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My keynote at GOTO Berlin 2013
The Epistemology of Software Engineering
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Presented at Data Day Texas on January 10th, 2015
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Talk given to Storm NYC meetup group on 3/18/2015
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My presentation of Storm at the Bay Area Hadoop User Group on January 18th, 2012.
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Storm: Distributed and fault tolerant realtime computation
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Introduction to Storm
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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
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Empfohlen
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
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nathanmarz
My presentation of Storm at the Bay Area Hadoop User Group on January 18th, 2012.
Storm
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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
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The talk I gave a while back on the work we did at Yahoo to make Apache Storm a secure multi-tenant hosted service.
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Spark vs storm
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Resource Aware Scheduling in Apache Storm
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Analysis big data by use php with storm
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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
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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
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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
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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
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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
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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
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Yahoo Developer Network
Presented by Matt Jacobs, Edge Platform engineer at Netflix, during DevNexus 2016 conference in Atlanta
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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
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DataWorks Summit
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
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Clojure at BackType
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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
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Introduction to Apache Storm
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Introduction to Apache Storm: - Storm Concept: topology, tuple, stream, spout, bolt, stream grouping - Storm Component: Master and Worker - Example: GitHub Commit Feed
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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
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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
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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
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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
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Using Hystrix to Build Resilient Distributed Systems
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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
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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
Mehr von 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
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Cascading
Cascading
nathanmarz
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Demystifying Data Engineering
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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
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Workshop Build With AI - Google Developers Group Rio Verde
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
Sandro Moreira
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
The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext 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/
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
apidays
The microservices honeymoon is over. When starting a new project or revamping a legacy monolith, teams started looking for alternatives to microservices. The Modular Monolith, or 'Modulith', is an architecture that reaps the benefits of (vertical) functional decoupling without the high costs associated with separate deployments. This talk will delve into the advantages and challenges of this progressive architecture, beginning with exploring the concept of a 'module', its internal structure, public API, and inter-module communication patterns. Supported by spring-modulith, the talk provides practical guidance on addressing the main challenges of a Modultith Architecture: finding and guarding module boundaries, data decoupling, and integration module-testing. You should not miss this talk if you are a software architect or tech lead seeking practical, scalable solutions. About the author With 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 world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
Nanddeep Nachan
Presented by Mike Hicks
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
ThousandEyes
Axa Assurance Maroc - Insurer Innovation Award 2024
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Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s 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 world’s 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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Victor Rentea
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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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AXA XL - Insurer Innovation Award 2024
AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
The Digital Insurer
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
In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
Overkill Security
Passkeys: Developing APIs to enable passwordless authentication Cody Salas, Sr Developer Advocate | Solutions Architect - Yubico 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/
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
apidays
When you’re building (micro)services, you have lots of framework options. Spring Boot is no doubt a popular choice. But there’s more! Take Quarkus, a framework that’s considered the rising star for Kubernetes-native Java. It always depends on what's best for your situation, but how to choose the best solution if you're comparing 2 frameworks? Both Spring Boot and Quarkus have their positives and negatives. Let us compare the two by live coding a couple of common use cases in Spring Boot and Quarkus. After this talk, you’ll be ready to get started with Quarkus yourself, and know when to select Quarkus or Spring Boot.
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Jago de Vreede
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.
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
danishmna97
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 user’s 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 ‘bad calls’ 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
Why Teams call analytics are critical to your entire business
panagenda
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
The Digital Insurer
Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults 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/
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
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[BuildWithAI] Introduction to Gemini.pdf
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Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
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AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
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
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