Submit Search
Upload
Your Code is Wrong
•
29 likes
•
31,503 views
N
nathanmarz
Follow
My keynote at NoSQL Now! on August 21st, 2013
Read less
Read more
Technology
Report
Share
Report
Share
1 of 106
Download now
Download to read offline
Recommended
My keynote at GOTO Berlin 2013
The Epistemology of Software Engineering
The Epistemology of Software Engineering
nathanmarz
Presented at Data Day Texas on January 10th, 2015
Using Simplicity to Make Hard Big Data Problems Easy
Using Simplicity to Make Hard Big Data Problems Easy
nathanmarz
Talk given to Storm NYC meetup group on 3/18/2015
The inherent complexity of stream processing
The inherent complexity of stream processing
nathanmarz
My presentation of Storm at the Bay Area Hadoop User Group on January 18th, 2012.
Storm
Storm
nathanmarz
Storm: Distributed and fault tolerant realtime computation
Storm: Distributed and fault tolerant realtime computation
Ferran Galí Reniu
Introduction to Storm
Introduction to Storm
Eugene Dvorkin
Over the last couple years, Apache Storm became a de-facto standard for developing real-time analytics and complex event processing applications. Storm enables to tackle real-time data processing challenges the same way Hadoop enables batch processing of Big Data. Storm enables companies to have "Fast Data" alongside with "Big Data". Some use cases where Storm can be used are Fraud Detection, Operation Intelligence, Machine Learning, ETL, Analytics, etc. In this meetup, Eugene Dvorkin, Architect @WebMD and NYC Storm User Group organizer will teach Apache Storm and Stream Processing fundamentals. While this meeting is geared toward new Storm users, experienced users may find something interesting as well. Following topics will be covered: • Why use Apache Storm? • Common use cases • Storm Architecture - components, concepts, topology • Building simple Storm topology with Java and Groovy • Trident and micro-batch processing • Fault tolerance and guaranteed message delivery • Running and monitoring Storm in production • Kafka • Storm at WebMD • Resources
Learning Stream Processing with Apache Storm
Learning Stream Processing with Apache Storm
Eugene Dvorkin
Details of the real time stream processing STORM internal design.
Storm presentation
Storm presentation
Shyam Raj
Recommended
My keynote at GOTO Berlin 2013
The Epistemology of Software Engineering
The Epistemology of Software Engineering
nathanmarz
Presented at Data Day Texas on January 10th, 2015
Using Simplicity to Make Hard Big Data Problems Easy
Using Simplicity to Make Hard Big Data Problems Easy
nathanmarz
Talk given to Storm NYC meetup group on 3/18/2015
The inherent complexity of stream processing
The inherent complexity of stream processing
nathanmarz
My presentation of Storm at the Bay Area Hadoop User Group on January 18th, 2012.
Storm
Storm
nathanmarz
Storm: Distributed and fault tolerant realtime computation
Storm: Distributed and fault tolerant realtime computation
Ferran Galí Reniu
Introduction to Storm
Introduction to Storm
Eugene Dvorkin
Over the last couple years, Apache Storm became a de-facto standard for developing real-time analytics and complex event processing applications. Storm enables to tackle real-time data processing challenges the same way Hadoop enables batch processing of Big Data. Storm enables companies to have "Fast Data" alongside with "Big Data". Some use cases where Storm can be used are Fraud Detection, Operation Intelligence, Machine Learning, ETL, Analytics, etc. In this meetup, Eugene Dvorkin, Architect @WebMD and NYC Storm User Group organizer will teach Apache Storm and Stream Processing fundamentals. While this meeting is geared toward new Storm users, experienced users may find something interesting as well. Following topics will be covered: • Why use Apache Storm? • Common use cases • Storm Architecture - components, concepts, topology • Building simple Storm topology with Java and Groovy • Trident and micro-batch processing • Fault tolerance and guaranteed message delivery • Running and monitoring Storm in production • Kafka • Storm at WebMD • Resources
Learning Stream Processing with Apache Storm
Learning Stream Processing with Apache Storm
Eugene Dvorkin
Details of the real time stream processing STORM internal design.
Storm presentation
Storm presentation
Shyam Raj
This presentation gives you more detailed overview of Apache Storm (distributed real time computing system)
Apache Storm Internals
Apache Storm Internals
Humoyun Ahmedov
Given at Supercomputer Education Research Centre, IISc, Bangalore
Storm Real Time Computation
Storm Real Time Computation
Sonal Raj
How the framework Apache Storm works
Storm
Storm
Pouyan Rezazadeh
The talk I gave a while back on the work we did at Yahoo to make Apache Storm a secure multi-tenant hosted service.
Multi-tenant Apache Storm as a service
Multi-tenant Apache Storm as a service
Robert Evans
Apache Storm - A Real-time Processing System
Apache Storm
Apache Storm
masifqadri
Apache Storm based Real Time Analytics for Recommending Trending Topics and Sentiment Analysis on Cloud Compouting Environment
Apache Storm based Real Time Analytics for Recommending Trending Topics and S...
Apache Storm based Real Time Analytics for Recommending Trending Topics and S...
Humoyun Ahmedov
A short comparison of 2 current data streaming technologies
Spark vs storm
Spark vs storm
Trong Ton
Resource Aware Scheduling in Apache Storm
Resource Aware Scheduling in Apache Storm
Resource Aware Scheduling in Apache Storm
DataWorks Summit/Hadoop Summit
using Storm and PHP analysis big data real-time
Analysis big data by use php with storm
Analysis big data by use php with storm
毅 吕
Counters are one of the two core metric types in Prometheus, allowing for tracking of request rates, error ratios and other key measurements. Learn why are they designed the way they are, how client libraries implement them and how rate() works. If you'd like more information about Prometheus, contact us at prometheus@robustperception.io
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Brian Brazil
This slides are for a brief seminar that I give in a Ph.D. exam "Perspective in Parallel Computing" (held by prof. Marco Danelutto) at University of Pisa (Italy). They are a rapid introduction to Apache Storm and how it relates to classical algorithmic skeleton parallel frameworks
Introduction to Apache Storm
Introduction to Apache Storm
Tiziano De Matteis
Introduction to Apache Storm: - Storm Concept: topology, tuple, stream, spout, bolt, stream grouping - Storm Component: Master and Worker - Example: GitHub Commit Feed
Introduction to Apache Storm - Concept & Example
Introduction to Apache Storm - Concept & Example
Dung Ngua
1) Storm is a distributed, real-time computation system. 2) The input stream of a Storm cluster is handled by a component called a spout. The spout passes the data to a bolt, a bolt either persists the data in some sort of storage, or passes it to some other bolt. You can imagine a Storm cluster as a chain of bolt components that each make some kind of transformation on the data exposed by the spout. 1) Real-time systems must guarantee the data processing. 2) And also it should be horizontally scalable, means, just adding few nodes to improve the scalability of a cluster. 3) It should be fault-tolerance, means, if any error occurs or any node goes down, our system should work without any hesitation. 4) We need to get rid of all the intermediate message brokers, because they are complex, and slow, because, instead of sending messages directly from producer to consumers, it has to go through third party message brokers, moreover, those third party message brokers are persist the input data into the disk. This whole process will consume extra time to process the data. 5) In comparison with Storm, Hadoop is ok, because Hadoop also provides a high latency system, so if you take a few hours of down time, you still have high latency, but in real time systems, if you take few hours of down time. Then you no longer in real time, which means robustness requirements, are much harder. Storm satisfies all those properties without any hesitation. 1) Both Hadoop and Storm are distributed and fault-Tolerance systems, but, Hadoop mainly used for batch processing systems, whereas Storm used for Real-time computation systems. 2) Storm doesn’t have inbuilt Storage system, it mainly builds on “come and get some” strategy. In other side, Hadoop have HDFS as storage file system. 1) Both Storm and Flume used for real-time data processing, but Flume will not give you real-time computation systems. moreover flume depends on channel Message broker component, for, guaranteed data processing, here, channel always persist the data before sending it to Consumer, but for Storm, there is no intermediate message brokers concept, it Just Works like as lite as possible. Whatever business logic that you want to write, will goes under Bolt component of Storm.
Apache Storm and twitter Streaming API integration
Apache Storm and twitter Streaming API integration
Uday Vakalapudi
Slides from talk given at the NYC Cassandra Meetup. Discussing how Storm works and how it integrates well with Apache Cassandra. There is also a segway into a example project that uses Storm and Cassandra to implement a scalable reactive web crawler. http://github.com/tjake/stormscraper
Storm and Cassandra
Storm and Cassandra
T Jake Luciani
Created by Nathan Marz at Twitter, Storm promises to help companies augment their batch-based big data processing systems with real-time computation.
Storm: The Real-Time Layer - GlueCon 2012
Storm: The Real-Time Layer - GlueCon 2012
Dan Lynn
Apache Spark - A Real-time Processing Tool
Apache Spark
Apache Spark
masifqadri
Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it’s fast — you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Storm was open-sourced by Twitter in September of 2011 and has since been adopted by many companies around the world. Storm has a wide range of use cases, from stream processing to continuous computation to distributed RPC. In this talk I'll introduce Storm and show how easy it is to use for realtime computation.
Jan 2012 HUG: Storm
Jan 2012 HUG: Storm
Yahoo Developer Network
Presented by Matt Jacobs, Edge Platform engineer at Netflix, during DevNexus 2016 conference in Atlanta
Using Hystrix to Build Resilient Distributed Systems
Using Hystrix to Build Resilient Distributed Systems
Matt Jacobs
adoop plays a central role for Yahoo! to provide personalized experiences for our users and create value for our advertisers. In this talk, we will discuss the convergence of low-latency processing and Hadoop platform. To enable the convergence, we have developed Storm-on-YARN to enable Storm streaming/microbatch applications and Hadoop batch applications hosted in a single cluster. Storm applications could leverage YARN for resource management, and apply Hadoop style security to Hadoop datasets on HDFS and HBase. In Storm-on-YARN, YARN is used to launch Storm application master (Nimbus), and enable Nimbus to request resources for Storm workers (Supervisors). YARN resource manager and Storm scheduler work together to support multi-tenancy and high availability. HDFS enables Storm to achieve higher availability of Nimbus itself. We are introducing Hadoop style security into Storm through JAAS authentication (Kerberos and Digest). Storm servers (Nimbus and DRPC) will be configured with authorization plugins for access control and audit. The security context enables Storm applications to access authorized datasets only (including those created by Hadoop applications). Yahoo! is making our contribution on Storm and YARN available as open source. We will work with industry partners to foster the convergence of low-latency processing and big-data.
Storm-on-YARN: Convergence of Low-Latency and Big-Data
Storm-on-YARN: Convergence of Low-Latency and Big-Data
DataWorks Summit
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Taewoo Kim
Presentation to a combined meetup of Bay Area Lisp and Bay Area Clojure groups. Presented three Clojure projects at BackType: Cascalog - Batch processing in Clojure ElephantDB - Database written in Clojure Storm - Distributed, fault-tolerant, reliable stream processing and RPC
Clojure at BackType
Clojure at BackType
nathanmarz
A commissioned study conducted by Forrester Consulting on behalf of EnterpriseDB, published in January 2015, presents a case study for the evolution of relational database management systems. The study, Relational Databases are Evolving to Support New Data Capabilities, found that the majority—78%—of database decisions makers wanted one solution that could handle relational and NoSQL data types. The study finds that relational databases are evolving to address the needs of end users seeking to link unstructured and structured data types and that decision makers should look to invest in these solutions. EDB’s Postgres Plus Advanced Server, for example, addresses these needs with such capabilities as support for unstructured data types, non-durable tables, tools for large-scale data loads, and integration technologies that connect standalone NoSQL solutions with Postgres.
Relational Databases are Evolving To Support New Data Capabilities
Relational Databases are Evolving To Support New Data Capabilities
EDB
More Related Content
What's hot
This presentation gives you more detailed overview of Apache Storm (distributed real time computing system)
Apache Storm Internals
Apache Storm Internals
Humoyun Ahmedov
Given at Supercomputer Education Research Centre, IISc, Bangalore
Storm Real Time Computation
Storm Real Time Computation
Sonal Raj
How the framework Apache Storm works
Storm
Storm
Pouyan Rezazadeh
The talk I gave a while back on the work we did at Yahoo to make Apache Storm a secure multi-tenant hosted service.
Multi-tenant Apache Storm as a service
Multi-tenant Apache Storm as a service
Robert Evans
Apache Storm - A Real-time Processing System
Apache Storm
Apache Storm
masifqadri
Apache Storm based Real Time Analytics for Recommending Trending Topics and Sentiment Analysis on Cloud Compouting Environment
Apache Storm based Real Time Analytics for Recommending Trending Topics and S...
Apache Storm based Real Time Analytics for Recommending Trending Topics and S...
Humoyun Ahmedov
A short comparison of 2 current data streaming technologies
Spark vs storm
Spark vs storm
Trong Ton
Resource Aware Scheduling in Apache Storm
Resource Aware Scheduling in Apache Storm
Resource Aware Scheduling in Apache Storm
DataWorks Summit/Hadoop Summit
using Storm and PHP analysis big data real-time
Analysis big data by use php with storm
Analysis big data by use php with storm
毅 吕
Counters are one of the two core metric types in Prometheus, allowing for tracking of request rates, error ratios and other key measurements. Learn why are they designed the way they are, how client libraries implement them and how rate() works. If you'd like more information about Prometheus, contact us at prometheus@robustperception.io
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Brian Brazil
This slides are for a brief seminar that I give in a Ph.D. exam "Perspective in Parallel Computing" (held by prof. Marco Danelutto) at University of Pisa (Italy). They are a rapid introduction to Apache Storm and how it relates to classical algorithmic skeleton parallel frameworks
Introduction to Apache Storm
Introduction to Apache Storm
Tiziano De Matteis
Introduction to Apache Storm: - Storm Concept: topology, tuple, stream, spout, bolt, stream grouping - Storm Component: Master and Worker - Example: GitHub Commit Feed
Introduction to Apache Storm - Concept & Example
Introduction to Apache Storm - Concept & Example
Dung Ngua
1) Storm is a distributed, real-time computation system. 2) The input stream of a Storm cluster is handled by a component called a spout. The spout passes the data to a bolt, a bolt either persists the data in some sort of storage, or passes it to some other bolt. You can imagine a Storm cluster as a chain of bolt components that each make some kind of transformation on the data exposed by the spout. 1) Real-time systems must guarantee the data processing. 2) And also it should be horizontally scalable, means, just adding few nodes to improve the scalability of a cluster. 3) It should be fault-tolerance, means, if any error occurs or any node goes down, our system should work without any hesitation. 4) We need to get rid of all the intermediate message brokers, because they are complex, and slow, because, instead of sending messages directly from producer to consumers, it has to go through third party message brokers, moreover, those third party message brokers are persist the input data into the disk. This whole process will consume extra time to process the data. 5) In comparison with Storm, Hadoop is ok, because Hadoop also provides a high latency system, so if you take a few hours of down time, you still have high latency, but in real time systems, if you take few hours of down time. Then you no longer in real time, which means robustness requirements, are much harder. Storm satisfies all those properties without any hesitation. 1) Both Hadoop and Storm are distributed and fault-Tolerance systems, but, Hadoop mainly used for batch processing systems, whereas Storm used for Real-time computation systems. 2) Storm doesn’t have inbuilt Storage system, it mainly builds on “come and get some” strategy. In other side, Hadoop have HDFS as storage file system. 1) Both Storm and Flume used for real-time data processing, but Flume will not give you real-time computation systems. moreover flume depends on channel Message broker component, for, guaranteed data processing, here, channel always persist the data before sending it to Consumer, but for Storm, there is no intermediate message brokers concept, it Just Works like as lite as possible. Whatever business logic that you want to write, will goes under Bolt component of Storm.
Apache Storm and twitter Streaming API integration
Apache Storm and twitter Streaming API integration
Uday Vakalapudi
Slides from talk given at the NYC Cassandra Meetup. Discussing how Storm works and how it integrates well with Apache Cassandra. There is also a segway into a example project that uses Storm and Cassandra to implement a scalable reactive web crawler. http://github.com/tjake/stormscraper
Storm and Cassandra
Storm and Cassandra
T Jake Luciani
Created by Nathan Marz at Twitter, Storm promises to help companies augment their batch-based big data processing systems with real-time computation.
Storm: The Real-Time Layer - GlueCon 2012
Storm: The Real-Time Layer - GlueCon 2012
Dan Lynn
Apache Spark - A Real-time Processing Tool
Apache Spark
Apache Spark
masifqadri
Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it’s fast — you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Storm was open-sourced by Twitter in September of 2011 and has since been adopted by many companies around the world. Storm has a wide range of use cases, from stream processing to continuous computation to distributed RPC. In this talk I'll introduce Storm and show how easy it is to use for realtime computation.
Jan 2012 HUG: Storm
Jan 2012 HUG: Storm
Yahoo Developer Network
Presented by Matt Jacobs, Edge Platform engineer at Netflix, during DevNexus 2016 conference in Atlanta
Using Hystrix to Build Resilient Distributed Systems
Using Hystrix to Build Resilient Distributed Systems
Matt Jacobs
adoop plays a central role for Yahoo! to provide personalized experiences for our users and create value for our advertisers. In this talk, we will discuss the convergence of low-latency processing and Hadoop platform. To enable the convergence, we have developed Storm-on-YARN to enable Storm streaming/microbatch applications and Hadoop batch applications hosted in a single cluster. Storm applications could leverage YARN for resource management, and apply Hadoop style security to Hadoop datasets on HDFS and HBase. In Storm-on-YARN, YARN is used to launch Storm application master (Nimbus), and enable Nimbus to request resources for Storm workers (Supervisors). YARN resource manager and Storm scheduler work together to support multi-tenancy and high availability. HDFS enables Storm to achieve higher availability of Nimbus itself. We are introducing Hadoop style security into Storm through JAAS authentication (Kerberos and Digest). Storm servers (Nimbus and DRPC) will be configured with authorization plugins for access control and audit. The security context enables Storm applications to access authorized datasets only (including those created by Hadoop applications). Yahoo! is making our contribution on Storm and YARN available as open source. We will work with industry partners to foster the convergence of low-latency processing and big-data.
Storm-on-YARN: Convergence of Low-Latency and Big-Data
Storm-on-YARN: Convergence of Low-Latency and Big-Data
DataWorks Summit
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Taewoo Kim
What's hot
(20)
Apache Storm Internals
Apache Storm Internals
Storm Real Time Computation
Storm Real Time Computation
Storm
Storm
Multi-tenant Apache Storm as a service
Multi-tenant Apache Storm as a service
Apache Storm
Apache Storm
Apache Storm based Real Time Analytics for Recommending Trending Topics and S...
Apache Storm based Real Time Analytics for Recommending Trending Topics and S...
Spark vs storm
Spark vs storm
Resource Aware Scheduling in Apache Storm
Resource Aware Scheduling in Apache Storm
Analysis big data by use php with storm
Analysis big data by use php with storm
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Introduction to Apache Storm
Introduction to Apache Storm
Introduction to Apache Storm - Concept & Example
Introduction to Apache Storm - Concept & Example
Apache Storm and twitter Streaming API integration
Apache Storm and twitter Streaming API integration
Storm and Cassandra
Storm and Cassandra
Storm: The Real-Time Layer - GlueCon 2012
Storm: The Real-Time Layer - GlueCon 2012
Apache Spark
Apache Spark
Jan 2012 HUG: Storm
Jan 2012 HUG: Storm
Using Hystrix to Build Resilient Distributed Systems
Using Hystrix to Build Resilient Distributed Systems
Storm-on-YARN: Convergence of Low-Latency and Big-Data
Storm-on-YARN: Convergence of Low-Latency and Big-Data
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Viewers also liked
Presentation to a combined meetup of Bay Area Lisp and Bay Area Clojure groups. Presented three Clojure projects at BackType: Cascalog - Batch processing in Clojure ElephantDB - Database written in Clojure Storm - Distributed, fault-tolerant, reliable stream processing and RPC
Clojure at BackType
Clojure at BackType
nathanmarz
A commissioned study conducted by Forrester Consulting on behalf of EnterpriseDB, published in January 2015, presents a case study for the evolution of relational database management systems. The study, Relational Databases are Evolving to Support New Data Capabilities, found that the majority—78%—of database decisions makers wanted one solution that could handle relational and NoSQL data types. The study finds that relational databases are evolving to address the needs of end users seeking to link unstructured and structured data types and that decision makers should look to invest in these solutions. EDB’s Postgres Plus Advanced Server, for example, addresses these needs with such capabilities as support for unstructured data types, non-durable tables, tools for large-scale data loads, and integration technologies that connect standalone NoSQL solutions with Postgres.
Relational Databases are Evolving To Support New Data Capabilities
Relational Databases are Evolving To Support New Data Capabilities
EDB
Watch the full video at: https://skillsmatter.com/skillscasts/6100-scala-abide-a-lint-tool-for-scala Recently there's been a flurry of compiler plugins aimed at finding potential errors, or forbidding certain patterns, in Scala: Linter and its forks, Wart Remover, ScalaStyle. [Abide](https://github.com/scala/scala-abide) aims at providing a common frame for all such efforts. Abide integrates with Sbt, IDEs (via compiler plugins) and soon with Maven. Users can add project-specific rules, and additional rule libraries can be imported from any ivy or maven repository. Rules have access to the fully type-checked tree and may use quasiquotes for easy AST pattern matching.
Scala Abide: A lint tool for Scala
Scala Abide: A lint tool for Scala
Iulian Dragos
Gordon Rowell's talk "Puppet at Google" from Puppet Camp Sydney 2013.
Puppet at Google
Puppet at Google
Puppet
Apache Spark - Frequently asked questions
Why Spark?
Why Spark?
Álvaro Agea Herradón
Have you heard that all in-memory databases are equally fast but unreliable, inconsistent and expensive? This session highlights in-memory technology that busts all those myths. Redis, the fastest database on the planet, is not a simply in-memory key-value data-store; but rather a rich in-memory data-structure engine that serves the world’s most popular apps. Redis Labs’ unique clustering technology enables Redis to be highly reliable, keeping every data byte intact despite hundreds of cloud instance failures and dozens of complete data-center outages. It delivers full CP system characteristics at high performance. And with the latest Redis on Flash technology, Redis Labs achieves close to in-memory performance at 70% lower operational costs. Learn about the best uses of in-memory computing to accelerate everyday applications such as high volume transactions, real time analytics, IoT data ingestion and more.
IMCSummit 2015 - Day 2 IT Business Track - 4 Myths about In-Memory Databases ...
IMCSummit 2015 - Day 2 IT Business Track - 4 Myths about In-Memory Databases ...
In-Memory Computing Summit
Need for Async - version for ScalaWorld
The Need for Async @ ScalaWorld
The Need for Async @ ScalaWorld
Konrad Malawski
Streaming Big Data: Delivering Meaning In Near-Real Time At High Velocity At Massive Scale with Apache Spark, Apache Kafka, Apache Cassandra, Akka and the Spark Cassandra Connector. Why this pairing of technologies and How easy it is to implement. Example application: https://github.com/killrweather/killrweather
Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with A...
Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with A...
Helena Edelson
Slides of my talk for Scala NSK Usergroup. Video in Russian: http://www.youtube.com/watch?v=fWnaW3CP7OI
Purely Functional Data Structures in Scala
Purely Functional Data Structures in Scala
Vladimir Kostyukov
Monadic Java
Monadic Java
Mario Fusco
NewSQL overview: - History of RDBMs - The reasons why NoSQL concept appeared - Why NoSQL was not enough, the necessity of NewSQL - Characteristics of NewSQL - 7 DBs that belongs to NewSQL - Overview Table with main properties
NewSQL overview, Feb 2015
NewSQL overview, Feb 2015
Ivan Glushkov
The new Actor representation in Akka Typed allows formulations that lend themselves to monadic interpretation or introspection. This leads us to explore possibilities for expressing and verifying dynamic properties like the adherence to a communication protocol between multiple agents as well as the safety properties of that protocol on a global level. Academic research in this area is far from complete, but there are interesting initial results that we explore in this session: precisely how much purity and reasoning can we bring to the distributed world?
The Newest in Session Types
The Newest in Session Types
Roland Kuhn
Scala Days Keynote
Scala Days San Francisco
Scala Days San Francisco
Martin Odersky
This paper, written by the LinkedIn Espresso Team, appeared at the ACM SIGMOD/PODS Conference (June 2013). To see the talk given by Swaroop Jagadish (Staff Software Engineer @ LinkedIn), go here: http://www.slideshare.net/amywtang/li-espresso-sigmodtalk
Espresso: LinkedIn's Distributed Data Serving Platform (Paper)
Espresso: LinkedIn's Distributed Data Serving Platform (Paper)
Amy W. Tang
(video of these slides available here http://fsharpforfunandprofit.com/fppatterns/) In object-oriented development, we are all familiar with design patterns such as the Strategy pattern and Decorator pattern, and design principles such as SOLID. The functional programming community has design patterns and principles as well. This talk will provide an overview of some of these, and present some demonstrations of FP design in practice.
Functional Programming Patterns (BuildStuff '14)
Functional Programming Patterns (BuildStuff '14)
Scott Wlaschin
Scala Days 2013, New York
Concurrency: The Good, The Bad and The Ugly
Concurrency: The Good, The Bad and The Ugly
legendofklang
Viewers also liked
(16)
Clojure at BackType
Clojure at BackType
Relational Databases are Evolving To Support New Data Capabilities
Relational Databases are Evolving To Support New Data Capabilities
Scala Abide: A lint tool for Scala
Scala Abide: A lint tool for Scala
Puppet at Google
Puppet at Google
Why Spark?
Why Spark?
IMCSummit 2015 - Day 2 IT Business Track - 4 Myths about In-Memory Databases ...
IMCSummit 2015 - Day 2 IT Business Track - 4 Myths about In-Memory Databases ...
The Need for Async @ ScalaWorld
The Need for Async @ ScalaWorld
Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with A...
Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with A...
Purely Functional Data Structures in Scala
Purely Functional Data Structures in Scala
Monadic Java
Monadic Java
NewSQL overview, Feb 2015
NewSQL overview, Feb 2015
The Newest in Session Types
The Newest in Session Types
Scala Days San Francisco
Scala Days San Francisco
Espresso: LinkedIn's Distributed Data Serving Platform (Paper)
Espresso: LinkedIn's Distributed Data Serving Platform (Paper)
Functional Programming Patterns (BuildStuff '14)
Functional Programming Patterns (BuildStuff '14)
Concurrency: The Good, The Bad and The Ugly
Concurrency: The Good, The Bad and The Ugly
Similar to Your Code is Wrong
I created the baker's dozen of things to think about when migrating or deploying in AWS. Use comments to add your input. Read time approx. 15-20 minutes max. There is also a long form written version of this on https://blog.lacework.com.
Security for AWS : Journey to Least Privilege (update)
Security for AWS : Journey to Least Privilege (update)
dhubbard858
A baker's dozen of top items to consider when migrating or deploying in AWS.
Security for AWS: Journey to Least Privilege
Security for AWS: Journey to Least Privilege
Lacework
The goal of Skynet is to avoid human doing repetitive things and make a system doing them in a better way. System automation should be the way to go for any system management so that human can focus on stuff that really matters. Related blog post for more informations https://engineering.linkedin.com/slideshare/skynet-project-_-monitor-scale-and-auto-heal-system-cloud
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Sylvain Kalache
This talk looks at the evolution of monitoring over time, the ways in which you can approach monitoring, where Prometheus fit into all this, and how Prometheus itself has grown over time.
Evolution of Monitoring and Prometheus (Dublin 2018)
Evolution of Monitoring and Prometheus (Dublin 2018)
Brian Brazil
Presented at NULL Hyderabad
Hacking android apps by srini0x00
Hacking android apps by srini0x00
srini0x00
There is often a considerable delay between the discovery of a vulnerability and the issue of a patch. One mitigation strategy for this window of vulnerability is to use a configuration workaround, which prevents the vulnerable code from being executed at the cost of some lost functionality -- but if one is available. Since application configurations are not specifically designed to mitigate software vulnerabilities, we find that they only cover 25.2% of vulnerabilities. To minimize patch delay vulnerabilities and address the limitations of configuration workarounds, we propose Security Workarounds for Rapid Response (SWRRs), which are designed to neutralize security vulnerabilities in a timely, secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs neutralize vulnerabilities by preventing vulnerable code from being executed at the cost of some lost functionality. However, the key difference is that SWRRs use existing error-handling code within applications, which enables them to be mechanically inserted with minimal knowledge of the application and minimal developer effort. This allows SWRRs to achieve high coverage while still being fast and easy to deploy. We designed and implemented Talos, a system that mechanically instrument SWRRs into a given application, and evaluate it on five popular Linux server applications. We run exploits against 11 real-world software vulnerabilities and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can neutralize 75.1% of all potential vulnerabilities and incur a loss of functionality similar to configuration workarounds in 71.3% of those cases. Our overall conclusion is that automatically generated SWRRs can safely mitigate 2.1x times more vulnerabilities, while only incurring a loss of functionality comparable to that of traditional configuration workarounds.
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...
Zhen Huang
Often what you monitor and get alerted on is defined by your tools, rather than what makes the most sense to you and your organisation. Alerts on metrics such as CPU usage which are noisy and rarely spot real problems, while outages go undetected. Monitoring systems can also be challenging to maintain, and overall provide a poor return on investment. In the past few years several new monitoring systems have appeared with more powerful semantics and which are easier to run, which offer a way to vastly improve how your organisation operates and prepare you for a Cloud Native environment. Prometheus is one such system. This talk will look at the monitoring ideal and how whitebox monitoring with a time series database, multi-dimensional labels and a powerful querying/alerting language can free you from midnight pages.
An Introduction to Prometheus (GrafanaCon 2016)
An Introduction to Prometheus (GrafanaCon 2016)
Brian Brazil
https://irjet.net/archives/V4/i2/IRJET-V4I2261.pdf
Online java compiler with security editor
Online java compiler with security editor
IRJET Journal
This is an in-depth guide on how to do excel-like row selection in jQuery DataTable. In the end, you'll master row selection.
How To Do Excel-Like Row Selection in jQuery DataTable?
How To Do Excel-Like Row Selection in jQuery DataTable?
Polyxer Systems
A great research on what is vulnerable on the net
Internet census 2012
Internet census 2012
Giuliano Tavaroli
Our technology, work processes, and activities all are depend based on Operation Systems to be safe and secure. Join us virtually for our upcoming "The Hacking Games - Operation System Vulnerabilities" Meetup to learn how hacker can compromise Operation System, bypass AntiVirus protection layer and exploiting Linux eBPF.
The Hacking Games - Operation System Vulnerabilities Meetup 29112022
The Hacking Games - Operation System Vulnerabilities Meetup 29112022
lior mazor
http://www.iosrjournals.org/iosr-jce/pages/v13i1.html
Procuring the Anomaly Packets and Accountability Detection in the Network
Procuring the Anomaly Packets and Accountability Detection in the Network
IOSR Journals
Instrument production applications (both in AWS and on prem) with x-ray to collect live telemetry and latency metrics on your applications. You can also use it to debug live!
Deep Dive: AWS X-Ray London Summit 2017
Deep Dive: AWS X-Ray London Summit 2017
Randall Hunt
Often what you monitor and get alerted on is defined by your tools, rather than what makes the most sense to you and your organisation. Alerts on metrics such as CPU usage which are noisy and rarely spot real problems, while outages go undetected. Monitoring systems can also be challenging to maintain, and overall provide a poor return on investment. In the past few years several new monitoring systems have appeared with more powerful semantics and which are easier to run, which offer a way to vastly improve how your organisation operates Prometheus is one such system. This talk will look at the monitoring ideal and how whitebox monitoring with a time series database, multi-dimensional labels and a powerful querying/alerting language can free you from midnight pages.
Monitoring What Matters: The Prometheus Approach to Whitebox Monitoring (Berl...
Monitoring What Matters: The Prometheus Approach to Whitebox Monitoring (Berl...
Brian Brazil
f you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
Time Series Anomaly Detection with Azure and .NETT
Time Series Anomaly Detection with Azure and .NETT
Marco Parenzan
A summary of server-side JavaScript weaknesses
Node.js security tour
Node.js security tour
Giacomo De Liberali
A birthmark is a set of characteristic possessed by a program that uniquely recognizes a program. Birthmark of the software is based on Heap Graph. It is generated by using Google Chrome Developer Tools when the program is in execution. Software’s behavioural structure is demonstrated in the heap graph. It describes how the objects are related to each other to deliver the desired functionality of the website. Our aim is to develop and evaluate a system that can find theft/similarity between websites by using Agglomerative Clustering and Improved Frequent Subgraph Mining. To identify if a website is using the original program’s code or its module, birthmark of the original program is explored in the suspected program’s heap graph.
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Swati Patel
This is an interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD) tool intended for verifying parallel applications. In this article you will learn about the history of creating RRD, its basic abilities and also about some other similar tools and the way they differ from RRD.
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
PVS-Studio
Aspects to check on security in php
Secure programming with php
Secure programming with php
Mohmad Feroz
Based on Anna University Syllabus.
Information Management 2marks with answer
Information Management 2marks with answer
suchi2480
Similar to Your Code is Wrong
(20)
Security for AWS : Journey to Least Privilege (update)
Security for AWS : Journey to Least Privilege (update)
Security for AWS: Journey to Least Privilege
Security for AWS: Journey to Least Privilege
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Evolution of Monitoring and Prometheus (Dublin 2018)
Evolution of Monitoring and Prometheus (Dublin 2018)
Hacking android apps by srini0x00
Hacking android apps by srini0x00
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...
An Introduction to Prometheus (GrafanaCon 2016)
An Introduction to Prometheus (GrafanaCon 2016)
Online java compiler with security editor
Online java compiler with security editor
How To Do Excel-Like Row Selection in jQuery DataTable?
How To Do Excel-Like Row Selection in jQuery DataTable?
Internet census 2012
Internet census 2012
The Hacking Games - Operation System Vulnerabilities Meetup 29112022
The Hacking Games - Operation System Vulnerabilities Meetup 29112022
Procuring the Anomaly Packets and Accountability Detection in the Network
Procuring the Anomaly Packets and Accountability Detection in the Network
Deep Dive: AWS X-Ray London Summit 2017
Deep Dive: AWS X-Ray London Summit 2017
Monitoring What Matters: The Prometheus Approach to Whitebox Monitoring (Berl...
Monitoring What Matters: The Prometheus Approach to Whitebox Monitoring (Berl...
Time Series Anomaly Detection with Azure and .NETT
Time Series Anomaly Detection with Azure and .NETT
Node.js security tour
Node.js security tour
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
Secure programming with php
Secure programming with php
Information Management 2marks with answer
Information Management 2marks with answer
More from nathanmarz
Talk given in NYC on 7/20/2015
Demystifying Data Engineering
Demystifying Data Engineering
nathanmarz
Runaway complexity in Big Data... and a plan to stop it
Runaway complexity in Big Data... and a plan to stop it
nathanmarz
Storm: distributed and fault-tolerant realtime computation
Storm: distributed and fault-tolerant realtime computation
nathanmarz
ElephantDB
ElephantDB
nathanmarz
How BackType does a lot with a little. Presented at POSSCON ’11.
Become Efficient or Die: The Story of BackType
Become Efficient or Die: The Story of BackType
nathanmarz
The architectural principles behind building systems that scale to vast amounts of data and operate on that data in realtime. Presented at POSSCON '11.
The Secrets of Building Realtime Big Data Systems
The Secrets of Building Realtime Big Data Systems
nathanmarz
Visuals for the Cascalog workshop on February 19th, 2011.
Cascalog workshop
Cascalog workshop
nathanmarz
Presentation of Cascalog at Strange Loop on October 15th, 2010. http://github.com/nathanmarz/cascalog
Cascalog at Strange Loop
Cascalog at Strange Loop
nathanmarz
My talk about Cascalog at Hadoop Day in Seattle.
Cascalog at Hadoop Day
Cascalog at Hadoop Day
nathanmarz
Presentation about Cascalog, a Clojure-based query language for Hadoop.
Cascalog at May Bay Area Hadoop User Group
Cascalog at May Bay Area Hadoop User Group
nathanmarz
Presentation I gave at Bay Area Clojure Meetup Group on May 6th, 2010. Also demoed examples from introductory tutorial: http://nathanmarz.com/blog/introducing-cascalog/
Cascalog
Cascalog
nathanmarz
High level overview of Cascading.
Cascading
Cascading
nathanmarz
More from nathanmarz
(12)
Demystifying Data Engineering
Demystifying Data Engineering
Runaway complexity in Big Data... and a plan to stop it
Runaway complexity in Big Data... and a plan to stop it
Storm: distributed and fault-tolerant realtime computation
Storm: distributed and fault-tolerant realtime computation
ElephantDB
ElephantDB
Become Efficient or Die: The Story of BackType
Become Efficient or Die: The Story of BackType
The Secrets of Building Realtime Big Data Systems
The Secrets of Building Realtime Big Data Systems
Cascalog workshop
Cascalog workshop
Cascalog at Strange Loop
Cascalog at Strange Loop
Cascalog at Hadoop Day
Cascalog at Hadoop Day
Cascalog at May Bay Area Hadoop User Group
Cascalog at May Bay Area Hadoop User Group
Cascalog
Cascalog
Cascading
Cascading
Recently uploaded
BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Delhi Call girls
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
45-60 minute session deck from introducing Google Apps Script to developers, IT leadership, and other technical professionals.
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
wesley chun
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
The Digital Insurer
This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
wesley chun
Slides from the presentation on Machine Learning for the Arts & Humanities seminar at the University of Bologna (Digital Humanities and Digital Knowledge program)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Maria Levchenko
Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024. Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights. Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer. Learn about: - The essence and purpose of taxonomies and ontologies in information and knowledge management; - Advantages of semantic layers leveraging organizational taxonomies; and - Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
Enterprise Knowledge
BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Delhi Call girls
writing some innovation for development and search
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
Stay safe, grab a drink and join us virtually for our upcoming "GenAI Risks & Security" Meetup to hear about how to uncover critical GenAI risks and vulnerabilities, AI security considerations in every company, and how a CISO should navigate through GenAI Risks.
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
lior mazor
BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
Delhi Call girls
Details
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
The Raspberry Pi 5 was announced on October 2023. This new version of the popular embedded device comes with a new iteration of Broadcom’s VideoCore GPU platform, and was released with a fully open source driver stack, developed by Igalia. The presentation will discuss some of the major changes required to support this new Video Core iteration, the challenges we faced in the process and the solutions we provided in order to deliver conformant OpenGL ES and Vulkan drivers. The talk will also cover the next steps for the open source Raspberry Pi 5 graphics stack. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://eoss24.sched.com/event/1aBEx
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Igalia
With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
Digital Global Overview Report 2024 Slides presentation for Event presented in 2024 after compilation of data around last year.
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
hans926745
These are the slides delivered in a workshop at Data Innovation Summit Stockholm April 2024, by Kristof Neys and Jonas El Reweny.
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Neo4j
Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
Explore the leading Large Language Models (LLMs) and their capabilities with a comprehensive evaluation. Dive into their performance, architecture, and applications to gain insights into the state-of-the-art in natural language processing. Discover which LLM best suits your needs and stay ahead in the world of AI-driven language understanding.
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
ChristopherTHyatt
Recently uploaded
(20)
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
Your Code is Wrong
1.
Your Code is
Wrong Nathan Marz @nathanmarz 1
2.
Let’s start with
an example
3.
Storm’s “reportError” method
4.
(Storm is a
realtime computation system, like Hadoop but for realtime)
5.
Storm architecture
6.
Storm architecture Master node
(similar to Hadoop JobTracker)
7.
Storm architecture Used for
cluster coordination
8.
Storm architecture Run worker
processes
9.
Storm’s “reportError” method
10.
Used to show
errors in the Storm UI
11.
Error info is
stored in Zookeeper
12.
What happens when
a user deploys code like this?
13.
Denial-of-service on Zookeeper and
cluster goes down
14.
Robust! Designed input space
Actual input space
15.
Your code is
wrong
16.
Your code is
literally wrong
17.
Your code is
wrong
18.
19.
Why do you
believe your code is correct?
20.
Your code Dependency 1 Dependency
2 Dependency 3
21.
Dependency 1 Dependency 4 Dependency
5
22.
Dependency 4 Dependency 6 Dependency
9 Dependency 7 Dependency 8
23.
Dependency 3,000,000 Hardware
24.
Electronics
25.
Chemistry
26.
Atomic physics
27.
Quantum mechanics
28.
I think I
can safely say that nobody understands quantum mechanics. Richard Feynman
29.
Your code is
wrong
30.
Your code ...
31.
All the software
you’ve used has had bugs in it
32.
Including the software you’ve
written
33.
Your code is sometimes
correct
34.
That’s good enough!
35.
36.
Treat code as
nondeterministic
37.
Embrace “your code
is wrong” to design better software
38.
Robust! Designed input space
Actual input space
39.
Robust! Designed input space
Actual input space
40.
An example
41.
Learning from Hadoop Jobtracker Job Job Job
42.
Learning from Hadoop Jobtracker Job Job Job
43.
Learning from Hadoop Jobtracker Job Job Job
44.
Your code is
wrong
45.
So your processes
will crash
46.
Storm’s daemons are process
fault-tolerant
47.
Storm Nimbus Topology Topology Topology
48.
Storm Nimbus Topology Topology Topology
49.
Storm Nimbus Topology Topology Topology
50.
Storm Nimbus Topology Topology Topology
51.
Storm Nimbus Topology Topology Topology
52.
Robust! Designed input space
Actual input space
53.
Robust! Designed input space
Actual input space
54.
The impact of
code being wrong
55.
Robust! Designed input space
Actual input space Failures! Bad performance! Security holes! Irrelevant!
56.
Design principle #1 Measuring
and monitoring are the foundation of solid engineering
57.
Measuring: Under what range
of inputs does my software function well?
58.
Monitoring: What’s the actual
input space of my software?
59.
Measure & Monitor Latency Throughput Stack
traces Buffer sizes Memory usage CPU usage #threads spawned ...
60.
How you monitor
your software is as important as its functionality
61.
Design principle #2 Embrace
immutability
62.
Read/write database Application
63.
MySQLApplication
64.
MongoDBApplication
65.
RiakApplication
66.
CassandraApplication
67.
HBaseApplication
68.
Your code is
wrong
69.
So data will
be corrupted
70.
And you may
not know why
71.
Views Immutable, ever-growing data Application Architecture based on
immutability
72.
Views Immutable, ever-growing data Application Lambda architecture
73.
Design principle #3 Minimize
dependencies
74.
The less that
can go wrong, the less that will go wrong
75.
Example: Storm’s usage of
Zookeeper
76.
Worker locations stored
in Zookeeper
77.
All workers must
know locations of other workers to send messages
78.
Two ways to
get location updates
79.
1. Poll Zookeeper Worker
Zookeeper
80.
2. Use Zookeeper
“watch” feature to get push notifications Worker Zookeeper
81.
Method 2 is
faster but relies on another feature
82.
Storm uses both
methods Worker Zookeeper
83.
If watch feature
fails, locations still propagate via polling
84.
Eliminating dependence justified by
small amount of code required
85.
Design principle #4 Explicitly
respect functional input ranges
86.
Storm’s “reportError” method
87.
Implement self-throttling to avoid
overloading other systems
88.
Design principle #5 Embrace
recomputation
89.
“Your code is
wrong” meanings 1. Design input space differs from actual input space 2. The logic of your code is wrong 3. Requirements are constantly changing
90.
You must be
able to change your code to match shifting requirements
91.
Example: blogging software
92.
New requirement: search
93.
Have to build
a search index
94.
95.
Recomputation gives you so
much more
96.
Views Immutable, ever-growing data Application
97.
Building software no
different than any other engineering
98.
The underlying challenges are
the same
99.
100.
101.
What will break
it?
102.
What are limits
of my dependencies?
103.
How can I
add redundancy to increase robustness?
104.
Can I isolate
failures?
105.
Our raw materials
are ideas instead of matter
106.
Thank you
Download now