IMA Lab Lead Software Architect, Gray Bowman and Senior Digital Graphic Designer, Rita Troyer showcase the development and design behind the Indianapolis Museum of Art Collection Page Redesign.
The document discusses storing a geopolitical ontology containing about 21,000 triples. It currently stores the ontology in a flat file and uses the JENA API. The document proposes alternative architectures for storage, including using a triple store or relational database, and compares loading times for different options based on benchmark results. It suggests hosting a SPARQL endpoint for querying if using a relational database-based storage.
Airline Reservations and Routing: A Graph Use CaseJason Plurad
We've all been there before... you hear the announcement that your flight is canceled. Fellow passengers race to the gate agent to rebook on the next available flight. How do they quickly determine the best route from Berlin to San Francisco? Ultimately the flight route network is best solved as a graph problem. We will discuss our lessons learned from working with a major airline to solve this problem using JanusGraph database. JanusGraph is an open source graph database designed for massive scale. It is compatible with several pieces of the open source big data stack: Apache TinkerPop (graph computing framework), HBase, Cassandra, and Solr. We will go into depth about our approach to benchmarking graph performance and discuss the utilities we developed. We will share our comparison results for evaluating which storage backend use with JanusGraph. Whether you are productizing a new database or you are a frustrated traveler, a fast resolution is needed to satisfy everybody involved. Presented at DataWorks Summit Berlin on April 18, 2018
Presented at the Linked Data Benchmark Council (LDBC) Technical User Group (TUG) Meeting on June 8, 2018. http://www.ldbcouncil.org/blog/11th-tuc-meeting-university-texas-austin
Exploring Graph Use Cases with JanusGraphJason Plurad
Graph databases are relative newcomers in the NoSQL database landscape. What are some graph model and design considerations when choosing a graph database in your architecture? Let's take a tour of a couple graph use cases that we've collaborated on recently with our clients to help you better understand how and why a graph database can be integrated to help solve problems found with connected data. Presented at DataWorks Summit San Jose - IBM Meetup on June 18, 2018.
https://www.meetup.com/BigDataDevelopers/events/251307524/
【EDD Workshop@140718】eztable architecture hao kang denEZTABLE
The document discusses the past, present, and future architecture of EDD WorkshopEZTABLE. In the past, the architecture consisted of PHP workers and a PHP/JavaScript frontend. Currently, the architecture is similar but includes apps and uses a hairball for communication. Going forward, the architecture will move to app facades running on different platforms like Node.js, Go and Lua, with workers and a RESTful API for communication between components.
One of the first problems a developer encounters when evaluating a graph database is how to construct a graph efficiently. Recognizing this need in 2014, TinkerPop's Stephen Mallette penned a series of blog posts titled "Powers of Ten" which addressed several bulkload techniques for Titan. Since then Titan has gone away, and the open source graph database landscape has evolved significantly. Do the same approaches stand the test of time? In this session, we will take a deep dive into strategies for loading data of various sizes into modern Apache TinkerPop graph systems. We will discuss bulkloading with JanusGraph, the scalable graph database forked from Titan, to better understand how its architecture can be optimized for ingestion. Presented at Data Day Texas on January 27, 2018.
From Idea to Model: Productionizing Data Pipelines with Apache AirflowDatabricks
When supporting a data science team, data engineers are tasked with building a platform that keeps a wide range of stakeholders happy. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports.
This presentation provides an overview of interactive data visualization using the Dash-Plotly libraries. It discusses the need for data visualization in presentations, reports, machine learning and real-time data. It contrasts static versus interactive graphs and how Dash allows for changing views and updating graphs automatically from multiple sources. An overview of the Plotly and Dash libraries is given, including the basic components of graphs, figures, data and layout. Examples of interactive graphs and dashboards using these libraries will be provided.
The document discusses storing a geopolitical ontology containing about 21,000 triples. It currently stores the ontology in a flat file and uses the JENA API. The document proposes alternative architectures for storage, including using a triple store or relational database, and compares loading times for different options based on benchmark results. It suggests hosting a SPARQL endpoint for querying if using a relational database-based storage.
Airline Reservations and Routing: A Graph Use CaseJason Plurad
We've all been there before... you hear the announcement that your flight is canceled. Fellow passengers race to the gate agent to rebook on the next available flight. How do they quickly determine the best route from Berlin to San Francisco? Ultimately the flight route network is best solved as a graph problem. We will discuss our lessons learned from working with a major airline to solve this problem using JanusGraph database. JanusGraph is an open source graph database designed for massive scale. It is compatible with several pieces of the open source big data stack: Apache TinkerPop (graph computing framework), HBase, Cassandra, and Solr. We will go into depth about our approach to benchmarking graph performance and discuss the utilities we developed. We will share our comparison results for evaluating which storage backend use with JanusGraph. Whether you are productizing a new database or you are a frustrated traveler, a fast resolution is needed to satisfy everybody involved. Presented at DataWorks Summit Berlin on April 18, 2018
Presented at the Linked Data Benchmark Council (LDBC) Technical User Group (TUG) Meeting on June 8, 2018. http://www.ldbcouncil.org/blog/11th-tuc-meeting-university-texas-austin
Exploring Graph Use Cases with JanusGraphJason Plurad
Graph databases are relative newcomers in the NoSQL database landscape. What are some graph model and design considerations when choosing a graph database in your architecture? Let's take a tour of a couple graph use cases that we've collaborated on recently with our clients to help you better understand how and why a graph database can be integrated to help solve problems found with connected data. Presented at DataWorks Summit San Jose - IBM Meetup on June 18, 2018.
https://www.meetup.com/BigDataDevelopers/events/251307524/
【EDD Workshop@140718】eztable architecture hao kang denEZTABLE
The document discusses the past, present, and future architecture of EDD WorkshopEZTABLE. In the past, the architecture consisted of PHP workers and a PHP/JavaScript frontend. Currently, the architecture is similar but includes apps and uses a hairball for communication. Going forward, the architecture will move to app facades running on different platforms like Node.js, Go and Lua, with workers and a RESTful API for communication between components.
One of the first problems a developer encounters when evaluating a graph database is how to construct a graph efficiently. Recognizing this need in 2014, TinkerPop's Stephen Mallette penned a series of blog posts titled "Powers of Ten" which addressed several bulkload techniques for Titan. Since then Titan has gone away, and the open source graph database landscape has evolved significantly. Do the same approaches stand the test of time? In this session, we will take a deep dive into strategies for loading data of various sizes into modern Apache TinkerPop graph systems. We will discuss bulkloading with JanusGraph, the scalable graph database forked from Titan, to better understand how its architecture can be optimized for ingestion. Presented at Data Day Texas on January 27, 2018.
From Idea to Model: Productionizing Data Pipelines with Apache AirflowDatabricks
When supporting a data science team, data engineers are tasked with building a platform that keeps a wide range of stakeholders happy. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports.
This presentation provides an overview of interactive data visualization using the Dash-Plotly libraries. It discusses the need for data visualization in presentations, reports, machine learning and real-time data. It contrasts static versus interactive graphs and how Dash allows for changing views and updating graphs automatically from multiple sources. An overview of the Plotly and Dash libraries is given, including the basic components of graphs, figures, data and layout. Examples of interactive graphs and dashboards using these libraries will be provided.
GT.M: A Tried and Tested Open-Source NoSQL DatabaseRob Tweed
GT.M is a tried and tested schema-less "NoSQL" database with a strong pedigree in the highly demanding banking sector. Its free open-source licensing on x86 GNU Linux makes it an excellent alternative to the list of new, largely untested, NoSQL databases.
GT.M is a NoSQL database and programming language used for mission critical applications. It provides ACID transactions for thousands of online banking transactions per second. GT.M uses a hierarchical key-value data store and supports SQL access. The scripting language M is widely used in healthcare. GT.M supports replication across multiple instances for high availability and supports long-distance replication up to 12,450 miles.
Community-Driven Graphs with JanusGraphJason Plurad
Presented at Open Camps (Database Camp, Search Camp) in New York City on November 19, 2017. http://www.searchcamp.io/2017/presentations/community-driven-graphs-with-janusgraph
This document describes YAMCAT, an open source catalog for managing spatial metadata and facilitating collaboration on projects. YAMCAT allows users to search for and share geospatial metadata, download geospatial data, and preview data layers using WMS. It includes forms for adding and editing metadata according to ISO 19139 standards and can export metadata to the GeoNetwork catalog. YAMCAT requires a web server with PHP and mySQL and optionally MapServer, and aims to support collaboration and data sharing for scientific projects.
Introduction to HDInsight, and its capabilities, including Azure Storage, Hive, MapReduce, Mahout and HBase. See also some of the tools mentioned at http://bigdata.red-gate.com/ and source code at https://github.com/simonellistonball/GettingYourBigDataOnMapReduce
Introduction to Dash framework from Plotly, reactive framework for building dashboards in Python. Tech talk covers basics and more advanced topics like custom component and scaling.
Speaker: Damian Rodziewicz.
Resources:
1. Blogpost
https://appsilon.com/overview-of-dash-python-framework-from-plotly-for-building-dashboards/
2. Github
https://github.com/DamianRodziewicz/dash_example
app.py
https://github.com/DamianRodziewicz/dash_example longer_computations_app.py
3. Youtube:
https://youtu.be/NUXUmv-aeG4
Presented at Open Camps (Database Camp) in New York City on November 19, 2017. http://www.db.camp/2017/presentations/graph-computing-with-apache-tinkerpop
Grokking TechTalk #29: Building Realtime Metrics Platform at LinkedInGrokking VN
Bài techtalk của anh Khải Trần nói về hệ thống data pipeline của LinkedIn được dùng để thu thập hàng chục tỷ messages mỗi ngày, và cách họ chạy hệ thống real-time processing để thống kê lượng dữ liệu này cho mục đính metrics monitoring.
1 số điểm bài talk sẽ chia sẻ:
- Giới thiệu về hệ thống unified metrics platform của LinkedIn
- Cách LinkedIn setup hệ thống BigData pipeline dùng Kafka, HDFS, Apache Calcite và Apache Samza.
- Khái niệm nearline storage, và cách LinkedIn chuyển từ offline architecture sang nearline architecture.
Speaker: Khai Tran, Staff Software Engineer - LinkedIn.
- Hiện đang là staff software engineer ở LinkedIn, phụ trách hệ thống metrics monitoring system. Trước đây từng làm ở Amazon AWS và Oracle.
- PhD, University of Wisconsin-Madison, nghiên cứu về Database Systems.
Grafana is an open source analytics and monitoring solution that allows users to visualize data and metrics from various sources. It provides a flexible dashboard interface that supports creating and sharing visualizations, alerting, and templating. Grafana has evolved over several major versions to support more data sources, improved UX, alerting capabilities, and a plugin system. It aims to continue expanding supported data sources and features like reporting, live data streaming, and clustering.
The JanusGraph project started at the Linux Foundation earlier this year, but it is not the new kid on the block. We'll start with a look at the origins and evolution of this open source graph database through the lens of a few IBM graph use cases. We'll discuss the new features in latest release of JanusGraph, and then take a look at future directions to explore together with the open community. Presented on October 18, 2017 at the Graph Technologies Meetup in Santa Clara, CA. https://www.meetup.com/_CAIDI/events/243122187/
An Introduction to the Heatmap / Histogram PluginMitsuhiro Tanda
This document introduces the Heatmap and Histogram plugins for Grafana. It describes how the Histogram plugin calculates histograms from time series data and is mostly compatible with the Graph panel. The Heatmap plugin generates heatmaps from time series data using the Epoch visualization library and allows users to visualize latency, utilization, and time series trends without missing details. Future plans include supporting pre-calculated histogram and heatmap data from various data sources and improving compatibility with the Graph panel.
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/nZzHFwaoMpU
In this presentation, we will demonstrate the integration of H2O Driverless.ai with NetApp Cloud Volumes Service. In addition, we’ll describe key considerations for the development of Deep Learning environments and the solutions that enable seamless data management across edge environments, on-premises data centers, and the cloud. This presentation is targeted for data scientists, data engineers, and line of business leaders.
Vinod comes with over 10 years of Marketing & Data Science experience in multiple startups. He was the founding employee for his previous startup, Activehours, where he helped build the product and bootstrap the user acquisition with growth hacking. He has seen the user base for his companies grow from scratch to millions of customers. He’s built models to score leads, reduce churn, increase conversion, prevent fraud and many more use cases. He brings a strong analytical side and an metrics driven approach to marketing.
Dataiku big data paris - the rise of the hadoop ecosystemDataiku
This document discusses the rise of the Hadoop ecosystem. It outlines how the ecosystem has expanded from the original Hadoop components of HDFS for storage and MapReduce for distributed computation. New frameworks have emerged that allow for real-time queries, updates, and machine learning on big data. These include Spark, Storm, Drill, and streaming engines. The ecosystem is now a complex network of interoperable tools for storage, computation, analytics and machine learning on large datasets.
Building Analytic Apps for SaaS: “Analytics as a Service”Amazon Web Services
TIBCO Jaspersoft® for AWS is a business intelligence suite that helps you deliver stunning interactive reports and dashboards inside your app that make it easy for your customers to get answers. Purpose-built for AWS, our reporting and analytics server quickly and easily connects to Amazon Relational Database Service (RDS), Amazon Redshift, and Amazon EMR. It includes ad-hoc reporting, dashboards, data analysis, data visualization, and data blending. In less than 10 minutes, you can be analyzing and reporting on your data. You get a full Cloud BI server starting at less than $1/hour, with no user or data limits and no additional fees.
This webinar deck shows how embeddable analytics with TIBCO Jaspersoft for AWS gives you the power to create the experience your end users demand and how to scale and manage that experience across your customer base with AWS.
The document discusses various concepts and techniques related to performance-driven front-end development, including:
- Layout, reflow, and repaint processes that occur when elements change on a page.
- Examples of performance issues like layout thrashing caused by unnecessary reflows, and solutions like minimizing DOM changes and using requestAnimationFrame.
- Tools for measuring performance like the DevTools timeline and ways to optimize aspects like page loading, images, fonts, animations, scrolling, and memory leaks.
Dev tools rendering & memory profilingOpen Academy
Chrome DevTools provide tools for debugging, profiling, and optimizing web page performance. They allow developers to visualize the rendering process, debug issues, and find opportunities to reduce layout reflows and repaints. Key features include the timeline panel to show paint rectangles and composited layer borders, heap snapshots to analyze memory usage, and memory profiling to detect leaks and optimize memory management. Using DevTools, developers can diagnose performance bottlenecks and ensure smooth interactions at 60 frames per second.
Softshake 2013: Introduction to NoSQL with CouchbaseTugdual Grall
This presentation was delivered during Softshake 2013. Learn why RDBMS are not enought and why NoSQL help developers to scale their applications and provide agility.
Mihai Nuta has over 14 years of experience developing computer systems and applications. He has extensive experience with technologies like Visual Basic, SQL, Oracle, and .NET. Currently he works as a senior programmer analyst at Xerox Corporation developing applications for General Motors, including a legal document application and tools for processing images and documents. He has strong skills in databases, web and client/server development, and software like Microsoft Office, SQL Server, and Visual Studio.
Datomic is a database that uses an immutable data model where data is never updated or erased, only added to over time. It uses distributed transactions where transactions are processed serially by transactors and changes are transmitted to peers. Peers cache data and submit transactions, handling queries using a merged view of the cache and latest data from the transactor. The data model consists of immutable datoms that are entities, attributes, values, and the transaction time, allowing queries over time.
Wix Architecture at Scale - QCon London 2014Aviran Mordo
In this talk I will go over Wix's architecture, how we evolved our system to be highly available even at the worst case scenarios when everything can break, how we built a self-healing eventual consistency system for website data distribution and will show some of the patterns we use that helps us render 45M websites while maintaining a relatively low number of servers.
The document provides an overview of JAMStack, a new approach to building web applications that uses JavaScript, APIs, and markup. It defines JAMStack as using JavaScript in the browser as a runtime, reusable HTTP APIs instead of app-specific databases, and prebuilt markup for delivery. It discusses different types of JAMStack projects including static HTML sites, sites with content from a CMS, web applications, and large websites. It also outlines advantages like improved performance, security, and scalability, as well as considerations for planning a JAMStack project such as managing content, choosing a site generator, automation, and CDNs.
GT.M: A Tried and Tested Open-Source NoSQL DatabaseRob Tweed
GT.M is a tried and tested schema-less "NoSQL" database with a strong pedigree in the highly demanding banking sector. Its free open-source licensing on x86 GNU Linux makes it an excellent alternative to the list of new, largely untested, NoSQL databases.
GT.M is a NoSQL database and programming language used for mission critical applications. It provides ACID transactions for thousands of online banking transactions per second. GT.M uses a hierarchical key-value data store and supports SQL access. The scripting language M is widely used in healthcare. GT.M supports replication across multiple instances for high availability and supports long-distance replication up to 12,450 miles.
Community-Driven Graphs with JanusGraphJason Plurad
Presented at Open Camps (Database Camp, Search Camp) in New York City on November 19, 2017. http://www.searchcamp.io/2017/presentations/community-driven-graphs-with-janusgraph
This document describes YAMCAT, an open source catalog for managing spatial metadata and facilitating collaboration on projects. YAMCAT allows users to search for and share geospatial metadata, download geospatial data, and preview data layers using WMS. It includes forms for adding and editing metadata according to ISO 19139 standards and can export metadata to the GeoNetwork catalog. YAMCAT requires a web server with PHP and mySQL and optionally MapServer, and aims to support collaboration and data sharing for scientific projects.
Introduction to HDInsight, and its capabilities, including Azure Storage, Hive, MapReduce, Mahout and HBase. See also some of the tools mentioned at http://bigdata.red-gate.com/ and source code at https://github.com/simonellistonball/GettingYourBigDataOnMapReduce
Introduction to Dash framework from Plotly, reactive framework for building dashboards in Python. Tech talk covers basics and more advanced topics like custom component and scaling.
Speaker: Damian Rodziewicz.
Resources:
1. Blogpost
https://appsilon.com/overview-of-dash-python-framework-from-plotly-for-building-dashboards/
2. Github
https://github.com/DamianRodziewicz/dash_example
app.py
https://github.com/DamianRodziewicz/dash_example longer_computations_app.py
3. Youtube:
https://youtu.be/NUXUmv-aeG4
Presented at Open Camps (Database Camp) in New York City on November 19, 2017. http://www.db.camp/2017/presentations/graph-computing-with-apache-tinkerpop
Grokking TechTalk #29: Building Realtime Metrics Platform at LinkedInGrokking VN
Bài techtalk của anh Khải Trần nói về hệ thống data pipeline của LinkedIn được dùng để thu thập hàng chục tỷ messages mỗi ngày, và cách họ chạy hệ thống real-time processing để thống kê lượng dữ liệu này cho mục đính metrics monitoring.
1 số điểm bài talk sẽ chia sẻ:
- Giới thiệu về hệ thống unified metrics platform của LinkedIn
- Cách LinkedIn setup hệ thống BigData pipeline dùng Kafka, HDFS, Apache Calcite và Apache Samza.
- Khái niệm nearline storage, và cách LinkedIn chuyển từ offline architecture sang nearline architecture.
Speaker: Khai Tran, Staff Software Engineer - LinkedIn.
- Hiện đang là staff software engineer ở LinkedIn, phụ trách hệ thống metrics monitoring system. Trước đây từng làm ở Amazon AWS và Oracle.
- PhD, University of Wisconsin-Madison, nghiên cứu về Database Systems.
Grafana is an open source analytics and monitoring solution that allows users to visualize data and metrics from various sources. It provides a flexible dashboard interface that supports creating and sharing visualizations, alerting, and templating. Grafana has evolved over several major versions to support more data sources, improved UX, alerting capabilities, and a plugin system. It aims to continue expanding supported data sources and features like reporting, live data streaming, and clustering.
The JanusGraph project started at the Linux Foundation earlier this year, but it is not the new kid on the block. We'll start with a look at the origins and evolution of this open source graph database through the lens of a few IBM graph use cases. We'll discuss the new features in latest release of JanusGraph, and then take a look at future directions to explore together with the open community. Presented on October 18, 2017 at the Graph Technologies Meetup in Santa Clara, CA. https://www.meetup.com/_CAIDI/events/243122187/
An Introduction to the Heatmap / Histogram PluginMitsuhiro Tanda
This document introduces the Heatmap and Histogram plugins for Grafana. It describes how the Histogram plugin calculates histograms from time series data and is mostly compatible with the Graph panel. The Heatmap plugin generates heatmaps from time series data using the Epoch visualization library and allows users to visualize latency, utilization, and time series trends without missing details. Future plans include supporting pre-calculated histogram and heatmap data from various data sources and improving compatibility with the Graph panel.
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/nZzHFwaoMpU
In this presentation, we will demonstrate the integration of H2O Driverless.ai with NetApp Cloud Volumes Service. In addition, we’ll describe key considerations for the development of Deep Learning environments and the solutions that enable seamless data management across edge environments, on-premises data centers, and the cloud. This presentation is targeted for data scientists, data engineers, and line of business leaders.
Vinod comes with over 10 years of Marketing & Data Science experience in multiple startups. He was the founding employee for his previous startup, Activehours, where he helped build the product and bootstrap the user acquisition with growth hacking. He has seen the user base for his companies grow from scratch to millions of customers. He’s built models to score leads, reduce churn, increase conversion, prevent fraud and many more use cases. He brings a strong analytical side and an metrics driven approach to marketing.
Dataiku big data paris - the rise of the hadoop ecosystemDataiku
This document discusses the rise of the Hadoop ecosystem. It outlines how the ecosystem has expanded from the original Hadoop components of HDFS for storage and MapReduce for distributed computation. New frameworks have emerged that allow for real-time queries, updates, and machine learning on big data. These include Spark, Storm, Drill, and streaming engines. The ecosystem is now a complex network of interoperable tools for storage, computation, analytics and machine learning on large datasets.
Building Analytic Apps for SaaS: “Analytics as a Service”Amazon Web Services
TIBCO Jaspersoft® for AWS is a business intelligence suite that helps you deliver stunning interactive reports and dashboards inside your app that make it easy for your customers to get answers. Purpose-built for AWS, our reporting and analytics server quickly and easily connects to Amazon Relational Database Service (RDS), Amazon Redshift, and Amazon EMR. It includes ad-hoc reporting, dashboards, data analysis, data visualization, and data blending. In less than 10 minutes, you can be analyzing and reporting on your data. You get a full Cloud BI server starting at less than $1/hour, with no user or data limits and no additional fees.
This webinar deck shows how embeddable analytics with TIBCO Jaspersoft for AWS gives you the power to create the experience your end users demand and how to scale and manage that experience across your customer base with AWS.
The document discusses various concepts and techniques related to performance-driven front-end development, including:
- Layout, reflow, and repaint processes that occur when elements change on a page.
- Examples of performance issues like layout thrashing caused by unnecessary reflows, and solutions like minimizing DOM changes and using requestAnimationFrame.
- Tools for measuring performance like the DevTools timeline and ways to optimize aspects like page loading, images, fonts, animations, scrolling, and memory leaks.
Dev tools rendering & memory profilingOpen Academy
Chrome DevTools provide tools for debugging, profiling, and optimizing web page performance. They allow developers to visualize the rendering process, debug issues, and find opportunities to reduce layout reflows and repaints. Key features include the timeline panel to show paint rectangles and composited layer borders, heap snapshots to analyze memory usage, and memory profiling to detect leaks and optimize memory management. Using DevTools, developers can diagnose performance bottlenecks and ensure smooth interactions at 60 frames per second.
Softshake 2013: Introduction to NoSQL with CouchbaseTugdual Grall
This presentation was delivered during Softshake 2013. Learn why RDBMS are not enought and why NoSQL help developers to scale their applications and provide agility.
Mihai Nuta has over 14 years of experience developing computer systems and applications. He has extensive experience with technologies like Visual Basic, SQL, Oracle, and .NET. Currently he works as a senior programmer analyst at Xerox Corporation developing applications for General Motors, including a legal document application and tools for processing images and documents. He has strong skills in databases, web and client/server development, and software like Microsoft Office, SQL Server, and Visual Studio.
Datomic is a database that uses an immutable data model where data is never updated or erased, only added to over time. It uses distributed transactions where transactions are processed serially by transactors and changes are transmitted to peers. Peers cache data and submit transactions, handling queries using a merged view of the cache and latest data from the transactor. The data model consists of immutable datoms that are entities, attributes, values, and the transaction time, allowing queries over time.
Wix Architecture at Scale - QCon London 2014Aviran Mordo
In this talk I will go over Wix's architecture, how we evolved our system to be highly available even at the worst case scenarios when everything can break, how we built a self-healing eventual consistency system for website data distribution and will show some of the patterns we use that helps us render 45M websites while maintaining a relatively low number of servers.
The document provides an overview of JAMStack, a new approach to building web applications that uses JavaScript, APIs, and markup. It defines JAMStack as using JavaScript in the browser as a runtime, reusable HTTP APIs instead of app-specific databases, and prebuilt markup for delivery. It discusses different types of JAMStack projects including static HTML sites, sites with content from a CMS, web applications, and large websites. It also outlines advantages like improved performance, security, and scalability, as well as considerations for planning a JAMStack project such as managing content, choosing a site generator, automation, and CDNs.
- SignalR provides a simple way to add real-time web functionality to applications. It allows for persistent connections and messaging between servers and clients.
- It abstracts away the various techniques for real-time communication like websockets, long polling, and server-sent events and chooses the best transport.
- SignalR uses hubs to facilitate two-way communication between clients and servers through methods. This allows for different message types and structures to be sent.
This document provides an overview of NoSQL databases and then discusses Amazon DynamoDB in more depth. It explains that NoSQL databases are an alternative to relational databases for certain data-intensive applications. It then discusses DynamoDB specifically, highlighting that it is a fully managed NoSQL database that provides fast and predictable performance, flexible data model, automatic scaling, and pay per request pricing. The document also provides examples of applications that were built on DynamoDB as part of a challenge.
This document provides an overview of NoSQL databases and then discusses Amazon DynamoDB in more depth. It explains that NoSQL databases are an alternative to relational databases for certain data-intensive applications. It then discusses DynamoDB specifically, highlighting that it is a fully managed NoSQL database that provides fast and predictable performance, flexible data model, automatic scaling, and pay per request pricing. The document also provides examples of applications that were built on DynamoDB as part of a challenge.
This document provides an overview of architecting cloud applications for scale. It discusses key concepts like horizontal scaling, distributed computing, and common cloud architecture patterns. Specific examples are given of how large companies like Facebook, Twitter, and Flickr architect their systems using horizontal scaling, partitioning, caching, and other techniques to handle massive loads in a scalable way.
JS Fest 2019/Autumn. Александр Товмач. JAMstackJSFestUA
Вы уже слышали о JAMstack, который пришел на смену SSR и SPA? Подход, который оптимизирует веб приложения так, что они ограничены только скоростью вашего интернет соединения. Никаких просадок при рендере на клиенте, никаких падений серверов от нагрузки, только SEO-friendly приложения без проблем с масштабируемостью.
TiConnect: Memory Management in Titanium appsTim Poulsen
Slides from my presentation at the 2014 ConnectJS / TiConnect conference in Atlanta. I cover tips and background info on managing memory and performance in your Titanium apps.
Basic performance application optimization techniques that can be applied to any application, from web to desktop or mobile, but with focus on php/mysql stack. How to identify bottlenecks and resolve them and what strategies to choose to avoid them upfront.
Live presentation:
https://www.youtube.com/watch?v=aas8oM7CLjk
QuestDB: ingesting a million time series per second on a single instance. Big...javier ramirez
In this session I will show you the technical decisions we made when building QuestDB, the open source, Postgres compatible, time-series database, and how we can achieve a million row writes per second without blocking or slowing down the reads.
This document provides an overview of the tools available in Chrome Dev Tools for web development and performance analysis. It describes the Elements, Styles, and Resources panels for inspecting and editing pages. It also covers the Network panel for analyzing resource loading, the Timeline for performance profiling, and the Audits and PageSpeed panels for optimization suggestions. Tips are provided on using these various Dev Tools to debug issues, optimize pages, and remotely debug on devices.
The document provides an overview of best practices for web development, including developing iteratively, using AJAX to improve performance and usability, and optimizing for speed and user experience. It recommends developing and deploying early to get quick feedback, using AJAX and JSON to asynchronously update parts of the interface without reloading, and prioritizing usability, readability, and simplicity over technical features.
Similar to IMA Lab: Indianapolis Museum of Art Collection Page Redesign (20)
Securing BGP: Operational Strategies and Best Practices for Network Defenders...APNIC
Md. Zobair Khan,
Network Analyst and Technical Trainer at APNIC, presented 'Securing BGP: Operational Strategies and Best Practices for Network Defenders' at the Phoenix Summit held in Dhaka, Bangladesh from 23 to 24 May 2024.
HijackLoader Evolution: Interactive Process HollowingDonato Onofri
CrowdStrike researchers have identified a HijackLoader (aka IDAT Loader) sample that employs sophisticated evasion techniques to enhance the complexity of the threat. HijackLoader, an increasingly popular tool among adversaries for deploying additional payloads and tooling, continues to evolve as its developers experiment and enhance its capabilities.
In their analysis of a recent HijackLoader sample, CrowdStrike researchers discovered new techniques designed to increase the defense evasion capabilities of the loader. The malware developer used a standard process hollowing technique coupled with an additional trigger that was activated by the parent process writing to a pipe. This new approach, called "Interactive Process Hollowing", has the potential to make defense evasion stealthier.
Honeypots Unveiled: Proactive Defense Tactics for Cyber Security, Phoenix Sum...APNIC
Adli Wahid, Senior Internet Security Specialist at APNIC, delivered a presentation titled 'Honeypots Unveiled: Proactive Defense Tactics for Cyber Security' at the Phoenix Summit held in Dhaka, Bangladesh from 23 to 24 May 2024.
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8. A web page that is delivered to the user exactly as
stored, in contrast to dynamic web pages which are
generated by a web application.
Static web pages are suitable for content that never or
rarely needs to be updated. However, maintaining large
numbers of static pages as files can be impractical
without automated tools.
Any personalization or interactivity has to be run client
side.
STATIC WEB PAGE
Dagwood concept: all layers of the sandwich rep an isolated data source
Not the first time: Mercury – antiquated, complex (java, maven, with a lot of dependencies), fragile, missing data sources, little internal skill or will
Drawing of the sources, with dagwood in the middle, isolating the new datasources vs mercury
Tags (Steve), extended text
Both well proven, stable technologies that are highly unlikely to drastically change (angular)
Could have stopped here and refactored the drupal page to use the new middleware
All this data comes from a database. Every time the page is loaded.
Caching can help, but only for anonymous users.
Typical drupal page can be well over 100 db queries
Wanted to use this opportunity to enhance the collection pages
Grunt, assemble.io, markdown
Sync methodology – update versus rebuild – instead of matching records, less bookkeeping - much less error prone
- Object tiles
R
-From September 2013-October 2014, mobile and tablet users to imamuseum.org have increased to 40% of our overall website traffic.
COLLECTION SECTION
-Visitors view more pages (4 pages/session) and stay 1 minute longer than on pages throughout the rest of the site. 4 minute average visit.
-Within the collection section, users visit more than one artwork 45% of the time and are returning to search and browse.
-Overall, users who visit the collection section are staying in the collection section or going to Visit.
Overall we wanted to design these pages to have a more clean, mobile first user interface approach.
The result is a more streamlined user experience with an object. The goal of these pages is to encourage exploration and discoverability of objects throughout our collection.
Addition of Search the Collection in navigation
More minimal navigation, no footer (caters to users who already stay in this section)
Bring more presence to object imagery
Zoom functionality
Full screen viewer
Alternate object views
Rights and reproductions info / Image DL info
Object status (on view/not on view)
Immediately pop down to info/tombstone information if you’d like
For objects that have lots of content, there is now a greater emphasis on research items unique to the IMA.
(photography, gallery labels, provenance information, related text, multimedia content,, collection data)
Our hope is to expand this into more info unique to the IMA ex: conservation, archives
Object information/tombstone
Addition of colors
Eventual linking of all fields
Encourage cross-collection exploration with increased visibility of the newly renamed “You May Also Like” vs More Like This and future browse items