The document discusses the evolution of computing models from clusters and grids to cloud computing. It describes how cluster computing involved tightly coupled resources within a LAN, while grids allowed for resource sharing across domains. Utility computing introduced an ownership model where users leased computing power. Finally, cloud computing allows access to services and data from any internet-connected device through a browser.
The Anatomy Of The Google Architecture Fina Lv1.1Hassy Veldstra
A comprehensive overview of Google's architecture - starting from the search page and all the way to its internal networks.
By Ed Austin, talk given at Edinburgh Techmeetup in December 2009
http://techmeetup.co.uk
This presentation helps you understand the basics of Hadoop.
What is Big Data?? How google search so fast and what is MapReduce algorithm? all these questions will be answered in the presentation.
Hybrid data lake on google cloud with alluxio and dataprocAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Hybrid Data Lake on Google Cloud with Alluxio and Dataproc
Roderick Yao, Strategic Cloud Engineer (Google Cloud)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Hadoop Institutes: kelly technologies are the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
http://www.kellytechno.com/Hyderabad/Course/Hadoop-Training
The Anatomy Of The Google Architecture Fina Lv1.1Hassy Veldstra
A comprehensive overview of Google's architecture - starting from the search page and all the way to its internal networks.
By Ed Austin, talk given at Edinburgh Techmeetup in December 2009
http://techmeetup.co.uk
This presentation helps you understand the basics of Hadoop.
What is Big Data?? How google search so fast and what is MapReduce algorithm? all these questions will be answered in the presentation.
Hybrid data lake on google cloud with alluxio and dataprocAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Hybrid Data Lake on Google Cloud with Alluxio and Dataproc
Roderick Yao, Strategic Cloud Engineer (Google Cloud)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Hadoop Institutes: kelly technologies are the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
http://www.kellytechno.com/Hyderabad/Course/Hadoop-Training
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
Accumulo Summit 2015: Performance Models for Apache Accumulo: The Heavy Tail ...Accumulo Summit
Talk Abstract
As with all open-source databases, Accumulo developers often compete between building exciting new features and hacking on performance and stability. As the core features solidify and expand, we see many opportunities to improve performance. An effective methodology for performance improvement is scientific in nature, and follows a well-definite modeling and simulation approach, matching theory to experimentation in an iterative fashion.
Ingest performance is one of the most differentiating characteristics of Accumulo. However, there is much room for improvement for typical ingest-heavy applications. Accumulo supports two mechanisms to bring data in: streaming ingest and bulk ingest. In bulk ingest, the goal is to maximize throughput without constraining latency. Bulk ingest involves creating a set of files that conform to Accumulo's internal RFile format and then registering those files with Accumulo. MapReduce provides a framework for generating, sorting, and storing key/value pairs, which form the primary elements of preparing RFiles for bulk ingest. MapReduce has been used many times over the years to break sorting records, such as Terasort. We can expect it is a reasonable choice for maximizing bulk ingest throughput. However, the theory often proves challenging to implement as there are many performance pitfalls along the way.
In this talk, we dive deep into optimizing MapReduce for Accumulo bulk ingest. We share detailed theoretical and empirical performance models, we discuss techniques for profiling performance, and we suggest reusable techniques for squeezing the maximum performance out of enterprise-grade Accumulo bulk ingest.
Speaker
Chris McCubbin
Director of Data Science, Sqrrl
Chris is the Director of Data Science for Sqrrl. He has extensive experience with the Hadoop ecosystem and applying scientific computation algorithms to real-world datasets. Previously, Chris developed Big Data analysis tools for the Intelligence Community and applied artificial intelligence techniques to unmanned vehicle systems. He holds a MS in Computer Science and BS in Computer Science and Mathematics from the University of Maryland.
Accumulo Summit 2015: Building Aggregation Systems on Accumulo [Leveraging Ac...Accumulo Summit
Talk Abstract
Aggregation has long been a use case of Accumulo Iterators. Iterators' ability to reduce data during compaction and scanning can greatly simplify an aggregation system built on Accumulo. This talk will first review how Accumulo's Iterators/Combiners work in the context of aggregating values. I'll then step back and look at the abstraction of aggregation functions as commutative operations and the several benefits that result by making this abstraction. We will see how it becomes no harder to introduce powerful operations such as cardinality estimation and approximate top-k than it is to sum integers. I will show how to integrate these ideas into Accumulo with an example schema and Iterator. Finally, a practical aggregation use case will be discussed to highlight the concepts from the talk.
Speakers
Gadalia O'Bryan
Senior Solutions Architect, Koverse
Gadalia O'Bryan is a Sr. Solutions Architect at Koverse, where she leads customer projects and contributes to key feature and algorithm design, such as Koverse's Aggregation Framework. Prior to Koverse, Gadalia was a mathematician for the National Security Agency. She has an M.A. in mathematics from UCLA and has been working with Accumulo for the past 6 years.
Bill Slacum
Software Engineer, Koverse
Bill is an Accumulo committer and PMC member who has been working on large scale query and analytic frameworks since 2010. He holds BS's in computer science and financial economics from UMBC. Having never used his passport to leave the United States, he is currently a national man of mystery.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Fluid: When Alluxio Meets Kubernetes
Yang Che, Staff Engineer at Alibaba Cloud
Rong Gu, Professor at Nanjing University
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Extending DevOps to Big Data Applications with KubernetesNicola Ferraro
DevOps, continuous delivery and modern architectural trends can incredibly speed up the software development process. Big Data applications cannot be an exception and need to keep the same pace.
اسلایدهای کارگاه پردازش های موازی با استفاده از زیرساخت جی پی یو GPU
اولین کارگاه ملی رایانش ابری کشور
وحید امیری
vahidamiry.ir
دانشگاه صنعتی امیرکبیر - 1391
MapR M7: Providing an enterprise quality Apache HBase APImcsrivas
Provides an overview of M7, which is the first unified data platform for tables and files. Does a deep dive into the MapR architecture, especially containers, and how M7 tables integrates with the rest of MapR architecture, including volumes, management and Hadoop.
Describes some of the problems with Apache HBase, and how M7 from MapR solves many of these issues.
Accumulo Summit 2015: Ferrari on a Bumpy Road: Shock Absorbers to Smooth Out ...Accumulo Summit
Talk Abstract
Accumulo has a solid theoretical foundation, endowing it with huge scalability, high reliability, and the makings of class-leading performance for NoSQL operations. Several publications show Accumulo achieving multi-petabyte scalability and outperforming other databases in its class by orders of magnitude. However, there are challenges arising in practice that slow down that performance and introduce bottlenecks.
The root of Accumulo's distributed scale and performance while maintaining consistency comes from a multi-level amplification. Zookeeper bootstraps the consistency with a highly durable quorum. The Accumulo root table uses buffering and caching to boost that performance for sorted key/value operations. With the metadata tablets and data tables, Accumulo continues to boost performance and divides and conqures a highly scalable key/value space to leverage the resources of a large cluster. The challenge arrises when metadata operations at the core of Accumulo bottleneck performance for the entire cluster.
In this talk we will describe the Accumulo metadata operations model in detail. With a couple of prototypical application scenarios, we will show a few areas that are current bottlenecks or that we can expect to be bottlenecks in the near future. We will also propose modifications to the current model and outline projects that the community can take on to keep Accumulo in the lead for performance and scalability.
Speaker
Adam Fuchs
Chief Technology Officer, Sqrrl
As the Chief Technology Officer and co-founder of Sqrrl, Adam Fuchs is responsible for ensuring that Sqrrl is leading the world in Big Data Infrastructure technology. Previously at the National Security Agency, Adam was an innovator and technical director for several database projects, handling some of the world’s largest and most diverse data sets. He is a co-founder of the Apache Accumulo project. Adam has a BS in Computer Science from the University of Washington and has completed extensive graduate-level course work at the University of Maryland.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
Designing a Scalable Twitter - Patterns for Designing Scalable Real-Time Web ...Nati Shalom
Twitter is a good example for next generation real-time web applications, but building such an application imposes challenges such as handling an every growing volume of tweets and responses, as well as a large number of concurrent users, who continually *listen* for tweets from users (or topics) they follow. During this session we will review some of the key design principles addressing these challenges, including alternatives *NoSQL* alternatives and blackboard patterns. We will be using Twitter as a use case, while learning how to apply these to any real-time we application
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
Accumulo Summit 2015: Performance Models for Apache Accumulo: The Heavy Tail ...Accumulo Summit
Talk Abstract
As with all open-source databases, Accumulo developers often compete between building exciting new features and hacking on performance and stability. As the core features solidify and expand, we see many opportunities to improve performance. An effective methodology for performance improvement is scientific in nature, and follows a well-definite modeling and simulation approach, matching theory to experimentation in an iterative fashion.
Ingest performance is one of the most differentiating characteristics of Accumulo. However, there is much room for improvement for typical ingest-heavy applications. Accumulo supports two mechanisms to bring data in: streaming ingest and bulk ingest. In bulk ingest, the goal is to maximize throughput without constraining latency. Bulk ingest involves creating a set of files that conform to Accumulo's internal RFile format and then registering those files with Accumulo. MapReduce provides a framework for generating, sorting, and storing key/value pairs, which form the primary elements of preparing RFiles for bulk ingest. MapReduce has been used many times over the years to break sorting records, such as Terasort. We can expect it is a reasonable choice for maximizing bulk ingest throughput. However, the theory often proves challenging to implement as there are many performance pitfalls along the way.
In this talk, we dive deep into optimizing MapReduce for Accumulo bulk ingest. We share detailed theoretical and empirical performance models, we discuss techniques for profiling performance, and we suggest reusable techniques for squeezing the maximum performance out of enterprise-grade Accumulo bulk ingest.
Speaker
Chris McCubbin
Director of Data Science, Sqrrl
Chris is the Director of Data Science for Sqrrl. He has extensive experience with the Hadoop ecosystem and applying scientific computation algorithms to real-world datasets. Previously, Chris developed Big Data analysis tools for the Intelligence Community and applied artificial intelligence techniques to unmanned vehicle systems. He holds a MS in Computer Science and BS in Computer Science and Mathematics from the University of Maryland.
Accumulo Summit 2015: Building Aggregation Systems on Accumulo [Leveraging Ac...Accumulo Summit
Talk Abstract
Aggregation has long been a use case of Accumulo Iterators. Iterators' ability to reduce data during compaction and scanning can greatly simplify an aggregation system built on Accumulo. This talk will first review how Accumulo's Iterators/Combiners work in the context of aggregating values. I'll then step back and look at the abstraction of aggregation functions as commutative operations and the several benefits that result by making this abstraction. We will see how it becomes no harder to introduce powerful operations such as cardinality estimation and approximate top-k than it is to sum integers. I will show how to integrate these ideas into Accumulo with an example schema and Iterator. Finally, a practical aggregation use case will be discussed to highlight the concepts from the talk.
Speakers
Gadalia O'Bryan
Senior Solutions Architect, Koverse
Gadalia O'Bryan is a Sr. Solutions Architect at Koverse, where she leads customer projects and contributes to key feature and algorithm design, such as Koverse's Aggregation Framework. Prior to Koverse, Gadalia was a mathematician for the National Security Agency. She has an M.A. in mathematics from UCLA and has been working with Accumulo for the past 6 years.
Bill Slacum
Software Engineer, Koverse
Bill is an Accumulo committer and PMC member who has been working on large scale query and analytic frameworks since 2010. He holds BS's in computer science and financial economics from UMBC. Having never used his passport to leave the United States, he is currently a national man of mystery.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Fluid: When Alluxio Meets Kubernetes
Yang Che, Staff Engineer at Alibaba Cloud
Rong Gu, Professor at Nanjing University
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Extending DevOps to Big Data Applications with KubernetesNicola Ferraro
DevOps, continuous delivery and modern architectural trends can incredibly speed up the software development process. Big Data applications cannot be an exception and need to keep the same pace.
اسلایدهای کارگاه پردازش های موازی با استفاده از زیرساخت جی پی یو GPU
اولین کارگاه ملی رایانش ابری کشور
وحید امیری
vahidamiry.ir
دانشگاه صنعتی امیرکبیر - 1391
MapR M7: Providing an enterprise quality Apache HBase APImcsrivas
Provides an overview of M7, which is the first unified data platform for tables and files. Does a deep dive into the MapR architecture, especially containers, and how M7 tables integrates with the rest of MapR architecture, including volumes, management and Hadoop.
Describes some of the problems with Apache HBase, and how M7 from MapR solves many of these issues.
Accumulo Summit 2015: Ferrari on a Bumpy Road: Shock Absorbers to Smooth Out ...Accumulo Summit
Talk Abstract
Accumulo has a solid theoretical foundation, endowing it with huge scalability, high reliability, and the makings of class-leading performance for NoSQL operations. Several publications show Accumulo achieving multi-petabyte scalability and outperforming other databases in its class by orders of magnitude. However, there are challenges arising in practice that slow down that performance and introduce bottlenecks.
The root of Accumulo's distributed scale and performance while maintaining consistency comes from a multi-level amplification. Zookeeper bootstraps the consistency with a highly durable quorum. The Accumulo root table uses buffering and caching to boost that performance for sorted key/value operations. With the metadata tablets and data tables, Accumulo continues to boost performance and divides and conqures a highly scalable key/value space to leverage the resources of a large cluster. The challenge arrises when metadata operations at the core of Accumulo bottleneck performance for the entire cluster.
In this talk we will describe the Accumulo metadata operations model in detail. With a couple of prototypical application scenarios, we will show a few areas that are current bottlenecks or that we can expect to be bottlenecks in the near future. We will also propose modifications to the current model and outline projects that the community can take on to keep Accumulo in the lead for performance and scalability.
Speaker
Adam Fuchs
Chief Technology Officer, Sqrrl
As the Chief Technology Officer and co-founder of Sqrrl, Adam Fuchs is responsible for ensuring that Sqrrl is leading the world in Big Data Infrastructure technology. Previously at the National Security Agency, Adam was an innovator and technical director for several database projects, handling some of the world’s largest and most diverse data sets. He is a co-founder of the Apache Accumulo project. Adam has a BS in Computer Science from the University of Washington and has completed extensive graduate-level course work at the University of Maryland.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
Designing a Scalable Twitter - Patterns for Designing Scalable Real-Time Web ...Nati Shalom
Twitter is a good example for next generation real-time web applications, but building such an application imposes challenges such as handling an every growing volume of tweets and responses, as well as a large number of concurrent users, who continually *listen* for tweets from users (or topics) they follow. During this session we will review some of the key design principles addressing these challenges, including alternatives *NoSQL* alternatives and blackboard patterns. We will be using Twitter as a use case, while learning how to apply these to any real-time we application
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
DM Radio Webinar: Adopting a Streaming-Enabled ArchitectureDATAVERSITY
Architecture matters. That's why today's innovators are taking a hard look at streaming data, an increasingly attractive option that can transform business in several ways: replacing aging data ingestion techniques like ETL; solving long-standing data quality challenges; improving business processes ranging from sales and marketing to logistics and procurement; or any number of activities related to accelerating data warehousing, business intelligence and analytics.
Register for this DM Radio Deep Dive Webinar to learn how streaming data can rejuvenate or supplant traditional data management practices. Host Eric Kavanagh will explain how streaming-first architectures can relieve data engineers from time-consuming, error-prone processes, ideally bidding farewell to those unpleasant batch windows. He'll be joined by Kevin Petrie of Attunity, who will explain why (with real-world story successes) streaming data solutions can keep the business fueled with trusted data in a timely, efficient manner for improved business outcomes.
Scaling Security on 100s of Millions of Mobile Devices Using Apache Kafka® an...confluent
Watch this talk here: https://www.confluent.io/online-talks/scaling-security-on-100s-of-millions-of-mobile-devices-using-kafka-and-scylla-on-demand
Join mobile cybersecurity leader Lookout as they talk through their data ingestion journey.
Lookout enables enterprises to protect their data by evaluating threats and risks at post-perimeter endpoint devices and providing access to corporate data after conditional security scans. Their continuous assessment of device health creates a massive amount of telemetry data, forcing new approaches to data ingestion. Learn how Lookout changed its approach in order to grow from 1.5 million devices to 100 million devices and beyond, by implementing Confluent Platform and switching to Scylla.
Cluster based storage - Nasd and Google file system - advanced operating syst...Antonio Cesarano
This is a seminar at the Course of Advanced Operating Systems at University of Salerno which shows the first cluster based storage technology (NASD) and its evolution till the development of the new Google File System.
Solving enterprise challenges through scale out storage & big compute finalAvere Systems
Google Cloud Platform, Avere Systems, and Cycle Computing experts will share best practices for advancing solutions to big challenges faced by enterprises with growing compute and storage needs. In this “best practices” webinar, you’ll hear how these companies are working to improve results that drive businesses forward through scalability, performance, and ease of management.
The slides were from a webinar presented January 24, 2017. The audience learned:
- How enterprises are using Google Cloud Platform to gain compute and storage capacity on-demand
- Best practices for efficient use of cloud compute and storage resources
- Overcoming the need for file systems within a hybrid cloud environment
- Understand how to eliminate latency between cloud and data center architectures
- Learn how to best manage simulation, analytics, and big data workloads in dynamic environments
- Look at market dynamics drawing companies to new storage models over the next several years
Presenters communicated a foundation to build infrastructure to support ongoing demand growth.
Despite rumours to the contrary, a private cloud model offers a secure data repository that is protected by cast iron service guarantees (and if your service provider can’t provide them – consider looking elsewhere), which can either be used as a standalone option or integrated within existing client infrastructure to form a hybrid solution
Similar to Google Cloud Computing on Google Developer 2008 Day (20)
6. The Next Step: Cloud Computing Services and data are in the cloud, accessible with any device connected to the cloud with a browser
7. The Next Step: Cloud Computing Services and data are in the cloud, accessible with any device connected to the cloud with a browser A key technical issue for developers: Scalability
8. Applications on the Web internet splat map: http://flickr.com/photos/jurvetson/916142/ , CC-by 2.0 baby picture: http://flickr.com/photos/cdharrison/280252512/ , CC-by-sa 2.0 Your user internet splat map: http://flickr.com/photos/jurvetson/916142/ , CC-by 2.0 baby picture: http://flickr.com/photos/cdharrison/280252512/ , CC-by-sa 2.0 Your Coolest Web Application
9. Applications on the Web internet splat map: http://flickr.com/photos/jurvetson/916142/ , CC-by 2.0 baby picture: http://flickr.com/photos/cdharrison/280252512/ , CC-by-sa 2.0 Your user internet splat map: http://flickr.com/photos/jurvetson/916142/ , CC-by 2.0 baby picture: http://flickr.com/photos/cdharrison/280252512/ , CC-by-sa 2.0 The Cloud Your Coolest Web Application
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11. How many users do you want to have? The Cloud Your Coolest Web Application
12. How many users do you want to have? The Cloud Your Coolest Web Application
13. Google Growth Nov. '98: 10,000 queries on 25 computers Apr. '99: 500,000 queries on 300 computers Sep. '99: 3,000,000 queries on 2100 computers
24. How to develop a web application that scales? Storage Database Serving Google's solution/replacement Google File System BigTable MapReduce Google AppEngine Data Processing
25. How to develop a web application that scales? Storage Database Serving Google's solution/replacement Google File System BigTable MapReduce Google AppEngine Published papers Opened on 2008/5/28 Data Processing hadoop: open source implementation
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30. System Structure Lock service Bigtable master Bigtable tablet server Bigtable tablet server Bigtable tablet server GFS Cluster scheduling system … holds metadata, handles master-election holds tablet data, logs handles failover, monitoring performs metadata ops + load balancing serves data serves data serves data BigTable Cell
31. System Structure Lock service Bigtable master Bigtable tablet server Bigtable tablet server Bigtable tablet server GFS Cluster scheduling system … holds metadata, handles master-election holds tablet data, logs handles failover, monitoring performs metadata ops + load balancing serves data serves data serves data Bigtable client Bigtable client library Open() read/write metadata ops BigTable Cell
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34. Pseudo Codes for Phase 1 and 2 def findBucket(requestTime): # return minute of the week numRequest = zeros(1440*7) # an array of 1440*7 zeros for filename in sys.argv[2:]: for line in open(filename): minuteBucket = findBucket(findTime(line)) numRequest[minuteBucket] += 1 outFile = open(sys.argv[1], 'w') for i in range(1440*7): outFile.write("%d %d" % (i, numRequest[i])) outFile.close() numRequest = zeros(1440*7) # an array of 1440*7 zeros for filename in sys.argv[2:]: for line in open(filename): col = line.split() [i, count] = [int(col[0]), int(col[1])] numRequest[i] += count # write out numRequest[] like phase 1
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53. MapReduce: Adoption at Google MapReduce Programs in Google ’ s Source Tree Summer intern effect New MapReduce Programs Per Month
67. The era of Cloud Computing is here! Photo by mr.hero on panoramio (http://www.panoramio.com/photo/1127015) news people book search photo product search video maps e-mails mobile blogs groups calendar scholar Earth Sky web desktop translate messages