Submit Search
Upload
펌웨어추출 기초
•
Download as PPTX, PDF
•
1 like
•
1,007 views
재
재진 장
Follow
펌웨어추출 기초
Read less
Read more
Technology
Report
Share
Report
Share
1 of 14
Download now
Recommended
Conference: dotnetdays 2020 Location: Iaşi, Romania Abstract: Ever had production problems and found that you cannot debug to find the problem? Or that you need to find out where potential issues are coming from in your Azure cloud solution and you have no idea what is happening? Your applications need to be instrumented with logging, tracing and metrics, so you can see what is going on where. In .NET Core logging and tracing are built into the framework. We will have a look at the differences between logging, tracing and instrumentation in general. You will learn how to use .NET Core to implement logging and tracing with best practices, do semantic logging, work with logging factories, trace providers. Also, you will learn how to instrument using Application Insights and add W3C compliant tracing for correlation across cloud resources in a distributed application. Finally, we will put everything together to see how your logs and traces can give a rich way to get insights into your applications and services running in the Azure cloud or container based solutions.
Logging tracing and metrics in .NET Core and Azure - dotnetdays 2020
Logging tracing and metrics in .NET Core and Azure - dotnetdays 2020
Alex Thissen
Azure architecture
Azure architecture
Amal Dev
Hadoop Summit 2015
What it takes to run Hadoop at Scale: Yahoo! Perspectives
What it takes to run Hadoop at Scale: Yahoo! Perspectives
DataWorks Summit
Cf la seconde partie https://www.slideshare.net/lespetitescases/ralisation-dun-mashup-de-donnes-avec-dss-de-dataiku-et-visualisation-avec-palladio-deuxime-partie Tutoriel pour réaliser un mashup à partir de jeux de données libres téléchargés sur data.gouv.fr et Wikidata entre autres avec le logiciel DSS de Dataiku. Après une introduction sur la notion de mashup et des exemples, cette première partie s'intéresse à la préparation de deux jeux de données issues de data.gouv.fr et provenant du Centre national du cinéma. Ce tutoriel a servi de support de cours au Master 2 "Technologies numériques appliqués à l'histoire" de l'Ecole nationale des chartes lors de l'année universitaire 2016-2017.
Réalisation d'un mashup de données avec DSS de Dataiku - Première partie
Réalisation d'un mashup de données avec DSS de Dataiku - Première partie
Gautier Poupeau
This presentation is a part of meetup session delivered in the Microsoft User Group - Chandigarh. In this meetup we looked into how to deploy and manage Virtual Machines in Microsoft Azure cloud. This was an advanced session and targeted more towards IT Pro audience. Developers were welcome also. We covered created virtual machines via ARM template and covered with Virtual Machine Scale Sets with a live demo with Autoscale.
Deep dive into azure virtual machines
Deep dive into azure virtual machines
Jasjit Chopra
The basics of HTTP protocol
Introduction to HTTP protocol
Introduction to HTTP protocol
Aviran Mordo
Join this session to hear from the Photon product and engineering team talk about the latest developments with the project. As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to. Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution. Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
Radical Speed for SQL Queries on Databricks: Photon Under the Hood
Radical Speed for SQL Queries on Databricks: Photon Under the Hood
Databricks
Pour accéder aux fichiers nécessaires pour faire ce TP, visitez: https://drive.google.com/folderview?id=0Bz7DokLRQvx7M2JWZEt1VHdwSE0&usp=sharing Pour plus de contenu, Visitez http://liliasfaxi.wix.com/liliasfaxi !
BigData_TP3 : Spark
BigData_TP3 : Spark
Lilia Sfaxi
Recommended
Conference: dotnetdays 2020 Location: Iaşi, Romania Abstract: Ever had production problems and found that you cannot debug to find the problem? Or that you need to find out where potential issues are coming from in your Azure cloud solution and you have no idea what is happening? Your applications need to be instrumented with logging, tracing and metrics, so you can see what is going on where. In .NET Core logging and tracing are built into the framework. We will have a look at the differences between logging, tracing and instrumentation in general. You will learn how to use .NET Core to implement logging and tracing with best practices, do semantic logging, work with logging factories, trace providers. Also, you will learn how to instrument using Application Insights and add W3C compliant tracing for correlation across cloud resources in a distributed application. Finally, we will put everything together to see how your logs and traces can give a rich way to get insights into your applications and services running in the Azure cloud or container based solutions.
Logging tracing and metrics in .NET Core and Azure - dotnetdays 2020
Logging tracing and metrics in .NET Core and Azure - dotnetdays 2020
Alex Thissen
Azure architecture
Azure architecture
Amal Dev
Hadoop Summit 2015
What it takes to run Hadoop at Scale: Yahoo! Perspectives
What it takes to run Hadoop at Scale: Yahoo! Perspectives
DataWorks Summit
Cf la seconde partie https://www.slideshare.net/lespetitescases/ralisation-dun-mashup-de-donnes-avec-dss-de-dataiku-et-visualisation-avec-palladio-deuxime-partie Tutoriel pour réaliser un mashup à partir de jeux de données libres téléchargés sur data.gouv.fr et Wikidata entre autres avec le logiciel DSS de Dataiku. Après une introduction sur la notion de mashup et des exemples, cette première partie s'intéresse à la préparation de deux jeux de données issues de data.gouv.fr et provenant du Centre national du cinéma. Ce tutoriel a servi de support de cours au Master 2 "Technologies numériques appliqués à l'histoire" de l'Ecole nationale des chartes lors de l'année universitaire 2016-2017.
Réalisation d'un mashup de données avec DSS de Dataiku - Première partie
Réalisation d'un mashup de données avec DSS de Dataiku - Première partie
Gautier Poupeau
This presentation is a part of meetup session delivered in the Microsoft User Group - Chandigarh. In this meetup we looked into how to deploy and manage Virtual Machines in Microsoft Azure cloud. This was an advanced session and targeted more towards IT Pro audience. Developers were welcome also. We covered created virtual machines via ARM template and covered with Virtual Machine Scale Sets with a live demo with Autoscale.
Deep dive into azure virtual machines
Deep dive into azure virtual machines
Jasjit Chopra
The basics of HTTP protocol
Introduction to HTTP protocol
Introduction to HTTP protocol
Aviran Mordo
Join this session to hear from the Photon product and engineering team talk about the latest developments with the project. As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to. Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution. Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
Radical Speed for SQL Queries on Databricks: Photon Under the Hood
Radical Speed for SQL Queries on Databricks: Photon Under the Hood
Databricks
Pour accéder aux fichiers nécessaires pour faire ce TP, visitez: https://drive.google.com/folderview?id=0Bz7DokLRQvx7M2JWZEt1VHdwSE0&usp=sharing Pour plus de contenu, Visitez http://liliasfaxi.wix.com/liliasfaxi !
BigData_TP3 : Spark
BigData_TP3 : Spark
Lilia Sfaxi
Real-time data streaming is a hot topic in the Telecommunications Industry / Telecom Sector. As telecommunications companies strive to offer high speed, integrated networks with reduced connection times, connect countless devices at reduced latency, and transform the digital experience worldwide, more and more companies are turning to Apache Kafka’s data stream processing solutions to deliver a scalable, real-time infrastructure for OSS and BSS scenarios. Enabling a combination of on-premise data centers, edge processing, and multi-cloud architectures is becoming the new normal in the Telco Industry. This combination is enabling accelerated growth from value-added services delivered over mobile networks. Join Kai Waehner, Technology Evangelist at Confluent, for this session which explores various telecommunications use cases, including data integration, infrastructure monitoring, data distribution, data processing and business applications. Different architectures and components from the Kafka ecosystem are also discussed. This talk explores: - Overcome challenges for building a modern hybrid telco infrastructure - Build a real time infrastructure to correlate relevant events - Connect thousands of devices, networks, infrastructures, and people - Work together with different companies, organisations and business models - Leverage open source and fully managed solutions from the Apache Kafka ecosystem, Confluent Platform and Confluent Cloud
Apache Kafka in the Telco Industry (OSS, BSS, OTT, IMS, NFV, Middleware, Main...
Apache Kafka in the Telco Industry (OSS, BSS, OTT, IMS, NFV, Middleware, Main...
Kai Wähner
Deep dive into HDInsight Best practices
Azure HDInsight
Azure HDInsight
Ashish Thapliyal
Building highly efficient data lakes using Apache Hudi (Incubating) Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake. Speaker: Vinoth Chandar (Uber) Vinoth is Technical Lead at Uber Data Infrastructure Team
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
Chester Chen
http://flink-forward.org/kb_sessions/scaling-stream-processing-with-apache-flink-to-very-large-state/ The majority of streaming programs is ‘stateful’: Windowed Aggregations, Sessions, Joins, Complex Event Processing, Tables – they all require to keep some form of state across individual events. With the migration of more and more complex batch jobs or data processing pipelines to streaming applications, some streaming programs need to keep terabytes of state. Apache Flink implements a checkpointing-based recovery mechanism that guarantees exactly-once semantics for state also in the presence of failures. The cost of checkpointing and recovery depends on the size of the program’s state. In this talk, we will discuss the current status of stateful processing in Apache Flink, as well as the ongoing efforts to make Flink’s fault tolerance mechanism scale to very large state sizes, supporting frequent checkpoints and faster recovery of large state, without requiring excessive numbers of machines.
Stephan Ewen - Scaling to large State
Stephan Ewen - Scaling to large State
Flink Forward
Apache Kafka, Un système distribué de messagerie hautement performant
Apache Kafka, Un système distribué de messagerie hautement performant
Apache Kafka, Un système distribué de messagerie hautement performant
ALTIC Altic
See how Apache Flink's Kafka Consumer is integrating with the checkpointing mechanisms of Flink for exactly once guarantees
Click-Through Example for Flink’s KafkaConsumer Checkpointing
Click-Through Example for Flink’s KafkaConsumer Checkpointing
Robert Metzger
Big data et NoSQL
Big data
Big data
Youssef CHOUNI
Apache Solr crash course
Apache Solr crash course
Tommaso Teofili
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Introduction to Apache Spark
Introduction to Apache Spark
Rahul Jain
TP2 Big Data - HBase Manipulation de l'exemple HBase "MyLittleHBaseTable" sur une VM Hadoop Cloudera. GI3 - ENIS
TP2 Big Data HBase
TP2 Big Data HBase
Amal Abid
This is a presentation deck for Data+AI Summit 2021 at https://databricks.com/session_na21/enabling-vectorized-engine-in-apache-spark
Enabling Vectorized Engine in Apache Spark
Enabling Vectorized Engine in Apache Spark
Kazuaki Ishizaki
Slides from my DataWorks Summit presentation on JanusGraph
Large Scale Graph Analytics with JanusGraph
Large Scale Graph Analytics with JanusGraph
P. Taylor Goetz
At Facebook, millions of Hive queries are executed on a daily basis, and the workload contributes to important analytics that drive product decisions and insights. Spark SQL in Apache Spark provides much of the same functionality as Hive query language (HQL) more efficiently, and Facebook is building a framework to migrate existing production Hive workload to Spark SQL with minimal user intervention. Before Facebook began large-scale migration to SparkSQL, they worked on identifying the gap between HQL and SparkSQL. They built an offline syntax analysis tool that parses, analyzes, optimizes and generates physical plans on daily HQL workload. In this session, they’ll share their results. After finding their syntactic analysis encouraging, they built tooling for offline semantic analysis where they run HQL queries in their Spark shadow cluster and validate the outputs. Output validation is necessary since the runtime behavior in Spark SQL may be different from HQL. They have built a migration framework that supports HQL in both Hive and Spark execution engines, can shadow and validate HQL workloads in Spark, and makes it easy for users to convert their workloads.
Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang
Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang
Databricks
Troubleshooting Strategies for CloudStack Installations by Kirk Kosinski
Troubleshooting Strategies for CloudStack Installations by Kirk Kosinski
buildacloud
VMware NSX provides a platform for deployment of software-defined network (SDN) and network function virtualization (NFV) services across physical network devices in a way that is analogous to server virtualization.
VMware NSX 101: What, Why & How
VMware NSX 101: What, Why & How
Aniekan Akpaffiong
My use case is to provide monitoring, and improving the overall search data quality, also to find the unusual patterns of user’s search behavior, and notifying the intent on-site back to the respective business stakeholders. To achieve the same, I explored various big data processing engines, which can process the huge data with complex business logic in real time. Eventually, I used Flink Stream processing. This talk will showcase how I used Flink to accomplish my goal.
Real time data quality on Flink
Real time data quality on Flink
Jaydeep Vishwakarma
Fundamental of Azure Virtual Machine
Get started With Microsoft Azure Virtual Machine
Get started With Microsoft Azure Virtual Machine
Lai Yoong Seng
These are the slides of my talk on June 30, 2015 at the first event of the Chicago Apache Flink meetup. Although most of the current buzz is about Apache Spark, the talk shows how Apache Flink offers the only hybrid open source (Real-Time Streaming + Batch) distributed data processing engine supporting many use cases: Real-Time stream processing, machine learning at scale, graph analytics and batch processing. In these slides, you will find answers to the following questions: What is Apache Flink stack and how it fits into the Big Data ecosystem? How Apache Flink integrates with Apache Hadoop and other open source tools for data input and output as well as deployment? What is the architecture of Apache Flink? What are the different execution modes of Apache Flink? Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? Who is using Apache Flink? Where to learn more about Apache Flink?
Overview of Apache Flink: Next-Gen Big Data Analytics Framework
Overview of Apache Flink: Next-Gen Big Data Analytics Framework
Slim Baltagi
Ce support explique les concepts de base de Big Data Processing. Elle aborde les parties suivantes : Série de vidéos : https://www.youtube.com/watch?v=1JAljjxpm-Q - Introduction au Big Data - Système de stockage en Big Data - Batch Processing et Stream Processing en Big Data - Aperçu bref de l’écosystème de Hadoop - Aperçu de l’écosystème des outils du Bid Gata - Big data stream processing avec Kafka écosystème - Architecture de Kafka (Brokers, Zookeeper, Procuder, Consumer, Kafka Streams, Connecteurs) - Comment démarrer un cluster de brokers KAFKA - Création et configuration des Topics - Création d’un Java Kafka consumer - Création d’un Java Kafka Produder - Kafka Producer et Kafka Consumer dans une application basée sur Spring - Kafka Streams - Intégration de Kafka dans Spring Cloud. Mot clés : Big data, Big Data Processing, Stream Processing, Kafka, Kafka Streams, Java, Spring Bon apprentissage
Traitement distribue en BIg Data - KAFKA Broker and Kafka Streams
Traitement distribue en BIg Data - KAFKA Broker and Kafka Streams
ENSET, Université Hassan II Casablanca
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Tuning and Debugging in Apache Spark
Tuning and Debugging in Apache Spark
Patrick Wendell
CPU Basic, Instruction set, Addressing mode
Cpu basic
Cpu basic
Dongho Yu
컴퓨터 하드웨어 구조
컴퓨터 하드웨어 구조
dddddanbi
More Related Content
What's hot
Real-time data streaming is a hot topic in the Telecommunications Industry / Telecom Sector. As telecommunications companies strive to offer high speed, integrated networks with reduced connection times, connect countless devices at reduced latency, and transform the digital experience worldwide, more and more companies are turning to Apache Kafka’s data stream processing solutions to deliver a scalable, real-time infrastructure for OSS and BSS scenarios. Enabling a combination of on-premise data centers, edge processing, and multi-cloud architectures is becoming the new normal in the Telco Industry. This combination is enabling accelerated growth from value-added services delivered over mobile networks. Join Kai Waehner, Technology Evangelist at Confluent, for this session which explores various telecommunications use cases, including data integration, infrastructure monitoring, data distribution, data processing and business applications. Different architectures and components from the Kafka ecosystem are also discussed. This talk explores: - Overcome challenges for building a modern hybrid telco infrastructure - Build a real time infrastructure to correlate relevant events - Connect thousands of devices, networks, infrastructures, and people - Work together with different companies, organisations and business models - Leverage open source and fully managed solutions from the Apache Kafka ecosystem, Confluent Platform and Confluent Cloud
Apache Kafka in the Telco Industry (OSS, BSS, OTT, IMS, NFV, Middleware, Main...
Apache Kafka in the Telco Industry (OSS, BSS, OTT, IMS, NFV, Middleware, Main...
Kai Wähner
Deep dive into HDInsight Best practices
Azure HDInsight
Azure HDInsight
Ashish Thapliyal
Building highly efficient data lakes using Apache Hudi (Incubating) Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake. Speaker: Vinoth Chandar (Uber) Vinoth is Technical Lead at Uber Data Infrastructure Team
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
Chester Chen
http://flink-forward.org/kb_sessions/scaling-stream-processing-with-apache-flink-to-very-large-state/ The majority of streaming programs is ‘stateful’: Windowed Aggregations, Sessions, Joins, Complex Event Processing, Tables – they all require to keep some form of state across individual events. With the migration of more and more complex batch jobs or data processing pipelines to streaming applications, some streaming programs need to keep terabytes of state. Apache Flink implements a checkpointing-based recovery mechanism that guarantees exactly-once semantics for state also in the presence of failures. The cost of checkpointing and recovery depends on the size of the program’s state. In this talk, we will discuss the current status of stateful processing in Apache Flink, as well as the ongoing efforts to make Flink’s fault tolerance mechanism scale to very large state sizes, supporting frequent checkpoints and faster recovery of large state, without requiring excessive numbers of machines.
Stephan Ewen - Scaling to large State
Stephan Ewen - Scaling to large State
Flink Forward
Apache Kafka, Un système distribué de messagerie hautement performant
Apache Kafka, Un système distribué de messagerie hautement performant
Apache Kafka, Un système distribué de messagerie hautement performant
ALTIC Altic
See how Apache Flink's Kafka Consumer is integrating with the checkpointing mechanisms of Flink for exactly once guarantees
Click-Through Example for Flink’s KafkaConsumer Checkpointing
Click-Through Example for Flink’s KafkaConsumer Checkpointing
Robert Metzger
Big data et NoSQL
Big data
Big data
Youssef CHOUNI
Apache Solr crash course
Apache Solr crash course
Tommaso Teofili
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Introduction to Apache Spark
Introduction to Apache Spark
Rahul Jain
TP2 Big Data - HBase Manipulation de l'exemple HBase "MyLittleHBaseTable" sur une VM Hadoop Cloudera. GI3 - ENIS
TP2 Big Data HBase
TP2 Big Data HBase
Amal Abid
This is a presentation deck for Data+AI Summit 2021 at https://databricks.com/session_na21/enabling-vectorized-engine-in-apache-spark
Enabling Vectorized Engine in Apache Spark
Enabling Vectorized Engine in Apache Spark
Kazuaki Ishizaki
Slides from my DataWorks Summit presentation on JanusGraph
Large Scale Graph Analytics with JanusGraph
Large Scale Graph Analytics with JanusGraph
P. Taylor Goetz
At Facebook, millions of Hive queries are executed on a daily basis, and the workload contributes to important analytics that drive product decisions and insights. Spark SQL in Apache Spark provides much of the same functionality as Hive query language (HQL) more efficiently, and Facebook is building a framework to migrate existing production Hive workload to Spark SQL with minimal user intervention. Before Facebook began large-scale migration to SparkSQL, they worked on identifying the gap between HQL and SparkSQL. They built an offline syntax analysis tool that parses, analyzes, optimizes and generates physical plans on daily HQL workload. In this session, they’ll share their results. After finding their syntactic analysis encouraging, they built tooling for offline semantic analysis where they run HQL queries in their Spark shadow cluster and validate the outputs. Output validation is necessary since the runtime behavior in Spark SQL may be different from HQL. They have built a migration framework that supports HQL in both Hive and Spark execution engines, can shadow and validate HQL workloads in Spark, and makes it easy for users to convert their workloads.
Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang
Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang
Databricks
Troubleshooting Strategies for CloudStack Installations by Kirk Kosinski
Troubleshooting Strategies for CloudStack Installations by Kirk Kosinski
buildacloud
VMware NSX provides a platform for deployment of software-defined network (SDN) and network function virtualization (NFV) services across physical network devices in a way that is analogous to server virtualization.
VMware NSX 101: What, Why & How
VMware NSX 101: What, Why & How
Aniekan Akpaffiong
My use case is to provide monitoring, and improving the overall search data quality, also to find the unusual patterns of user’s search behavior, and notifying the intent on-site back to the respective business stakeholders. To achieve the same, I explored various big data processing engines, which can process the huge data with complex business logic in real time. Eventually, I used Flink Stream processing. This talk will showcase how I used Flink to accomplish my goal.
Real time data quality on Flink
Real time data quality on Flink
Jaydeep Vishwakarma
Fundamental of Azure Virtual Machine
Get started With Microsoft Azure Virtual Machine
Get started With Microsoft Azure Virtual Machine
Lai Yoong Seng
These are the slides of my talk on June 30, 2015 at the first event of the Chicago Apache Flink meetup. Although most of the current buzz is about Apache Spark, the talk shows how Apache Flink offers the only hybrid open source (Real-Time Streaming + Batch) distributed data processing engine supporting many use cases: Real-Time stream processing, machine learning at scale, graph analytics and batch processing. In these slides, you will find answers to the following questions: What is Apache Flink stack and how it fits into the Big Data ecosystem? How Apache Flink integrates with Apache Hadoop and other open source tools for data input and output as well as deployment? What is the architecture of Apache Flink? What are the different execution modes of Apache Flink? Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? Who is using Apache Flink? Where to learn more about Apache Flink?
Overview of Apache Flink: Next-Gen Big Data Analytics Framework
Overview of Apache Flink: Next-Gen Big Data Analytics Framework
Slim Baltagi
Ce support explique les concepts de base de Big Data Processing. Elle aborde les parties suivantes : Série de vidéos : https://www.youtube.com/watch?v=1JAljjxpm-Q - Introduction au Big Data - Système de stockage en Big Data - Batch Processing et Stream Processing en Big Data - Aperçu bref de l’écosystème de Hadoop - Aperçu de l’écosystème des outils du Bid Gata - Big data stream processing avec Kafka écosystème - Architecture de Kafka (Brokers, Zookeeper, Procuder, Consumer, Kafka Streams, Connecteurs) - Comment démarrer un cluster de brokers KAFKA - Création et configuration des Topics - Création d’un Java Kafka consumer - Création d’un Java Kafka Produder - Kafka Producer et Kafka Consumer dans une application basée sur Spring - Kafka Streams - Intégration de Kafka dans Spring Cloud. Mot clés : Big data, Big Data Processing, Stream Processing, Kafka, Kafka Streams, Java, Spring Bon apprentissage
Traitement distribue en BIg Data - KAFKA Broker and Kafka Streams
Traitement distribue en BIg Data - KAFKA Broker and Kafka Streams
ENSET, Université Hassan II Casablanca
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Tuning and Debugging in Apache Spark
Tuning and Debugging in Apache Spark
Patrick Wendell
What's hot
(20)
Apache Kafka in the Telco Industry (OSS, BSS, OTT, IMS, NFV, Middleware, Main...
Apache Kafka in the Telco Industry (OSS, BSS, OTT, IMS, NFV, Middleware, Main...
Azure HDInsight
Azure HDInsight
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
Stephan Ewen - Scaling to large State
Stephan Ewen - Scaling to large State
Apache Kafka, Un système distribué de messagerie hautement performant
Apache Kafka, Un système distribué de messagerie hautement performant
Click-Through Example for Flink’s KafkaConsumer Checkpointing
Click-Through Example for Flink’s KafkaConsumer Checkpointing
Big data
Big data
Apache Solr crash course
Apache Solr crash course
Introduction to Apache Spark
Introduction to Apache Spark
TP2 Big Data HBase
TP2 Big Data HBase
Enabling Vectorized Engine in Apache Spark
Enabling Vectorized Engine in Apache Spark
Large Scale Graph Analytics with JanusGraph
Large Scale Graph Analytics with JanusGraph
Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang
Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang
Troubleshooting Strategies for CloudStack Installations by Kirk Kosinski
Troubleshooting Strategies for CloudStack Installations by Kirk Kosinski
VMware NSX 101: What, Why & How
VMware NSX 101: What, Why & How
Real time data quality on Flink
Real time data quality on Flink
Get started With Microsoft Azure Virtual Machine
Get started With Microsoft Azure Virtual Machine
Overview of Apache Flink: Next-Gen Big Data Analytics Framework
Overview of Apache Flink: Next-Gen Big Data Analytics Framework
Traitement distribue en BIg Data - KAFKA Broker and Kafka Streams
Traitement distribue en BIg Data - KAFKA Broker and Kafka Streams
Tuning and Debugging in Apache Spark
Tuning and Debugging in Apache Spark
Similar to 펌웨어추출 기초
CPU Basic, Instruction set, Addressing mode
Cpu basic
Cpu basic
Dongho Yu
컴퓨터 하드웨어 구조
컴퓨터 하드웨어 구조
dddddanbi
2013 mcu( 마이크로컨트롤러 ) 수업자료 3
2013 mcu( 마이크로컨트롤러 ) 수업자료 3
진우 김
자바를 만들기 위해 만들어진 도구들과, 문제가 발생했을 때 JVM을 분석하기 위해 만들어진 도구에 대해서 살펴봅니다. 목차 1. jvmtop 2. jvm-tools 3. jcmd 4. jhsdb 5. OpenJDK Tools 대상 모든 자바 개발자
[2018] Java를 위한, Java에 의한 도구들
[2018] Java를 위한, Java에 의한 도구들
NHN FORWARD
Ryu with OpenFlow 1.3, Traffic Monitor
Ryu with OpenFlow 1.3, Traffic Monitor
Ryu with OpenFlow 1.3, Traffic Monitor
jieun kim
F-INSIGHT OPEN SEMINAR
(Fios#02) 4. 임베디드 다바이스 역분석
(Fios#02) 4. 임베디드 다바이스 역분석
INSIGHT FORENSIC
서버 성능 테스트 관련 도움이 되는 툴들을 소개
Server performance test tool
Server performance test tool
Chang-Hwan Han
삼성소프트웨어멤버십
백업을 위한 USB운영체제 완료세미나
백업을 위한 USB운영체제 완료세미나
Daniel Shin
JBoss 커뮤니티에서 제공하는 모니터링 오픈소스 플랫폼에 관한 소개입니다.
JBoss RHQ와 Byteman을 이용한 오픈소스 자바 애플리케이션 모니터링
JBoss RHQ와 Byteman을 이용한 오픈소스 자바 애플리케이션 모니터링
Ted Won
Acute TravelLogic TL2000B series 제품 소개 및 기능 설명
Acute travel logic logic analyzer(s)
Acute travel logic logic analyzer(s)
WAVENIX CO.,LTD.
-재미없는 java runtime process 디버그
20170908 tech day-9th-재미없는 java runtime process 디버그-김성중
20170908 tech day-9th-재미없는 java runtime process 디버그-김성중
ymtech
This slide allows you to increase your web application server performance. If you want to get this, please email us(support at osci.kr)
[오픈소스컨설팅]Performance Tuning How To
[오픈소스컨설팅]Performance Tuning How To
Ji-Woong Choi
웹개발자의 약한고리 SQL 뛰어넘기 박광일
ecdevday8 웹개발자의 약한고리 SQL 뛰어넘기
ecdevday8 웹개발자의 약한고리 SQL 뛰어넘기
Kenu, GwangNam Heo
Avr lecture1
Avr lecture1
봉조 김
31기 김종헌 Paradigm of computer architecture 세미나
Paradigm of computer architecture
Paradigm of computer architecture
hyu_jaram
SK planet 스트리밍 ( Streaming ) 시스템의 이해 ( 스트리밍 101, Use cases, Spark, Kafka, Flink Streaming, 개발/운영 시 챙겨야 할 것 )
SK planet Streaming system
SK planet Streaming system
용휘 김
2012 F-INSIGHT TALK
(120128) #fitalk sql server anti-forensics
(120128) #fitalk sql server anti-forensics
INSIGHT FORENSIC
3D 프린터 조립수업 강의자료 http://www.makecube.in/p/3d_3.html
3D 프린터 동작원리와 조립
3D 프린터 동작원리와 조립
Chiwon Song
자바에 대한 성능 튜닝 방법과 미들웨어(모든 WAS기준) 관점에서의 튜닝 트러블 슈팅에 대한 방법을 소개합니다.
[오픈소스컨설팅]Java Performance Tuning
[오픈소스컨설팅]Java Performance Tuning
Ji-Woong Choi
Avr lecture2
Avr lecture2
봉조 김
Similar to 펌웨어추출 기초
(20)
Cpu basic
Cpu basic
컴퓨터 하드웨어 구조
컴퓨터 하드웨어 구조
2013 mcu( 마이크로컨트롤러 ) 수업자료 3
2013 mcu( 마이크로컨트롤러 ) 수업자료 3
[2018] Java를 위한, Java에 의한 도구들
[2018] Java를 위한, Java에 의한 도구들
Ryu with OpenFlow 1.3, Traffic Monitor
Ryu with OpenFlow 1.3, Traffic Monitor
(Fios#02) 4. 임베디드 다바이스 역분석
(Fios#02) 4. 임베디드 다바이스 역분석
Server performance test tool
Server performance test tool
백업을 위한 USB운영체제 완료세미나
백업을 위한 USB운영체제 완료세미나
JBoss RHQ와 Byteman을 이용한 오픈소스 자바 애플리케이션 모니터링
JBoss RHQ와 Byteman을 이용한 오픈소스 자바 애플리케이션 모니터링
Acute travel logic logic analyzer(s)
Acute travel logic logic analyzer(s)
20170908 tech day-9th-재미없는 java runtime process 디버그-김성중
20170908 tech day-9th-재미없는 java runtime process 디버그-김성중
[오픈소스컨설팅]Performance Tuning How To
[오픈소스컨설팅]Performance Tuning How To
ecdevday8 웹개발자의 약한고리 SQL 뛰어넘기
ecdevday8 웹개발자의 약한고리 SQL 뛰어넘기
Avr lecture1
Avr lecture1
Paradigm of computer architecture
Paradigm of computer architecture
SK planet Streaming system
SK planet Streaming system
(120128) #fitalk sql server anti-forensics
(120128) #fitalk sql server anti-forensics
3D 프린터 동작원리와 조립
3D 프린터 동작원리와 조립
[오픈소스컨설팅]Java Performance Tuning
[오픈소스컨설팅]Java Performance Tuning
Avr lecture2
Avr lecture2
펌웨어추출 기초
1.
펌웨어 추출 장재진
2.
펌웨어 추출이란? • 메모리
소자의 데이터 시트를 보고 적절하게 프로그래밍하여 데이터를 뽑아내는 것 프로세서 메모리 입력 출력
3.
요구되는 능력 ① 정상적인
손 – 분해, 납땝, 회로 구성 등등 ② 시력 0.3이상의 눈 – 데이터시트 확인 ③ 보드에 맞게 프로그래밍 할 수 있는 뇌 – 입출력을 할 수 있는 보드라면 무관
4.
단계 ① 제품 분해
후 메모리 소자 확인 or Riffbox JTAG에 호환되는 프로세서 인지 확인 ② 메모리 데이터 시트 확인 – 기본 사항, 작동 전압, 명령어, 핀 역할 등 ③ 회로 구성 및 프로그래밍 ④ 추출 및 분석
5.
단계 - ①
분해 및 메모리 소자 확인 Winbond - SDRAM RTL8196E - SOC RTL8196ER - 무선랜 E522839 - 레귤레이터 25Q16bSIG - 플래시메모리
6.
단계 - ②
데이터 시트 확인
7.
단계 - ②
데이터 시트 확인
8.
단계 - ②
데이터 시트 확인
9.
단계 - ②
데이터 시트 확인
10.
단계 - ②
데이터 시트 확인
11.
단계 - ③
회로 구성 및 프로그래밍 • PA0 = /CS : HIGH->LOW • PA1 = /WP : HIGH • PA2 = VSS : LOW • PA3 = /HOLD : HIGH • PA4 = VCC : HIGH • PORTB = CLK : 클럭 • PORTC = SI : 입력 • PORTD = SO : 출력
12.
단계 - ③
회로 구성 및 프로그래밍
13.
단계 - ③
회로 구성 및 프로그래밍 • Getid 소스코드 분석
14.
단계 - ④
추출 및 분석 • 로그파일을 binary파일로 변환 • 툴을 이용해 제대로 추출됐는지 확인
Download now
