This document discusses three use cases for Hadoop: extract, transform, and load (ETL); file system access; and recommendations. It describes how Hadoop, through tools like Flume, HDFS, Pig, Sqoop, and FUSE-DFS, provides a scalable and flexible platform for ETL processes compared to traditional approaches. It also explains how Hadoop can be used to store log and customer data for generating recommendations.
The report discusses the key components and objectives of HDFS, including data replication for fault tolerance, HDFS architecture with a NameNode and DataNodes, and HDFS properties like large data sets, write once read many model, and commodity hardware. It provides an overview of HDFS and its design to reliably store and retrieve large volumes of distributed data.
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
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.
The document discusses and compares MapReduce and relational database management systems (RDBMS) for large-scale data processing. It describes several hybrid approaches that attempt to combine the scalability of MapReduce with the query optimization and efficiency of parallel RDBMS. HadoopDB is highlighted as a system that uses Hadoop for communication and data distribution across nodes running PostgreSQL for query execution. Performance evaluations show hybrid systems can outperform pure MapReduce but may still lag specialized parallel databases.
This document provides an overview of a SQL-on-Hadoop tutorial. It introduces the presenters and discusses why SQL is important for Hadoop, as MapReduce is not optimal for all use cases. It also notes that while the database community knows how to efficiently process data, SQL-on-Hadoop systems face challenges due to the limitations of running on top of HDFS and Hadoop ecosystems. The tutorial outline covers SQL-on-Hadoop technologies like storage formats, runtime engines, and query optimization.
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 provides an overview and comparison of RDBMS, Hadoop, and Spark. It introduces RDBMS and describes its use cases such as online transaction processing and data warehouses. It then introduces Hadoop and describes its ecosystem including HDFS, YARN, MapReduce, and related sub-modules. Common use cases for Hadoop are also outlined. Spark is then introduced along with its modules like Spark Core, SQL, and MLlib. Use cases for Spark include data enrichment, trigger event detection, and machine learning. The document concludes by comparing RDBMS and Hadoop, as well as Hadoop and Spark, and addressing common misconceptions about Hadoop and Spark.
The report discusses the key components and objectives of HDFS, including data replication for fault tolerance, HDFS architecture with a NameNode and DataNodes, and HDFS properties like large data sets, write once read many model, and commodity hardware. It provides an overview of HDFS and its design to reliably store and retrieve large volumes of distributed data.
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
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.
The document discusses and compares MapReduce and relational database management systems (RDBMS) for large-scale data processing. It describes several hybrid approaches that attempt to combine the scalability of MapReduce with the query optimization and efficiency of parallel RDBMS. HadoopDB is highlighted as a system that uses Hadoop for communication and data distribution across nodes running PostgreSQL for query execution. Performance evaluations show hybrid systems can outperform pure MapReduce but may still lag specialized parallel databases.
This document provides an overview of a SQL-on-Hadoop tutorial. It introduces the presenters and discusses why SQL is important for Hadoop, as MapReduce is not optimal for all use cases. It also notes that while the database community knows how to efficiently process data, SQL-on-Hadoop systems face challenges due to the limitations of running on top of HDFS and Hadoop ecosystems. The tutorial outline covers SQL-on-Hadoop technologies like storage formats, runtime engines, and query optimization.
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 provides an overview and comparison of RDBMS, Hadoop, and Spark. It introduces RDBMS and describes its use cases such as online transaction processing and data warehouses. It then introduces Hadoop and describes its ecosystem including HDFS, YARN, MapReduce, and related sub-modules. Common use cases for Hadoop are also outlined. Spark is then introduced along with its modules like Spark Core, SQL, and MLlib. Use cases for Spark include data enrichment, trigger event detection, and machine learning. The document concludes by comparing RDBMS and Hadoop, as well as Hadoop and Spark, and addressing common misconceptions about Hadoop and Spark.
Hadoop vs Spark | Which One to Choose? | Hadoop Training | Spark Training | E...Edureka!
( Apache Spark Training: https://www.edureka.co/apache-spark-scala-training )
( Hadoop Training: https://www.edureka.co/hadoop )
This Edureka Hadoop vs Spark video will help you to understand the differences between Hadoop and Spark. We will be comparing them on various parameters. We will be taking a broader look at:
1. Introduction to Hadoop
2. Introduction to Apache Spark
3. Spark vs Hadoop -
Performance
Ease of Use
Cost
Data Processing
Fault tolerance
Security
4. Hadoop Use-cases
5. Spark Use-cases
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
The proliferation of different database systems has led to data silos and inconsistencies. In the past, there was a single data warehouse but now there are many types of databases optimized for different purposes like transactions, analytics, streaming, etc. This can be addressed by having a common platform like Hadoop that supports different database types to reduce silos and enable data integration. However, more integration tools are still needed to fully realize this vision.
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.
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.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
The document provides an overview of Apache Hadoop and related big data technologies. It discusses Hadoop components like HDFS for storage, MapReduce for processing, and HBase for columnar storage. It also covers related projects like Hive for SQL queries, ZooKeeper for coordination, and Hortonworks and Cloudera distributions.
Big data architecture on cloud computing infrastructuredatastack
This document provides an overview of using OpenStack and Sahara to implement a big data architecture on cloud infrastructure. It discusses:
- The characteristics and service models of cloud computing
- An introduction to OpenStack, why it is used, and some of its key statistics
- What Sahara is and its role in provisioning and managing Hadoop, Spark, and Storm clusters on OpenStack
- Sahara's architecture, how it integrates with OpenStack, and examples of how it can be used to quickly provision data processing clusters and execute analytic jobs on cloud infrastructure.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.
Hadoop 2.0 architecture uses a scale-out storage and distributed processing framework. It stores large datasets across commodity hardware clusters and allows for processing using a simple programming model. The architecture utilizes HDFS for storage which splits files into 128MB blocks, stores replicas across racks for fault tolerance, and is managed by a resource manager and node managers that track hardware resources and heartbeats.
Apache hadoop introduction and architectureHarikrishnan K
Hadoop is an open-source 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 is a storage part known as Hadoop Distributed File System (HDFS) and a processing part known as MapReduce. HDFS provides distributed storage and MapReduce enables distributed processing of large datasets in a reliable, fault-tolerant and scalable manner. Hadoop has become popular for distributed computing as it is reliable, economical and scalable to handle large and varying amounts of data.
This document provides an overview of Hadoop, an open source framework for distributed storage and processing of large datasets. It discusses what Hadoop is, its applications and architecture, advantages like scalability and fault tolerance, and disadvantages such as security concerns. The document also outlines when Hadoop should be used, such as for large datasets that don't fit on a single machine or for extracting, transforming and loading large amounts of data. Key components of Hadoop include MapReduce, HDFS, YARN and its wider ecosystem of related projects.
SQL on Hadoop
Looking for the correct tool for your SQL-on-Hadoop use case?
There is a long list of alternatives to choose from; how to select the correct tool?
The tool selection is always based on use case requirements.
Read more on alternatives and our recommendations.
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
This document provides an overview of big data processing tools and NoSQL databases. It discusses how Hadoop uses MapReduce and HDFS to distribute processing across large clusters. Spark is presented as an alternative to Hadoop. The CAP theorem is explained as relating to consistency, availability, and network partitions. Different types of NoSQL databases are described including key-value, column, document and graph databases. Examples are provided for each type.
This document discusses Wordcount examples in MapReduce, Cascading, and Scalding. Scalding is a Scala wrapper for Cascading that allows working with data like in-memory collections. It includes parsers for CSV and date formats, as well as helper algorithms. The document also provides instructions for building Scalding jobs, deploying them on EMR, and some tips for development including increasing memory limits and reading data directly from HDFS. Resources with more documentation and examples are also listed.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
This document describes a Hadoop project to find adjusted closing stock prices when dividends are not reported. It involves reading data from two CSV files - one with dividend information and one with daily stock prices. The architecture uses a mapper to parse the input data and a reducer to retrieve the adjusted closing price by matching dates when dividends are zero. Pseudocode is provided for the mapper and reducer. The business implication is that adjusted closing prices provide a more accurate reflection of a stock's value over time compared to raw closing prices.
This presentation is based on a project for installing Apache Hadoop on a single node cluster along with Apache Hive for processing of structured data.
Hadoop vs Spark | Which One to Choose? | Hadoop Training | Spark Training | E...Edureka!
( Apache Spark Training: https://www.edureka.co/apache-spark-scala-training )
( Hadoop Training: https://www.edureka.co/hadoop )
This Edureka Hadoop vs Spark video will help you to understand the differences between Hadoop and Spark. We will be comparing them on various parameters. We will be taking a broader look at:
1. Introduction to Hadoop
2. Introduction to Apache Spark
3. Spark vs Hadoop -
Performance
Ease of Use
Cost
Data Processing
Fault tolerance
Security
4. Hadoop Use-cases
5. Spark Use-cases
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
The proliferation of different database systems has led to data silos and inconsistencies. In the past, there was a single data warehouse but now there are many types of databases optimized for different purposes like transactions, analytics, streaming, etc. This can be addressed by having a common platform like Hadoop that supports different database types to reduce silos and enable data integration. However, more integration tools are still needed to fully realize this vision.
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.
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.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
The document provides an overview of Apache Hadoop and related big data technologies. It discusses Hadoop components like HDFS for storage, MapReduce for processing, and HBase for columnar storage. It also covers related projects like Hive for SQL queries, ZooKeeper for coordination, and Hortonworks and Cloudera distributions.
Big data architecture on cloud computing infrastructuredatastack
This document provides an overview of using OpenStack and Sahara to implement a big data architecture on cloud infrastructure. It discusses:
- The characteristics and service models of cloud computing
- An introduction to OpenStack, why it is used, and some of its key statistics
- What Sahara is and its role in provisioning and managing Hadoop, Spark, and Storm clusters on OpenStack
- Sahara's architecture, how it integrates with OpenStack, and examples of how it can be used to quickly provision data processing clusters and execute analytic jobs on cloud infrastructure.
The document summarizes a technical seminar on Hadoop. It discusses Hadoop's history and origin, how it was developed from Google's distributed systems, and how it provides an open-source framework for distributed storage and processing of large datasets. It also summarizes key aspects of Hadoop including HDFS, MapReduce, HBase, Pig, Hive and YARN, and how they address challenges of big data analytics. The seminar provides an overview of Hadoop's architecture and ecosystem and how it can effectively process large datasets measured in petabytes.
The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.
Hadoop 2.0 architecture uses a scale-out storage and distributed processing framework. It stores large datasets across commodity hardware clusters and allows for processing using a simple programming model. The architecture utilizes HDFS for storage which splits files into 128MB blocks, stores replicas across racks for fault tolerance, and is managed by a resource manager and node managers that track hardware resources and heartbeats.
Apache hadoop introduction and architectureHarikrishnan K
Hadoop is an open-source 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 is a storage part known as Hadoop Distributed File System (HDFS) and a processing part known as MapReduce. HDFS provides distributed storage and MapReduce enables distributed processing of large datasets in a reliable, fault-tolerant and scalable manner. Hadoop has become popular for distributed computing as it is reliable, economical and scalable to handle large and varying amounts of data.
This document provides an overview of Hadoop, an open source framework for distributed storage and processing of large datasets. It discusses what Hadoop is, its applications and architecture, advantages like scalability and fault tolerance, and disadvantages such as security concerns. The document also outlines when Hadoop should be used, such as for large datasets that don't fit on a single machine or for extracting, transforming and loading large amounts of data. Key components of Hadoop include MapReduce, HDFS, YARN and its wider ecosystem of related projects.
SQL on Hadoop
Looking for the correct tool for your SQL-on-Hadoop use case?
There is a long list of alternatives to choose from; how to select the correct tool?
The tool selection is always based on use case requirements.
Read more on alternatives and our recommendations.
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
This document provides an overview of big data processing tools and NoSQL databases. It discusses how Hadoop uses MapReduce and HDFS to distribute processing across large clusters. Spark is presented as an alternative to Hadoop. The CAP theorem is explained as relating to consistency, availability, and network partitions. Different types of NoSQL databases are described including key-value, column, document and graph databases. Examples are provided for each type.
This document discusses Wordcount examples in MapReduce, Cascading, and Scalding. Scalding is a Scala wrapper for Cascading that allows working with data like in-memory collections. It includes parsers for CSV and date formats, as well as helper algorithms. The document also provides instructions for building Scalding jobs, deploying them on EMR, and some tips for development including increasing memory limits and reading data directly from HDFS. Resources with more documentation and examples are also listed.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
This document describes a Hadoop project to find adjusted closing stock prices when dividends are not reported. It involves reading data from two CSV files - one with dividend information and one with daily stock prices. The architecture uses a mapper to parse the input data and a reducer to retrieve the adjusted closing price by matching dates when dividends are zero. Pseudocode is provided for the mapper and reducer. The business implication is that adjusted closing prices provide a more accurate reflection of a stock's value over time compared to raw closing prices.
This presentation is based on a project for installing Apache Hadoop on a single node cluster along with Apache Hive for processing of structured data.
The document outlines the key steps in an online training program for Hadoop including setting up a virtual Hadoop cluster, loading and parsing payment data from XML files into databases incrementally using scheduling, building a migration flow from databases into Hadoop and Hive, running Hive queries and exporting data back to databases, and visualizing output data in reports. The training will be delivered online over 20 hours using tools like GoToMeeting.
Bigdata Hadoop project payment gateway domainKamal A
Live Hadoop project in payment gateway domain for people seeking real time work experience in bigdata domain. Email: Onlinetraining2011@gmail.com ,
Skypeid: onlinetraining2011
My profile: www.linkedin.com/pub/kamal-a/65/2b2/2b5
An example of a successful proof of conceptETLSolutions
In this presentation we explain how to create a successful proof of concept for software, using a real example from our work in the Oil & Gas industry.
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
Hadoop World 2011: How Hadoop Revolutionized Business Intelligence and Advanc...Cloudera, Inc.
"Amr Awadallah served as the VP of Engineering of Yahoo's Product
Intelligence Engineering (PIE) team for a number of years. The PIE
team was responsible for business intelligence and advanced data
analytics across a number of Yahoo's key consumer facing properties (search, mail, news, finance, sports, etc). Amr will share the data architecture that PIE had implementted before Hadoop was deployed and the headaches that architecture entailed. Amr will then show how most, if not all of these headaches were eliminated once Hadoop was deployed. Amr will illustrate how Hadoop and Relational Database complement each other within the traditional business intelligence data stack, and how that enables organizations to access all their data under different
operational and economic constraints."
How Apache Hadoop is Revolutionizing Business Intelligence and Data Analytics...Amr Awadallah
Apache Hadoop is revolutionizing business intelligence and data analytics by providing a scalable and fault-tolerant distributed system for data storage and processing. It allows businesses to explore raw data at scale, perform complex analytics, and keep data alive for long-term analysis. Hadoop provides agility through flexible schemas and the ability to store any data and run any analysis. It offers scalability from terabytes to petabytes and consolidation by enabling data sharing across silos.
Контроль зверей: инструменты для управления и мониторинга распределенных сист...yaevents
Александр Козлов, Cloudera Inc.
Александр Козлов, старший архитектор в Cloudera Inc., работает с большими компаниями, многие из которых находятся в рейтинге Fortune 500, над проектами по созданию систем анализа большого количества данных. Закончил аспирантуру физического факультета Московского государственного университета, после чего также получил степень Ph.D. в Стэнфорде. До Cloudera и после окончания учебы работал над статистическим анализом данных и соответствующими компьютерными технологиями в SGI, Hewlett-Packard, а также стартапе Turn.
Тема доклада
Контроль зверей: инструменты для управления и мониторинга распределенных систем от Cloudera.
Тезисы
Поддержание распределенных систем, состоящих из тысяч компьютеров, является сложной задачей. Компания Cloudera, которая специализируется на создании распределенных технологий, разработала набор средств для централизованного управления распределенных Hadoop/HBase кластеров. Hadoop и HBase являются проектами Apache Software Foundation, и их применение для анализа частично структурированных данных ускоряется во всем мире. В этом докладе будет рассказано о SCM, системе для конфигурации, настройки, и управления Hadoop/HBase и Activity Monitor, системе для мониторинга ряда ОС и Hadoop/HBase метрик, а также об особенностях подхода Cloudera в отличие от существующих решений для мониторинга (Tivoli, xCat, Ganglia, Nagios и т.д.).
The document discusses how Hadoop can help solve data and analytics problems at Yahoo before and after adopting Hadoop. It summarizes that before Hadoop, Yahoo had issues with limited ETL windows, inability to reprocess data for errors, loss of data granularity, inability to query raw data or have a consolidated data repository. After adopting Hadoop, Yahoo was able to do more advanced analytics and data exploration on their large amounts of raw data stored in Hadoop.
Hadoop in the Enterprise - Dr. Amr Awadallah @ Microstrategy World 2011Cloudera, Inc.
- Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware.
- Cloudera's Data Operating System (CDH) is an enterprise-grade distribution of Apache Hadoop that includes additional components for management, security, and integration with existing systems.
- CDH enables enterprises to leverage Hadoop for data agility, consolidation of structured and unstructured data sources, complex data processing using various programming languages, and economical storage of data regardless of type or size.
Houston Hadoop Meetup Presentation by Vikram Oberoi of ClouderaMark Kerzner
The document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It describes Hadoop's core components - the Hadoop Distributed File System (HDFS) for scalable data storage, and MapReduce for distributed processing of large datasets in parallel. Typical problems suited for Hadoop involve complex data from multiple sources that need to be consolidated, stored inexpensively at scale, and processed in parallel across the cluster.
Learn how Cloudera Impala empowers you to:
- Perform interactive, real-time analysis directly on source data stored in Hadoop
- Interact with data in HDFS and HBase at the “speed of thought”
- Reduce data movement between systems & eliminate double storage
Flume NG is a tool for collecting and moving large amounts of log data from distributed servers to a Hadoop cluster. It uses agents that collect data through sources like netcat, store data temporarily in channels like memory, and then write data to sinks like HDFS. Flume provides reliable data transport through its use of transactions and flexible configuration through sources, channels, and sinks.
The document discusses how Hadoop can be used for interactive and real-time data analysis. It notes that the amount of digital data is growing exponentially and will reach 40 zettabytes by 2020. Traditional data systems are struggling to manage this new data. Hadoop provides a solution by tying together inexpensive servers to act as one large computer for processing big data using various Apache projects for data access, governance, security and operations. Examples show how Hadoop can be used to analyze real-time streaming data from sensors on trucks to monitor routes, vehicles and drivers.
The document provides an introduction to Hadoop concepts including the core projects within Hadoop and how they fit together. It discusses common use cases for Hadoop across different industries and provides examples of how Hadoop can be used for tasks like social network analysis, content optimization, network analytics, and more. The document also summarizes key Hadoop concepts including HDFS, MapReduce, Pig, Hive, HBase and gives examples of how Hadoop can be applied in domains like financial services, science, energy and others.
Hive provides a SQL-like interface to query large datasets stored in Hadoop. Pig is a dataflow language for transforming datasets. HBase is a distributed, scalable, big data store that provides random real-time read/write access to 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.
Dancing Elephants - Efficiently Working with Object Stores from Apache Spark ...DataWorks Summit
This document discusses challenges and solutions for using object storage with Apache Spark and Hive. It covers:
- Eventual consistency issues in object storage and lack of atomic operations
- Improving performance of object storage connectors through caching, optimized metadata operations, and consistency guarantees
- Techniques like S3Guard and committers that address consistency and correctness problems with output commits in object storage
Webinar: Productionizing Hadoop: Lessons Learned - 20101208Cloudera, Inc.
Key insights in installing, configuring, and running Hadoop and Cloudera's Distribution for Hadoop in production. These are lessons learned from Cloudera helping organizations move to a productions state with Hadoop.
Oracle Unified Information Architeture + Analytics by ExampleHarald Erb
Der Vortrag gibt zunächst einen Architektur-Überblick zu den UIA-Komponenten und deren Zusammenspiel. Anhand eines Use Cases wird vorgestellt, wie im "UIA Data Reservoir" einerseits kostengünstig aktuelle Daten "as is" in einem Hadoop File System (HDFS) und andererseits veredelte Daten in einem Oracle 12c Data Warehouse miteinander kombiniert oder auch per Direktzugriff in Oracle Business Intelligence ausgewertet bzw. mit Endeca Information Discovery auf neue Zusammenhänge untersucht werden.
This document provides an overview of Cloudera's SQL on Hadoop technologies including Hive, Spark SQL, and Impala. It discusses the features and capabilities of each technology, how they differ, and when each would be best suited for different use cases. Key points covered include Hive being optimized for batch processing while Impala and Spark SQL enable lower latency queries. The document also reviews columnar data formats like Parquet that can improve performance.
Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. Holden Karau and Joey Echeverria explore how to debug Apache Spark applications, the different options for logging in Spark’s variety of supported languages, and some common errors and how to detect them.
Spark’s own internal logging can often be quite verbose. Holden and Joey demonstrate how to effectively search logs from Apache Spark to spot common problems and discuss options for logging from within your program itself. Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but Holden and Joey look at how to effectively use Spark’s current accumulators for debugging before gazing into the future to see the data property type accumulators that may be coming to Spark in future versions. And in addition to reading logs and instrumenting your program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems. Holden and Joey cover how to quickly use the UI to figure out if certain types of issues are occurring in your job.
The talk will wrap up with Holden trying to get everyone to buy several copies of her new book, High Performance Spark.
Building production spark streaming applicationsJoey Echeverria
Designing, implementing, and testing an Apache Spark Streaming application is necessary to deploy to production but is not sufficient for long term management and monitoring. Simply learning the Spark Streaming APIs only gets you part of the way there. In this talk, I’ll be focusing on everything that happens after you’ve implemented your application in the context of a real-time alerting system for IT operational data.
Real-time analysis starts with transforming raw data into structured records. Typically this is done with bespoke business logic custom written for each use case. Joey Echeverria presents a configuration-based, reusable library for streaming ETL that can be embedded in real-time stream-processing systems and demonstrates its real-world use cases with Apache Kafka and Apache Hadoop.
Embeddable data transformation for real time streamsJoey Echeverria
This document summarizes Joey Echeverria's presentation on embeddable data transformation for real-time streams. Some key points include:
- Stream processing requires the ability to perform common data transformations like filtering, extracting, projecting, and aggregating on streaming data.
- Tools like Apache Storm, Spark, and Flink can be used to build stream processing topologies and jobs, but also have limitations for embedding transformations.
- Rocana Transform provides a library and DSL for defining reusable data transformation configurations that can be run within different stream processing systems or in batch jobs.
- The library supports common transformations as well as custom actions defined through Java. Configurations can extract metrics, parse logs, and perform
As the volume of data and number of applications moving to Apache Hadoop has increased, so has the need to secure that data and those applications. In this presentation, we’ll take a brief look at where Hadoop security is today and then peer into the future to see where Hadoop security is headed. Along the way, we’ll visit new projects such as Apache Sentry (incubating) and Apache Knox (incubating) as well as initiatives such as Project Rhino. We’ll see how all of this activity is making good on the promise of Hadoop as the future of data management.
Data ingest is a deceptively hard problem. In the world of big data processing, it becomes exponentially more difficult. It's not sufficient to simply land data on a system, that data must be ready for processing and analysis. The Kite SDK is a data API designed for solving the issues related to data infest and preparation. In this talk you'll see how Kite can be used for everything from simple tasks to production ready data pipelines in minutes.
This document discusses Cloudera's support for Apache Accumulo running on CDH4. It provides an overview of Accumulo and how it relates to Hadoop. Cloudera has tested and packaged Accumulo 1.4.3 for CDH4, which supports Hadoop 2.0. The document demonstrates Accumulo storing and querying log data and integrating with Pig. It outlines Cloudera's future plans to further integrate Accumulo with the Hadoop ecosystem and provides next steps for users interested in trying the Accumulo beta release.
The document discusses analyzing Twitter data with Hadoop. It describes using Flume to pull Twitter data from the Twitter API and store it in HDFS as JSON files. Hive is then used to query the JSON data with SQL, taking advantage of the JSONSerDe to parse the JSON. Impala provides faster interactive queries of the same data compared to Hive running MapReduce jobs. The document provides examples of the Flume, Hive, and Impala configurations and queries used in this Twitter analytics workflow.
This document discusses security for big data systems like Hadoop. It describes the evolution of security features from basic file permissions and job queue access control lists added in early versions of Hadoop to modern features like Kerberos authentication, encryption of data in transit and at rest, and cell-level security in systems like Accumulo and HBase. It also outlines some priorities for the future, like more granular encryption APIs and improved security integration with tools like Hive.
The document discusses contributing code to the Apache Sqoop project. It provides instructions on getting the code from various repositories, building the code which requires Ant, JDK, and Maven, and testing code using JUnit. It encourages contributing patches to help with future releases and the community. The review process is described which involves uploading patches, describing changes and testing, and getting feedback through iterations. Contact information and links are provided to help with the contribution process.
The document discusses Apache Hadoop and HBase. It provides an overview of HBase, describing it as a BigTable-like storage system that stores data in HDFS and uses ZooKeeper for coordination. It then discusses several real-world applications of HBase including real-time ad optimizations, click stream sessionization, crash reporting from Mozilla Firefox, location-based content serving from Navteq, and monitoring customer clusters at Cloudera.
3. Cloudera’s Distribution including Apache Hadoop
File System Mount UI Framework SDK
FUSE-DFS HUE HUE SDK
Workflow Scheduling Metadata
APACHE OOZIE* APACHE OOZIE* APACHE HIVE
Languages / Compilers
APACHE PIG, APACHE HIVE Fast Read/Write
Data Integration
Access
APACHE FLUME*,
APACHE SQOOP* APACHE HBASE
Coordination
APACHE ZOOKEEPER
*currently under incubation in the Apache Software Foundation
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Copyright 2011 Cloudera Inc. All rights reserved
22. Pig
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Copyright 2011 Cloudera Inc. All rights reserved
23. Pig
• Scripting language
• Generates MapReduce jobs
• Perl for Hadoop
• Great for ETL
A = LOAD 'data' USING PigStorage() AS (f1:int, f2:int, f3:int);
B = GROUP A BY f1;
C = FOREACH B GENERATE COUNT ($0);
DUMP C;
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Copyright 2011 Cloudera Inc. All rights reserved
48. HBase
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Copyright 2011 Cloudera Inc. All rights reserved
49. HBase
• Key/value store
• Data stored in HDFS
• Access model is get/put/del
– Plus range scans and versions
• Random reads and writes for Hadoop
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Copyright 2011 Cloudera Inc. All rights reserved
71. CDH
File System Mount UI Framework SDK
FUSE-DFS HUE HUE SDK
Workflow Scheduling Metadata
APACHE OOZIE* APACHE OOZIE* APACHE HIVE
Languages / Compilers
APACHE PIG, APACHE HIVE Fast Read/Write
Data Integration
Access
APACHE
FLUME*, APACHE APACHE HBASE
SQOOP*
Coordination
APACHE ZOOKEEPER
*currently under incubation in the Apache Software Foundation
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Copyright 2011 Cloudera Inc. All rights reserved
72. What’s next?
• Cloudera Training Videos
• CDH Virtual Machines
• Hadoop: The Definitive Guide, 2nd Edition
• Cloudera University
– Developer Training in Columbia, MD
• Dec 13-16, Feb 13-16
– Administrator Training in Herndon, VA
• Jan 4-6
– Private Training
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Copyright 2011 Cloudera Inc. All rights reserved
73. We’re Hiring!
• http://www.cloudera.com/company/careers/
• Customer Operations
– Customer Operations Engineer
– Customer Operations Tools Developer
• Customer Solutions
– Solutions Architect
• Engineering
– Senior Data Integration Developer
– Senior Distributed Systems Engineer
– Senior UI Engineer
– Software Quality Engineer
– Technical Writer
• IT/Operations
– Systems Administrator
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Copyright 2011 Cloudera Inc. All rights reserved