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My keynote at GOTO Berlin 2013
The Epistemology of Software Engineering
The Epistemology of Software Engineering
nathanmarz
Â
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
Â
Talk given to Storm NYC meetup group on 3/18/2015
The inherent complexity of stream processing
The inherent complexity of stream processing
nathanmarz
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My presentation of Storm at the Bay Area Hadoop User Group on January 18th, 2012.
Storm
Storm
nathanmarz
Â
Storm: Distributed and fault tolerant realtime computation
Storm: Distributed and fault tolerant realtime computation
Ferran GalĂ Reniu
Â
Introduction to Storm
Introduction to Storm
Eugene Dvorkin
Â
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: âą Why use Apache Storm? âą Common use cases âą Storm Architecture - components, concepts, topology âą Building simple Storm topology with Java and Groovy âą Trident and micro-batch processing âą Fault tolerance and guaranteed message delivery âą Running and monitoring Storm in production âą Kafka âą Storm at WebMD âą Resources
Learning Stream Processing with Apache Storm
Learning Stream Processing with Apache Storm
Eugene Dvorkin
Â
Details of the real time stream processing STORM internal design.
Storm presentation
Storm presentation
Shyam Raj
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Empfohlen
My keynote at GOTO Berlin 2013
The Epistemology of Software Engineering
The Epistemology of Software Engineering
nathanmarz
Â
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
Â
Talk given to Storm NYC meetup group on 3/18/2015
The inherent complexity of stream processing
The inherent complexity of stream processing
nathanmarz
Â
My presentation of Storm at the Bay Area Hadoop User Group on January 18th, 2012.
Storm
Storm
nathanmarz
Â
Storm: Distributed and fault tolerant realtime computation
Storm: Distributed and fault tolerant realtime computation
Ferran GalĂ Reniu
Â
Introduction to Storm
Introduction to Storm
Eugene Dvorkin
Â
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: âą Why use Apache Storm? âą Common use cases âą Storm Architecture - components, concepts, topology âą Building simple Storm topology with Java and Groovy âą Trident and micro-batch processing âą Fault tolerance and guaranteed message delivery âą Running and monitoring Storm in production âą Kafka âą Storm at WebMD âą Resources
Learning Stream Processing with Apache Storm
Learning Stream Processing with Apache Storm
Eugene Dvorkin
Â
Details of the real time stream processing STORM internal design.
Storm presentation
Storm presentation
Shyam Raj
Â
This presentation gives you more detailed overview of Apache Storm (distributed real time computing system)
Apache Storm Internals
Apache Storm Internals
Humoyun Ahmedov
Â
Given at Supercomputer Education Research Centre, IISc, Bangalore
Storm Real Time Computation
Storm Real Time Computation
Sonal Raj
Â
How the framework Apache Storm works
Storm
Storm
Pouyan Rezazadeh
Â
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
Â
Apache Storm - A Real-time Processing System
Apache Storm
Apache Storm
masifqadri
Â
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
Â
A short comparison of 2 current data streaming technologies
Spark vs storm
Spark vs storm
Trong Ton
Â
Resource Aware Scheduling in Apache Storm
Resource Aware Scheduling in Apache Storm
Resource Aware Scheduling in Apache Storm
DataWorks Summit/Hadoop Summit
Â
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
æŻ ć
Â
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
Â
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
Â
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
Â
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 doesnât have inbuilt Storage system, it mainly builds on âcome and get someâ 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
Â
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
Â
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
Â
Apache Spark - A Real-time Processing Tool
Apache Spark
Apache Spark
masifqadri
Â
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 itâs fast â 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
Â
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
Â
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
Â
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Taewoo Kim
Â
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
Â
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 majorityâ78%âof 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. EDBâs 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
Â
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Was ist angesagt?
This presentation gives you more detailed overview of Apache Storm (distributed real time computing system)
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Â
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Â
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æŻ ć
Â
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)
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Â
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
Â
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
Â
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 doesnât have inbuilt Storage system, it mainly builds on âcome and get someâ 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
Â
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
Â
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
Â
Apache Spark - A Real-time Processing Tool
Apache Spark
Apache Spark
masifqadri
Â
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 itâs fast â 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
Â
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
Â
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
Â
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
Taewoo Kim
Â
Was ist angesagt?
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Apache Storm Internals
Apache Storm Internals
Â
Storm Real Time Computation
Storm Real Time Computation
Â
Storm
Storm
Â
Multi-tenant Apache Storm as a service
Multi-tenant Apache Storm as a service
Â
Apache Storm
Apache Storm
Â
Apache Storm based Real Time Analytics for Recommending Trending Topics and S...
Apache Storm based Real Time Analytics for Recommending Trending Topics and S...
Â
Spark vs storm
Spark vs storm
Â
Resource Aware Scheduling in Apache Storm
Resource Aware Scheduling in Apache Storm
Â
Analysis big data by use php with storm
Analysis big data by use php with storm
Â
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)
Â
Introduction to Apache Storm
Introduction to Apache Storm
Â
Introduction to Apache Storm - Concept & Example
Introduction to Apache Storm - Concept & Example
Â
Apache Storm and twitter Streaming API integration
Apache Storm and twitter Streaming API integration
Â
Storm and Cassandra
Storm and Cassandra
Â
Storm: The Real-Time Layer - GlueCon 2012
Storm: The Real-Time Layer - GlueCon 2012
Â
Apache Spark
Apache Spark
Â
Jan 2012 HUG: Storm
Jan 2012 HUG: Storm
Â
Using Hystrix to Build Resilient Distributed Systems
Using Hystrix to Build Resilient Distributed Systems
Â
Storm-on-YARN: Convergence of Low-Latency and Big-Data
Storm-on-YARN: Convergence of Low-Latency and Big-Data
Â
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
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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
Â
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 majorityâ78%âof 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. EDBâs 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
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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
Â
Gordon Rowell's talk "Puppet at Google" from Puppet Camp Sydney 2013.
Puppet at Google
Puppet at Google
Puppet
Â
Apache Spark - Frequently asked questions
Why Spark?
Why Spark?
Ălvaro Agea HerradĂłn
Â
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 worldâs most popular apps. Redis Labsâ 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 ...
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Need for Async - version for ScalaWorld
The Need for Async @ ScalaWorld
The Need for Async @ ScalaWorld
Konrad Malawski
Â
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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
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Slides of my talk for Scala NSK Usergroup. Video in Russian: http://www.youtube.com/watch?v=fWnaW3CP7OI
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Purely Functional Data Structures in Scala
Vladimir Kostyukov
Â
Monadic Java
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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
Â
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
Â
Scala Days Keynote
Scala Days San Francisco
Scala Days San Francisco
Martin Odersky
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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
Â
(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
Â
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Concurrency: The Good, The Bad and The Ugly
legendofklang
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Clojure at BackType
Clojure at BackType
Â
Relational Databases are Evolving To Support New Data Capabilities
Relational Databases are Evolving To Support New Data Capabilities
Â
Scala Abide: A lint tool for Scala
Scala Abide: A lint tool for Scala
Â
Puppet at Google
Puppet at Google
Â
Why Spark?
Why Spark?
Â
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 ...
Â
The Need for Async @ ScalaWorld
The Need for Async @ ScalaWorld
Â
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...
Â
Purely Functional Data Structures in Scala
Purely Functional Data Structures in Scala
Â
Monadic Java
Monadic Java
Â
NewSQL overview, Feb 2015
NewSQL overview, Feb 2015
Â
The Newest in Session Types
The Newest in Session Types
Â
Scala Days San Francisco
Scala Days San Francisco
Â
Espresso: LinkedIn's Distributed Data Serving Platform (Paper)
Espresso: LinkedIn's Distributed Data Serving Platform (Paper)
Â
Functional Programming Patterns (BuildStuff '14)
Functional Programming Patterns (BuildStuff '14)
Â
Concurrency: The Good, The Bad and The Ugly
Concurrency: The Good, The Bad and The Ugly
Â
Ăhnlich wie 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
Â
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
Â
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
Â
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
Â
Presented at NULL Hyderabad
Hacking android apps by srini0x00
Hacking android apps by srini0x00
srini0x00
Â
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
Â
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
Â
https://irjet.net/archives/V4/i2/IRJET-V4I2261.pdf
Online java compiler with security editor
Online java compiler with security editor
IRJET Journal
Â
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
Â
A great research on what is vulnerable on the net
Internet census 2012
Internet census 2012
Giuliano Tavaroli
Â
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
Â
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
Â
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
Â
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
Â
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
Â
A summary of server-side JavaScript weaknesses
Node.js security tour
Node.js security tour
Giacomo De Liberali
Â
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. Softwareâs 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 programâs code or its module, birthmark of the original program is explored in the suspected programâs heap graph.
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Swati Patel
Â
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
Â
Aspects to check on security in php
Secure programming with php
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Mohmad Feroz
Â
Based on Anna University Syllabus.
Information Management 2marks with answer
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Â
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Security for AWS : Journey to Least Privilege (update)
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Â
Security for AWS: Journey to Least Privilege
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Â
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Skynet project: Monitor, analyze, scale, and maintain a system in the Cloud
Â
Evolution of Monitoring and Prometheus (Dublin 2018)
Evolution of Monitoring and Prometheus (Dublin 2018)
Â
Hacking android apps by srini0x00
Hacking android apps by srini0x00
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Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...
Â
An Introduction to Prometheus (GrafanaCon 2016)
An Introduction to Prometheus (GrafanaCon 2016)
Â
Online java compiler with security editor
Online java compiler with security editor
Â
How To Do Excel-Like Row Selection in jQuery DataTable?
How To Do Excel-Like Row Selection in jQuery DataTable?
Â
Internet census 2012
Internet census 2012
Â
The Hacking Games - Operation System Vulnerabilities Meetup 29112022
The Hacking Games - Operation System Vulnerabilities Meetup 29112022
Â
Procuring the Anomaly Packets and Accountability Detection in the Network
Procuring the Anomaly Packets and Accountability Detection in the Network
Â
Deep Dive: AWS X-Ray London Summit 2017
Deep Dive: AWS X-Ray London Summit 2017
Â
Monitoring What Matters: The Prometheus Approach to Whitebox Monitoring (Berl...
Monitoring What Matters: The Prometheus Approach to Whitebox Monitoring (Berl...
Â
Time Series Anomaly Detection with Azure and .NETT
Time Series Anomaly Detection with Azure and .NETT
Â
Node.js security tour
Node.js security tour
Â
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Â
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
Â
Secure programming with php
Secure programming with php
Â
Information Management 2marks with answer
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Mehr von nathanmarz
Talk given in NYC on 7/20/2015
Demystifying Data Engineering
Demystifying Data Engineering
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Â
Runaway complexity in Big Data... and a plan to stop it
Runaway complexity in Big Data... and a plan to stop it
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Â
Storm: distributed and fault-tolerant realtime computation
Storm: distributed and fault-tolerant realtime computation
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Â
ElephantDB
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Â
How BackType does a lot with a little. Presented at POSSCON â11.
Become Efficient or Die: The Story of BackType
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Â
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
Â
Visuals for the Cascalog workshop on February 19th, 2011.
Cascalog workshop
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nathanmarz
Â
Presentation of Cascalog at Strange Loop on October 15th, 2010. http://github.com/nathanmarz/cascalog
Cascalog at Strange Loop
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nathanmarz
Â
My talk about Cascalog at Hadoop Day in Seattle.
Cascalog at Hadoop Day
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nathanmarz
Â
Presentation about Cascalog, a Clojure-based query language for Hadoop.
Cascalog at May Bay Area Hadoop User Group
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nathanmarz
Â
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
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Â
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Runaway complexity in Big Data... and a plan to stop it
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Storm: distributed and fault-tolerant realtime computation
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Â
ElephantDB
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Â
Become Efficient or Die: The Story of BackType
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Â
The Secrets of Building Realtime Big Data Systems
The Secrets of Building Realtime Big Data Systems
Â
Cascalog workshop
Cascalog workshop
Â
Cascalog at Strange Loop
Cascalog at Strange Loop
Â
Cascalog at Hadoop Day
Cascalog at Hadoop Day
Â
Cascalog at May Bay Area Hadoop User Group
Cascalog at May Bay Area Hadoop User Group
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Cascalog
Cascalog
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Cascading
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Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. Itâs so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the worldâs largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
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Â
FWD Group - Insurer Innovation Award 2024
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Â
In this keynote, Asanka Abeysinghe, CTO,WSO2 will explore the shift towards platformless technology ecosystems and their importance in driving digital adaptability and innovation. We will discuss strategies for leveraging decentralized architectures and integrating diverse technologies, with a focus on building resilient, flexible, and future-ready IT infrastructures. We will also highlight WSO2's roadmap, emphasizing our commitment to supporting this transformative journey with our evolving product suite.
Platformless Horizons for Digital Adaptability
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WSO2
Â
Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
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TrustArc
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Terragrunt, Terraspace, Terramate, terra... whatever. What is wrong with Terraform so people keep on creating wrappers and solutions around it? How OpenTofu will affect this dynamic? In this presentation, we will look into the fundamental driving forces behind a zoo of wrappers. Moreover, we are going to put together a wrapper ourselves so you can make an educated decision if you need one.
AWS Community Day CPH - Three problems of Terraform
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Â
In this presentation, we delve into leveraging Amazon Q to elevate developer efficiency and craft GenAI applications. Discover the key features and benefits of Amazon Q for streamlined application development. Learn how Amazon Q can revolutionize your development processes and empower you to create cutting-edge GenAI applications.
Elevate Developer Efficiency & build GenAI Application with Amazon Qâ
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The CNIC Information System is a comprehensive database managed by the National Database and Registration Authority (NADRA) of Pakistan. It serves as the primary source of identification for Pakistani citizens and residents, containing vital information such as name, date of birth, address, and biometric data.
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Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the userâs device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that âbad callsâ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.
Why Teams call analytics are critical to your entire business
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panagenda
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Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
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The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
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apidays
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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, weâll explore how RAG could work in languages other than English. Weâll explore French, Chinese, and Polish.
Introduction to Multilingual Retrieval Augmented Generation (RAG)
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Zilliz
Â
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CNIC Information System with Pakdata Cf In Pakistan
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Your Code is Wrong
1.
Your Code is
Wrong Nathan Marz @nathanmarz 1
2.
Letâs start with
an example
3.
Stormâs âreportErrorâ 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.
Stormâs âreportErrorâ 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
youâve used has had bugs in it
32.
Including the software youâve
written
33.
Your code is sometimes
correct
34.
Thatâs good enough!
35.
36.
Treat code as
nondeterministic
37.
Embrace âyour code
is wrongâ 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.
Stormâs 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: Whatâs the actual
input space of my software?
59.
Measure & Monitor Latency Throughput Stack
traces BuïŹer 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: Stormâs 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
âwatchâ feature to get push notiïŹcations 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 justiïŹed by
small amount of code required
85.
Design principle #4 Explicitly
respect functional input ranges
86.
Stormâs âreportErrorâ method
87.
Implement self-throttling to avoid
overloading other systems
88.
Design principle #5 Embrace
recomputation
89.
âYour code is
wrongâ meanings 1. Design input space diïŹers 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
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