multiple machines by splitting files into blocks and replicating those blocks for reliability. It addresses challenges of big data like volume, velocity, and variety by providing a distributed storage solution that scales horizontally. Traditional systems are limited by network bandwidth, storage capacity of individual machines, and single points of failure. HDFS introduces a scalable architecture with a master NameNode and slave DataNodes that stores data blocks, addressing these issues through data distribution and fault tolerance.
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2. • Big Data Fundamentals1
• Hadoop and
Components2
• QA3
Today’s Overview
3. Agenda – Big Data Fundamental
• What is Big Data ?
• Basic Characteristics
of Big Data
• Sources of Big Data
• V’s of Big Data
• Processing Of Data
– Traditional Approach
VS Big Data Approach
5. What is Big Data –con’t
• Basically Big Data is nothing but collection of
large set of Data that not able to processed
using traditional approach and also its
contains the followings
– Structured Data- Traditional Data
– Semi Structure Data- XML
– Unstructured Data – Image/PDF/Media and etc
8. Hadoop Fundamental
• What is Hadoop ?
• Key Characterstics
• Components
• HDFS
• MapReduce
• Yarn
• Benefits of Hadoop
9.
10. What is Hadoop
• Hadoop is an open-source software
framework for storing large amounts of data
and processing/querying those data on a
cluster with multiple nodes of commodity
hardware (i.e. low cost hardware).
12. Components
• Common Libraries
• High Volume of Distributed Data Storage
System –HDFS
• High Volume of Distributed Data Processing
Framework –MapReduce
• Resource and Meta Data Management -YARN
13.
14. – HDFS
• What is HDFS?
• Architecture
• Components
• Basic Features
15. What is HDFS ?
HDFS holds very large amount of data and
provides easier access. To store such huge data,
the files are stored across multiple machines.
These files are stored in redundant fashion to
rescue the system from possible data losses in
case of failure. HDFS also makes applications
available to parallel processing
16.
17. Components- HDFS
Master/slave architecture
HDFS cluster consists of a single Namenode, a
master server that manages the file system
namespace and regulates access to files by
clients.
There are a number of DataNodes usually one
per node in a cluster.
The DataNodes manage storage attached to the
nodes that they run on.
18. Components -HDFS
HDFS exposes a file system namespace and
allows user data to be stored in files.
A file is split into one or more blocks and set
of blocks are stored in DataNodes.
DataNodes: serves read, write requests,
performs block creation, deletion, and
replication upon instruction from Namenode
19. Features
• Highly fault-tolerant
• High throughput
• Suitable Distributed Storage for large
Amount of Data
• Streaming access to file system data
• Can be built out of commodity hardware
21. What is MapReduce
• Its framework mainly used to process the
large Amount of Data in parallel on the large
clusters of commodity hardware
• Its based on divide –conquer Principle which
provides built-in fault tolerance and
redundancy
• Its batch oriented parallel processing engine
to process the large volume of data
22. MapReduce
– Map stage : The map or mapper’s job is to process
the input data. Generally the input data is in the
form of file or directory and is stored in the Hadoop
file system (HDFS). The input file is passed to the
mapper function line by line. The mapper processes
the data and creates several small chunks of data.
– Reduce stage : This stage is the combination of the
Shuffle stage and the Reduce stage. The Reducer’s
job is to process the data that comes from the
mapper. After processing, it produces a new set of
output, which will be stored in the HDFS.
23. Stages of each Tasks
• Map Task have the following Stages
– Map
– Combine
– Partition
• Reduce Task have the following stages
– Shuffle and Sort
– Reduce
24. Demo
• Refer the PDF Attachment
• Mainly for reading the text and count the no
of word
25. – YARN
• What is YARN?
• Architecture and
Components
26. YARN
• YARN (Yet Another Resource Nagotiator): A
framework for job scheduling and cluster
resource management
27.
28. – Hive
• What is Hive?
• Architecture of Hive
• Flow in Hive
• Data Types
• Sample Query
• Not Hive
• Demo
29. What is Hive
• Its Data warehouse infrastructure tool to
process the structured data in Hadoop
platform
• Its originally developed by Facebook then
moves into apache umbrella
• Basic large volume of data is retrieve from
multiple resources and RDBMS system could
not fit as perfect solutions .We move into
Hive.
30. What is Hive
• Its Query Engine wrapper on top of the Hadoop
to perform the OLAP
• Provides the HiveQL is similar to SQL
• Targeted to the users/developer with SQL
background
• Its stores schema in database and process the
data in HDFS
• Data Stored in HDFS/HBASE and every tables
should reference to the file on HDFS/HBASE
31. Architecture - Hive
• Components
– User Interface- Infrastructure tool used to interaction
between user and HDFS/HBASE
– Meta Store – Used to store Schema/tables and etc,
Mainly used to store the meta data information
– SerDe- libraries used to Serialize/Deserialize for their
own data format. Read and Writes the rows from/in
the tables
– Query Processor -
33. Data Type
• Integral Type
• SmallInt,BigInt,TinyInt,INT
• Float Type
– Double,Decimal
• String Type
– Char , Varchar
• Misc Type
– Boolean ,Binary
• TimeStamp,Dates,Decimal
• Complex Type
– Struct,Map,Arrays
34. Sample Query
• Create Table
• Drop Table
• Alter Table
• Rename Table- Rename the table name
• Load Data –Insert
• Create View
• Select
35. Operator and Built in Function
• Arithmetic Operator
• Relational Operator
• Logical Operator
• Aggregate and Built in Function
• Supports Index/Order/Join
36. Disadvantages of HIVE
• Not for Real time Query
• Supports ACID from 0.14 version onwards
• Poor performance – It took more time to
process since each time Hive will
generate/process the Map Reduce or Spark
Program internally while processing the
Records sets
37. Disadvantages of HIVE
• It can process only for large volume of
Structured data not for other categories
41. CAP
• CAP Theorem
– Consistency
• Read the data from all the notes always consistent
– Availability
• Read/write always acknowledge either success or failure
– Partition Tolerance
• It can tolerate communication outage that spit the cluster
into multiple silos /data set
Distributed Data System only provides the any two of
the above properties
Distributed Data Storage based on the above theorem
43. BASE
• BASE
– Basic availability
– Soft state
– Eventual consistency
Above property mainly used in database
distributed data for non transactional data
44. SCV
• SCV
– Speed
– Consistency
– Volume
High Data Volume Data Processing is based on the
above algorithm
Data Processing should satisfied at max of two of
the above properties
45. Sharding
• Sharding
It’s the process of Horizontally partitioning of large
volume of data into smaller set of more
manageable data set
46. Replication
• Replication
Stores the multiple copies of the data set known as
replicas
Provides always high availability , scalability and
fault tolerance since its stores into multiple nodes
Replicas implements the following was
Master-slave
Peer -Peer
50. HDFS
• Blocks
– In HDFS File can split into small segments which
used to store the Data .Each Segments called as
Block
– Default size of the Block is 64 MB (Hadoop 1.X) ,
you can change the size in HDFS Configuration
upto 128 MB(Hadoop 2.x Advisable approach)
51. Types of File Format -MR
• TxtInputFormat-- Default
• KeyValueTxtInputFormat
• SequenceFileInputFormat
• SequenceAsFileTxtInputFormat
52. Reader and Writer
• RecordReader –
– Read the Record from file line by line , Each line
in the file treat as a record
– Perform before the Mapper function
• RecordWriter
–Write content into file as a output
– Perform after the Reducer
54. BoxClasses in MR
• Its equivalent to wrapper in JAVA
• IntWritter
• FloatWritter
• LongWritter
• DoubleWritter
• TextWritter
• Mainly used for (K,V) in MR
55. Schema on Read/Write
• Hadoop –Schema on Read approach
• RDBMS – Schema on Write approach
56. Key Steps in Big Data Solution
• Ingesting Data
• Storing Data
• Processing Data
58. Hadoop Tools
• 15+ frameworks & tools like Sqoop, Flume,
Kafka, Pig, Hive, Spark, Impala, etc to ingest data
into HDFS, store and process data within HDFS,
and to query data from HDFS for business
intelligence & analytics. Some tools like Pig &
Hive are abstraction layers on top of
MapReduce, whilst the other tools like Spark &
Impala are improved architecture/design from
MapReduce for much improved latencies to
support near real-time (i.e. NRT) & real-time
processing.
59. NRT
• Near Real time –
– Near real-time processing is when speed is
important, but processing time in minutes is
acceptable in lieu of seconds
60. HeartBit - HDFS
• Heartbeat is referred to a signal used
between a data node and Name node, and
between task tracker and job tracker
61. MapReducer – Partition
• all the value of a single key goes to the same
reducer from Mapper, eventually which helps
evenly distribution of the map output over
the reducers
62. HDFS VS NAS(Network Attached
Storage)
• HDFS data blocks are distributed across local
drives of all machines in a cluster
• NAS data is stored on dedicated hardware.
• HDFS there is data redundancy because of
the replication protocol.
• NAS there is no probability of data
redundancy
63. Commodity Hardware
• Commodity Hardware refers to inexpensive
systems that do not have high availability or
high quality. Commodity Hardware consists
of RAM because there are specific services
that need to be executed on RAM
65. Combine-MapReduce
• A “Combiner” is a mini “reducer” that
performs the local “reduce” task. It receives
the input from the “mapper” on a particular
“node” and sends the output to the
“reducer”. “Combiners” help in enhancing
the efficiency of “MapReduce” by reducing
the quantum of data that is required to be
sent to the “reducers”.
67. JobTracker –Functionality
– When Client applications submit map reduce jobs to the Job tracker. The
JobTracker talks to the Name node to determine the location of the data.
– The JobTracker locates Tasktracker nodes with available slots at or near the
data
– The JobTracker submits the work to the chosen Tasktracker nodes.
– The TaskTracker nodes are monitored. If they do not submit heartbeat
signals often enough, they are deemed to have failed and the work is
scheduled on a different TaskTracker.
– When the work is completed, the JobTracker updates its status.
– Client applications can poll the JobTracker for information.
69. Hive Support File Format
• Text File (Plain raw data)
• Sequence File(Key value pairs)
• RCFile (Record Columnar files which are
stored columns of the table in columnar
Database)
70. NameNode Vs MetaNode
• NameNode- Stores the MetaData information
about the files in Hadoop
• MetaNode-Stores the MetaData information
about the Tables /Data Base in Hive
71. Tez- Hive
• execute complex directed acyclic graphs of
general data processing tasks
• Its better than the MapReduce
72. Bucketing -Hive
• Bucketing provides mechanism to query and
examine random samples of data.
• Bucketing offers capability to execute queries
on a sub-set of random data