This is a talk I gave about Offline First development at jsDay Verona on May 14th, 2015 and TopConf Tallinn on November 18th, 2015 .
It covers why and when we should prepare our web apps for the offline state, which browser capabilities help us to accomplish the job and how we can detect the offline state for a better UI.
A refresher on IndexedDB, how the API evolved from its initial versions.
Example of LINQ on IndexedDB, IndexedDB-Jquery Plugin and some requirements from IndexedDB
Hive LLAP: A High Performance, Cost-effective Alternative to Traditional MPP ...DataWorks Summit
At Walmart Labs, we get close to 200 million customers every week across our 11000+ stores & online all over the world. As part of our data lake initiatives, we started a full-fledged migration to Hadoop based solutions for all our data needs at lower cost than traditional RDBMS/MPP solutions. While we have seen significant success in migrating to Hadoop based Data Lake solutions from traditional RDBMS based data warehouses, one challenge that we have faced is around migrating end users to Hadoop due to query latency issues. To solve this problem and to reduce the cost of the solution, Walmart Labs started using Hive LLAP.
In this session, we will introduce you to Hive LLAP, its architecture, best practices for deployment to achieve sub-second query performance and its cost comparison with traditional RDBMS systems for the same use case.
Detailed illustration of Apple Watch onboarding flow with some UX analysis, criticism and suggestions. A lot of screen shots and photos.
User onboarding is the process of improving a person's success with a product or service.
Hadoop Training | Hadoop Training For Beginners | Hadoop Architecture | Hadoo...Simplilearn
This presentation about Hadoop training will help you understand the need for Hadoop, what is Hadoop and concepts including Hadoop ecosystem, Hadoop features, how HDFS works, what is MapReduce and how YARN works. Finally, we will implement a banking case study using Hadoop. To solve the issue of rapidly increasing data, we need big data technologies such as Hadoop, Spark, Storm, Cassandra and many more. Hadoop can store and process vast volumes of data. You will understand the architecture of HDFS, MapReduce workflow and the architecture of YARN. In the demo, you will learn in detail on how to export data from RDBMS (MySQL) into HDFS using Sqoop commands. Now, let us get started and gain expertise with Hadoop training video.
Below topics are explained in this Hadoop training presentation:
1. Need for Hadoop
2. What is Hadoop
3. Hadoop ecosystem
4. Hadoop features
5. What is HDFS
6. What is MapReduce
7. What is YARN
8. Bank case study
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Aphrodite (Venus), goddess' liaisons. Famous Paintings.guimera
One of the most worshipped of Greek deities, Aphrodite is the goddess of love, beauty, and procreation.
Has a pretty wild love life, she provides Olympus with more scandal and trouble than it could ever want.
Hadoop Infrastructure @Uber Past, Present and FutureDataWorks Summit
Uber’s mission is to provide transportation as reliable as running water and for fulfilling that mission data plays a critical role. In Uber, Hadoop plays a critical role in Data Infrastructure. We want to talk about the journey of Hadoop @Uber and our future plans in terms of scaling for billions of trips. We will talk about most unique use case Uber have and how Hadoop and eco system which we built, helped us in this journey. We want to talk about how we scaled from 10 -> 2000 and In future to scale up to 10’s X1000 of Nodes. We will talk about our mistakes, learning and wins and how we process billions of events per day. We will talk about the unique challenges and real world use-cases and how we will co-locate the Uber’s service architecture with batch (e.g data pipelines, machine learning and analytical workloads). Uber have done lot of improvements to current Hadoop eco system and uniquely solved some of the problems in a way which is never been solved in the past. This presentation will help audience to use this as an example and even encourage them to enhance the eco system. This will help to increase the community of these project and overall help the whole big data space. Audience is anybody who is working on Big Data and want to understand how to scale Hadoop and eco system for 10s of thousands of node. This talk will help them understand the Hadoop ecosystem and how to efficiently use that. It will also introduce them to some of the awesome technologies which Uber team is building in big data space.
A refresher on IndexedDB, how the API evolved from its initial versions.
Example of LINQ on IndexedDB, IndexedDB-Jquery Plugin and some requirements from IndexedDB
Hive LLAP: A High Performance, Cost-effective Alternative to Traditional MPP ...DataWorks Summit
At Walmart Labs, we get close to 200 million customers every week across our 11000+ stores & online all over the world. As part of our data lake initiatives, we started a full-fledged migration to Hadoop based solutions for all our data needs at lower cost than traditional RDBMS/MPP solutions. While we have seen significant success in migrating to Hadoop based Data Lake solutions from traditional RDBMS based data warehouses, one challenge that we have faced is around migrating end users to Hadoop due to query latency issues. To solve this problem and to reduce the cost of the solution, Walmart Labs started using Hive LLAP.
In this session, we will introduce you to Hive LLAP, its architecture, best practices for deployment to achieve sub-second query performance and its cost comparison with traditional RDBMS systems for the same use case.
Detailed illustration of Apple Watch onboarding flow with some UX analysis, criticism and suggestions. A lot of screen shots and photos.
User onboarding is the process of improving a person's success with a product or service.
Hadoop Training | Hadoop Training For Beginners | Hadoop Architecture | Hadoo...Simplilearn
This presentation about Hadoop training will help you understand the need for Hadoop, what is Hadoop and concepts including Hadoop ecosystem, Hadoop features, how HDFS works, what is MapReduce and how YARN works. Finally, we will implement a banking case study using Hadoop. To solve the issue of rapidly increasing data, we need big data technologies such as Hadoop, Spark, Storm, Cassandra and many more. Hadoop can store and process vast volumes of data. You will understand the architecture of HDFS, MapReduce workflow and the architecture of YARN. In the demo, you will learn in detail on how to export data from RDBMS (MySQL) into HDFS using Sqoop commands. Now, let us get started and gain expertise with Hadoop training video.
Below topics are explained in this Hadoop training presentation:
1. Need for Hadoop
2. What is Hadoop
3. Hadoop ecosystem
4. Hadoop features
5. What is HDFS
6. What is MapReduce
7. What is YARN
8. Bank case study
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Aphrodite (Venus), goddess' liaisons. Famous Paintings.guimera
One of the most worshipped of Greek deities, Aphrodite is the goddess of love, beauty, and procreation.
Has a pretty wild love life, she provides Olympus with more scandal and trouble than it could ever want.
Hadoop Infrastructure @Uber Past, Present and FutureDataWorks Summit
Uber’s mission is to provide transportation as reliable as running water and for fulfilling that mission data plays a critical role. In Uber, Hadoop plays a critical role in Data Infrastructure. We want to talk about the journey of Hadoop @Uber and our future plans in terms of scaling for billions of trips. We will talk about most unique use case Uber have and how Hadoop and eco system which we built, helped us in this journey. We want to talk about how we scaled from 10 -> 2000 and In future to scale up to 10’s X1000 of Nodes. We will talk about our mistakes, learning and wins and how we process billions of events per day. We will talk about the unique challenges and real world use-cases and how we will co-locate the Uber’s service architecture with batch (e.g data pipelines, machine learning and analytical workloads). Uber have done lot of improvements to current Hadoop eco system and uniquely solved some of the problems in a way which is never been solved in the past. This presentation will help audience to use this as an example and even encourage them to enhance the eco system. This will help to increase the community of these project and overall help the whole big data space. Audience is anybody who is working on Big Data and want to understand how to scale Hadoop and eco system for 10s of thousands of node. This talk will help them understand the Hadoop ecosystem and how to efficiently use that. It will also introduce them to some of the awesome technologies which Uber team is building in big data space.
Agenda:
1.Data Flow Challenges in an Enterprise
2.Introduction to Apache NiFi
3.Core Features
4.Architecture
5.Demo –Simple Lambda Architecture
6.Use Cases
7.Q & A
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
This is the presentation I made on the Hadoop User Group Ireland meetup in Dublin. It covers the main ideas of both MPP, Hadoop and the distributed systems in general, and also how to chose the best option for you
Hive, Impala, and Spark, Oh My: SQL-on-Hadoop in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Presented by Cloudant Developer Advocate, Bradley Holt.
Web and mobile apps shouldn’t stop working when there’s no network connection. Bradley Holt demonstrates how to use the HTML5 offline application cache, PouchDB, and CouchDB to build offline-enabled responsive mobile and web apps.
Based on Apache CouchDB, PouchDB is an open source syncing JavaScript database that runs within a web browser. Offline-first apps built using PouchDB can provide a better, faster user experience—both on- and offline. Bradley discusses how to use PouchDB with Cordova/PhoneGap, Ionic, and CouchDB to build fully-featured, cross-platform native/hybrid apps or high-fidelity prototypes. PouchDB can also be run within Node.js and on devices for Internet of Things (IoT) applications.
Bradley provides code examples for creating a PouchDB database, creating a new document, updating a document, deleting a document, querying a database, syncing PouchDB with a remote database, and live updates to a user interface based on database changes. Bradley will also discuss user-interface patterns for offline-first apps.
Agenda:
1.Data Flow Challenges in an Enterprise
2.Introduction to Apache NiFi
3.Core Features
4.Architecture
5.Demo –Simple Lambda Architecture
6.Use Cases
7.Q & A
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
This is the presentation I made on the Hadoop User Group Ireland meetup in Dublin. It covers the main ideas of both MPP, Hadoop and the distributed systems in general, and also how to chose the best option for you
Hive, Impala, and Spark, Oh My: SQL-on-Hadoop in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Presented by Cloudant Developer Advocate, Bradley Holt.
Web and mobile apps shouldn’t stop working when there’s no network connection. Bradley Holt demonstrates how to use the HTML5 offline application cache, PouchDB, and CouchDB to build offline-enabled responsive mobile and web apps.
Based on Apache CouchDB, PouchDB is an open source syncing JavaScript database that runs within a web browser. Offline-first apps built using PouchDB can provide a better, faster user experience—both on- and offline. Bradley discusses how to use PouchDB with Cordova/PhoneGap, Ionic, and CouchDB to build fully-featured, cross-platform native/hybrid apps or high-fidelity prototypes. PouchDB can also be run within Node.js and on devices for Internet of Things (IoT) applications.
Bradley provides code examples for creating a PouchDB database, creating a new document, updating a document, deleting a document, querying a database, syncing PouchDB with a remote database, and live updates to a user interface based on database changes. Bradley will also discuss user-interface patterns for offline-first apps.
Offline-first web application development leads to faster apps and a better user experience, but is it realistic? It's hard enough to think about "mobile-first". And what if your code needs to run on a smart phone, in a browser, and as an installed desktop application? Do you really have time to implement "offline-first" for all these platforms and their variants? Thanks to a combination of open source packages including PouchDB, React, and Electron, it's now possible to write one offline-first web application that runs everywhere.
Realism, heroism, bravery, boldness or cowardiceAgha A
'Heroism' and 'realism', 'bravery' or 'cowardice' are powerful words pregnant with multiple meanings and thus often misunderstood in common discussion. This is not exactly an article but a cursory examination of how certain individuals in various stages of world history made remarkable achievements by being 'Heroic' 'Realistic' etc.
The 'Hero' is a man who does not surrender in face of overwhelming odds and thus emerges 'victorious' or is perceived by posterity to have been morally victorious despite having been physically destroyed.
Khalid Bin Waleed, Napoleon, Alexander, Churchill etc may be grouped in the first cate-gory and Joan of Arc, Syed Ahmad Shaheed may be grouped in the latter category. All these men did well and are even today well known figures in history.
We will first examine the issue in relation with the fact 'Whether the hero had an exact knowledge and sufficient time' to assess decisions that he made and which ultimately elevated him to the pedestal of a hero in history! This is important but very often forgotten or not understood at all by many. We will take the 'Rebels' or the 'Freedom Fighters' of 1857 as an example. All existing facts as we know them today prove that these 'Rebels' never really understood the real power and potential of the English East India Company.
Private Engineering Colleges in GurgaonDronacharya
Dronacharya College of Engineering offers the students with learning atmosphere with best facilities, and pleasant educational environment. The Institute provides best engineering courese and placement communication skills for the development of students.
Career Guidance In Engineering - DronacharyaDronacharya
If you want to make career in engineering and best guidance other way to visite Drona Institute is best in all engineering courses & get 100% Placements.
High Performance/Real-Time Web Applications can suffer from serial program execution, which can greatly decrease user experience, usability, application capabilities and overall performance. The new HTML5 WebWorker JavaScript API allows for multithreading in browser environment, which has removed serial code bottleneck that has always been an issue for processor intensive applications. Specifically at Game Theory Labs we were able to increase the performance of our application by 55% utilizing the techniques discussed. This meetup will show off the variations in the WebWorker API, associated overhead using the API, various WebWorker architectures (Inline vs External, Static vs Dynamic, Nested vs Shared) as well as implementing a 2-Tier Thread Management system that allows for generating child process outside of the main thread thereby increasing performance of handling/merging data between threads and the main application.
SUMMARY :
We all have the contradictory feeling to deliver not-so-bad projects, with no-so-bad performances.
But what really is an perfectly optimized project ?
For you : optimized PHP code & SQL queries
For your boss : the customer who never complains
For the customer : own experience on his workstation
For the business : who really know and care ?
For end-user : who can really know the end-user experience (could be millions of users) ?
Without losing interest on technical aspects (PHP, MySql, Solr, Varnish, CDN, etc.) & softwares (new relic, jmeter, etc.), this presentation will send a feedback from real projects to :
How to integrate performances within the project scope ?
What & how to measure & collect smart metrics ?
Enlarge the scope : from your dev workstation to the end-user… in china !
Experience level: Intermediate
Session Track: Performance
Everybody knows Javascript is single-threaded and that it shares this same thread with other browser-related processes such as painting and compositing. There are several techniques to implement pseudo multithreading in JavaScript; however, during this talk we will focus our attention on how to use and debug the Service Worker API. Our end goal is to explore practical use cases in order to simplify the process to render complex user interfaces and transitions in a browser.
slides of a presentation about cross-platform mobile app development I gave at MobileTechCon 2010 in Mainz (Germany).
Links and additional information on the related blog post at http://HeikoBehrens.net/2010/10/11/cross-platform-app-development-for-iphone-android-co-—-a-comparison-i-presented-at-mobiletechcon-2010/
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
18. App cache gotchas
1.filesalwayscomefromthecache
server side generated websites
single page applications
2.onlyupdatesifmanifestfilechange
3.needtoswapthecachemanually
serverappcache
user
browser
applicationCache.swapCache();
22. Caching mechanisms
dynamicdata
xhr, remote api calls
.json, .xml, .csv
xhr, remote api calls
indexedDB
webSql
webstorage
localforage
pouchDB
}lack of browser support -
api is overly complex -
23. Using web storage
no online
connection required
sessionstorage or localstorage
key/value store
appcachestorage
user
browser
24. Using web storage APIs
currentHighscore
lastMove
levelsCompleted
1890
D4E5
12
keys values
25. Using web storage APIs
localStorage.setItem("lastMove", "D4E5");
localStorage.getItem("levelsCompleted");
currentHighscore
lastMove
levelsCompleted
1890
D4E5
12
27. asynchronous
Why don’t we just use…indexedDB?
webstorage indexedDB
synchronous
limited to strings supports large data sets
no indexes indexes
10mb storage 50mb storage
47. static and dynamic data
with remote server
what to take offline
if offline-first makes sense
useevents
prepareui for flawless user experience
consider
decide
to detect offline state
cache
sync