OpenStack Data Processing ("Sahara") project update - December 2014
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Sahara provides scalable data processing in OpenStack by provisioning and operating data processing clusters and scheduling and operating jobs. It implements Elastic Data Processing for workflow management. The Juno release focused on code contributions from 50 people, implementing 32 blueprints and fixing 168 bugs. Plans for the Kilo release include support for new Hadoop/Spark versions, improved dashboards, better Heat integration and Ironic support.
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Storage Engine Considerations for Your Apache Spark Applications with Mladen ...
Storage Engine Considerations for Your Apache Spark Applications with Mladen ...
Building a Business Logic Translation Engine with Spark Streaming for Communi...
Building a Business Logic Translation Engine with Spark Streaming for Communi...
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Benchmarking sahara based big data as a service solutions
Sahara is an OpenStack service that provides a platform for big data and data analytics workflows. It allows users to easily provision Hadoop clusters on OpenStack. Sahara supports various data processing engines and provides features such as cluster scaling, storage integration, and security. Benchmark testing showed significant performance overhead from using virtual machines compared to bare metal deployment, especially for I/O intensive workloads. Future work may include improved support for container-based and bare metal clusters as well as enhanced data processing and cluster management capabilities.
Sahara presentation latest - Codemotion Rome 2015
The document discusses a presentation on OpenStack Sahara given at a conference in Rome. It begins with introducing the three speakers and their backgrounds. It then provides an agenda for the presentation which includes an introduction to big data, an overview of OpenStack components, and a demonstration of Sahara in action. The presentation discusses what big data is, provides a brief history of MapReduce and Hadoop, and explains how OpenStack is well-suited to host big data platforms through its various components and architecture. It concludes by introducing OpenStack Sahara as a way to simplify deploying and managing Hadoop clusters on OpenStack.
20151027 sahara + manila final
Sahara and Manila: Crossing the Desert to the Big Data Oasis. This is presented by Intel, NetApp, and Red Hat in OpenStack Tokyo Summit.
از نماینده ایران در WSIS Prizes 2016 حمایت کنید ... متشکریم ...
اجلاس جهانی سران جامعه اطلاعاتی (World Summit on the Information Society -WSIS) سالانه توسط اتحادیه جهانی مخابرات و با پشتیبانی سازمان ملل متحد با موضوع فناوری اطلاعات و ارتباطات و جامعه اطلاعاتی (ITU) برگزار می-گردد. این نشست جهانی که با حضور وزرای مخابرات، فناوری اطلاعات و ارتباطات کشورهای دنیا شکل می ¬گیرد هجده خط مشی اصلی را دنبال می ¬کند تا کشورهای جهان را ترغیب نماید در راستای این هجده خط مشی گام برداشته و فناوری اطلاعات و ارتباطات کشور خود را توسعه و ترقی دهند.
از مهم ¬ترین خط¬ مش¬ های تعیین شده، توسعه موارد کاربرد فناوری اطلاعات و ارتباطات در زمینه آموزش الکترونیکی (e-learning) و علم الکترونیکی (e-science) می¬ باشد که راهکار یکپارچه آموزش مجازی (ELIS) و شبکه کوثرنت (Kowsar-Net) به ترتیب در این راستا توسعه یافته و هم¬اکنون به عنوان نماینده ایران برای کسب جایزه جهانی اجلاس جهانی سران با رقبای خارجی خود وارد رقابت شده است. هم اکنون، این دو پروژه پس از قبولی در مرحله ارزیابی و داوری بین المللی نامزد دریافت جایزه بهترین پروژه در گروه آموزش الکترونیکی و علم الکترونیک شده و در مرحله رای¬ گیری عمومی می ¬باشد.
از این رو، این پیام با هدف حمایت از نماینده کشورمان در این عرصه بین المللی ارسال شده است. با توجه به رسالت این دو پروژه و ماهیت علمی، فرهنگی و اسلامی آن، از شما سرور گرامی دعوت می شود ضمن رای دادن و اعلام حمایت خود از پروژه های نامبرده (گروه 9 و 14)، سایر دوستان، همکاران و متخصصان را به حمایت از راهکار یکپارچه آموزش مجازی (ELIS) و شبکه کوثرنت (Kowsar-Net) دعوت نمایید. ضمنا رای شما به سایر پروژه¬ ها در دیگر گروه ها نیز ارزشمند است.
آدرس سایت جهت ثبت نام و رای دهی:
http://groups.itu.int/stocktaking/WSISPrizes/WSISPrizes2016/Voting.aspx
آخرین مهلت رای دهی: 20 اسفند 94
جهت کسب اطلاعات بیشتر کانال ما را در تلگرام دنبال کنید wsisprizeswhc@
20150704 benchmark and user experience in sahara weiting
Sahara provides a way to deploy and manage Hadoop clusters within an OpenStack cloud. It addresses common customer needs like providing an elastic environment for data processing jobs, integrating Hadoop with the existing private cloud infrastructure, and reducing costs. Key challenges include speeding up cluster provisioning times, supporting complex data workflows, optimizing storage architectures, and improving performance when using remote object storage.
Sahara Updates - Kilo Edition
Sahara provides scalable data processing in OpenStack by provisioning and operating data processing clusters and scheduling and operating jobs. It implements Elastic Data Processing for workflow management. The Juno release focused on code contributions from 50 people, implementing 32 blueprints and fixing 168 bugs. Plans for the Kilo release include support for new Hadoop/Spark versions, improved dashboards, better Heat integration and Ironic support.
آشنایی با جرمیابی قانونی رایانهای
اولین همایش تخصصی مرکز آپا دانشگاه تحصیلات تکمیلی و پیشرفته صنعتی کرمان
Cloud Security and Risk Management
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به همراه مثالهایی از رایانش ابری به زبان ساده و نتایج مطالعه موردی تحلیل مخاطرات فکس ابری
The Evolution of OpenStack – From Infancy to Enterprise
As OpenStack turns 5 this year, we thought it would be a good time to take a look back at the evolution of OpenStack. We start with a quick overview of what OpenStack is, how OpenStack came to be and describe the OpenStack Foundation. Next we describe the problem that OpenStack helps to solve, the components of OpenStack and the timeline for when these components came to be. Last, we outline the current features and benefits that make OpenStack ready for the enterprise with supporting Enterprise use case examples. Blog can be found here (
https://developer.rackspace.com/blog/evolution-of-openstack-from-infancy-to-enterprise/) and webinar can be found here (https://www.brighttalk.com/webcast/11427/138613)
Big Data on OpenStack
The massive computing and storage resources that are needed to support big data applications make cloud environments an ideal fit. In this session, you'll learn how to build your big data "database on-demand" using MongoDB, Cassandra, Solr, MySQL, or any other big data solution, as well as manage your big data application using a new open source framework called “Cloudify.” All this, on top of the OpenStack cloud.
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Benchmarking sahara based big data as a service solutions
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از نماینده ایران در WSIS Prizes 2016 حمایت کنید ... متشکریم ...
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20150704 benchmark and user experience in sahara weiting
20150704 benchmark and user experience in sahara weiting
The Evolution of OpenStack – From Infancy to Enterprise
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Similar to OpenStack Data Processing ("Sahara") project update - December 2014
Sahara Updates - Liberty Edition
Sahara provides scalable data processing by provisioning and operating data processing clusters and scheduling jobs. In the Kilo release, Sahara added support for new plugins like Apache Storm, improved the Sahara UI for guided cluster creation and job execution, and added features like indirect VM access, an event log, and default templates via the CLI. Looking ahead, upcoming Liberty releases will focus on high availability for CDH and HDP, support for Spark, improved data sources handling, editing existing objects, and enhanced testing.
Hortonworks tech workshop in-memory processing with spark
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
Creating the Internet of Your Things
The document discusses Microsoft's Azure IoT platform for connecting, managing, and analyzing Internet of Things devices and data. It provides an overview of the key components of Azure IoT including Azure IoT Hub for device connectivity and management, analytics services like Azure Machine Learning and Stream Analytics, and connectivity to other Azure services. It also highlights aspects of Azure IoT like its open ecosystem, support for open standards, and global infrastructure running on Microsoft's Azure cloud.
Bigdataappliance datasheet-1883358
The Oracle Big Data Appliance X4-2 is a comprehensive big data platform that includes Cloudera's Distribution of Apache Hadoop, Apache Spark, Oracle NoSQL Database, and other big data tools. It provides a massively scalable, secure infrastructure for storing and processing large volumes of data. The appliance offers optimized performance for both batch and interactive queries, and integrates with Oracle databases. It offers a lower total cost of ownership compared to do-it-yourself Hadoop systems through bundled hardware, software, and support.
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Hadoop 3.0 will include several major new features and improvements, including HDFS erasure coding for improved storage efficiency, built-in support for long running services in YARN, and better resource isolation including Docker support. It also focuses on compatibility by preserving wire compatibility with Hadoop 2 clients and supporting rolling upgrades. Extensive testing is planned through alpha, beta, and GA releases to stabilize and validate the new features.
Big Data Customer Education Webcast: The Latest Advancements in Syncsort DMX ...
Don’t wait! Get the ultimate in data agility with the software you own today!
While other data integration companies hold out the possibility of simplifying cross-platform data integration down the road… our customers have enjoyed these capabilities for close to two years! And our industry-leading Big Data integration software keeps getting better.
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In this webcast, we will cover important new features that help you speed development, adapt to the latest data management requirements, and leverage rapid innovation in Big Data technology, including:
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Apache Ambari BOF - OpenStack - Hadoop Summit 2013
Apache Ambari BOF Meet Up @ Hadoop Summit 2013
OpenStack
http://www.meetup.com/Apache-Ambari-User-Group/events/119184782/
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If you are running Apache Spark in cloud environments, Object Stores —such as Amazon S3 or Azure WASB— are a core part of your system. What you can’t do is treat them like “just another filesystem” —do that and things will, eventually, go horribly wrong.
This talk looks at the object stores in the cloud infrastructures, including underlying architectures., compares them to what a “real filesystem” is expected to do and shows how to use object stores efficiently and safely as sources of and destinations of data.
It goes into depth on recent “S3a” work, showing how including improvements in performance, security, functionality and measurement —and demonstrating how to use make best use of it from a spark application.
If you are planning to deploy Spark in cloud, or doing so today: this is information you need to understand. The performance of you code and integrity of your data depends on it.
Hadoop Now, Next and Beyond
The document discusses the history and development of Apache Hadoop. It notes that Hadoop was originally developed at Yahoo and later open sourced. It describes key features added in Hadoop 2.0 like YARN and rolling upgrades. It outlines performance improvements achieved in Hive through projects like Stinger that added features like Tez, vectorization, and a query planner. The document advocates that Hortonworks has made Hadoop enterprise ready with services for high availability, security, and operations.
Hadoop Summit Dublin 2016: Hadoop Platform at Yahoo - A Year in Review
Over the past year, a lot of progress has been made in advancing the Apache Hadoop platform at Yahoo. We underwent a massive infrastructure consolidation to lower the platform TCO. CaffeOnSpark was open-sourced for distributed deep learning on existing infrastructure with a combination of CPU and GPU-based computing. Traditional compute on MapReduce continues to shift to Apache Tez and Apache Spark for lower processing time. Our internal security, multi-tenancy, and scale changes to Apache Storm got pushed to the community in Storm 0.10. Omid was open-sourced for managing transactions reliably on Apache HBase. Multi-tenancy with region groups, splittable META, ZooKeeper-less assignment manager, favored nodes with HDFS block placement, and support for humongous tables have taken Apache HBase scale to new heights. Dependency management in Apache Oozie for combinatorial, conditional, and optional processing gives increased flexibility to our data pipelines teams in maintaining SLAs. Focus on ease of use and onboarding improvements have brought in a whole new class of use cases and users to the platform. In this talk, we will provide a comprehensive overview of the platform technology stack, recent developments, metrics, and share thoughts on where things are headed when it comes to big data at Yahoo.
Hadoop Platform at Yahoo
The document provides an overview of the Hadoop platform at Yahoo over the past year. It discusses the evolution of the platform infrastructure and metrics including growth in storage from 12PB to 65PB and compute capacity from 23TB to 240TB. It highlights new technologies added to the platform like CaffeOnSpark for distributed deep learning, Apache Storm for streaming analytics, and data sketches algorithms. It also discusses enhancements to existing technologies like HBase for transactions with Omid and improvements to Oozie for data pipelines. The document aims to provide insights on how the Hadoop platform at Yahoo has scaled to support growing analytics needs through consolidation, new services, and ease of use features.
Empower Data-Driven Organizations with HPE and Hadoop
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-Add-on solutions – Hadoop needs more to fill in the gaps. HPE partners with the right ecosystem partners to bring you solutions such an industrial grade SQL on Hadoop with Vertica, data encryption with SecureData, SAP ecosystem with SAP HANA VORA, Multitenancy with Blue Data, Object storage with Scality and more.
Apache Spark and Object Stores
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Introduction to the Hadoop EcoSystem
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Big data architecture on cloud computing infrastructure
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Modernize Your Existing EDW with IBM Big SQL & Hortonworks Data Platform
Find out how Hortonworks and IBM help you address these challenges to enable success to optimize your existing EDW environment.
https://hortonworks.com/webinar/modernize-existing-edw-ibm-big-sql-hortonworks-data-platform/
Cloudera + MicrosoftでHadoopするのがイイらしい。 #CWT2016
日本マイクロソフト: 藤田 稜氏、日立ソリューションズ：岩永 匡希氏、Cloudera: 川崎
３名による
Cloudera World Tokyo 2016 (http://www.clouderaworldtokyo.com/) の講演内容です
#CWT2016
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HPC DAY 2017 | FlyElephant Solutions for Data Science and HPC
HPC DAY 2017 - http://www.hpcday.eu/
FlyElephant Solutions for Data Science and HPC
Dmitry Spodarets | CEO at FlyElephant
Azure Big data
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Odoo ERP Vs. Traditional ERP Systems – A Comparative Analysis
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
SMS API Integration in Saudi Arabia| Best SMS API Service
SMS API Integration in Saudi Arabia| Best SMS API Service
What next after learning python programming basics
What next after learning python programming basics
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...
OpenStack Data Processing ("Sahara") project update - December 2014
- 1. Sergey Lukjanov PTL OpenStack Sahara Data Processing Update
- 2. To provide a scalable data processing stack and associated management interfaces Sahara ● provisions & operates data proc. clusters ● schedules & operates data proc. jobs / workloads
- 3. Elastic Data Processing (EDP) is Sahara’s take on data processing workflow management.
- 4. Juno release overview ● 540+ code commits from 50 people ● 32 blueprints implemented ● ~5000 code reviews ● ~168 bugs fixed details: https://launchpad.net/sahara/juno
- 5. Moved to specs for new features
- 6. Sahara UI completely merged into Horizon
- 8. Vanilla Apache Hadoop 2.4.1 support Cloudera Distribution of Apache Hadoop 5.X support Apache Spark 0.9.1 and 1.0.0 support
- 10. Heat resources for Sahara available
- 11. Auto security groups creation
- 12. Kilo plans ● New versions for CDH, HDP, etc. ● Dashboard UX improvements ● Better Heat integration ● Ironic support