Couchbase is a complete NoSQL database solution for big data. It provides a distributed database that can scale horizontally. Couchbase uses a document-oriented data model and supports the CAP theorem. It sacrifices consistency to achieve high availability and partition tolerance. Couchbase is used by many large companies for applications that involve large, complex datasets with high user volumes and real-time requirements.
In the times of rapid app development, we need better ways to quickly develop interactive web applications and that is where JavaScript frameworks such as angularJS come to the rescue. The slides discuss how the tech stack evolved, the architectural concepts behind them and the usage of such frameworks along-with few other technologies to use together
The document discusses building highly scalable Java applications on Windows Azure. It provides an overview of Windows Azure, including its compute and storage services. It then covers how to deploy and run Java applications on Azure, including using Tomcat, Jetty, GlassFish, and accessing SQL Azure and storage. It discusses current limitations and how the Eclipse tools will support Java development for Azure. Finally, it covers architectural approaches for scaling applications, comparing vertical to horizontal scaling.
The document discusses the history and concepts of NoSQL databases. It notes that traditional single-processor relational database management systems (RDBMS) struggled to handle the increasing volume, velocity, variability, and agility of data due to various limitations. This led engineers to explore scaled-out solutions using multiple processors and NoSQL databases, which embrace concepts like horizontal scaling, schema flexibility, and high performance on commodity hardware. Popular NoSQL database models include key-value stores, column-oriented databases, document stores, and graph databases.
This document contains a summary of Krishnakumar Rajendran's skills and experience. He has 6 years of experience developing responsive web applications using technologies like HTML, CSS, JavaScript, jQuery, AngularJS, and Bootstrap. He has expertise in full SDLC processes and agile methodologies. His experience includes developing single page applications, unit testing, and working with version control systems like Git and SVN. He has worked as a front end developer for clients in the US and India building web applications and user interfaces.
NoSQL databases get a lot of press coverage, but there seems to be a lot of confusion surrounding them, as in which situations they work better than a Relational Database, and how to choose one over another. This talk will give an overview of the NoSQL landscape and a classification for the different architectural categories, clarifying the base concepts and the terminology, and will provide a comparison of the features, the strengths and the drawbacks of the most popular projects (CouchDB, MongoDB, Riak, Redis, Membase, Neo4j, Cassandra, HBase, Hypertable).
Web Component Development Using Servlet & JSP Technologies (EE6) - Chapter 1...WebStackAcademy
Let's see take an example:
Deploy Your Application to Oracle Application Container Cloud Service
Extract the content of the employees-app.zip file in your local system.
Log in to Oracle Cloud at http://cloud.oracle.com/. Enter your account credentials in the Identity Domain, User Name, and Password fields.
In the Oracle Cloud Services dashboard, click the Action menu Menu, and select Application Container.
In the Applications list view, click Create Application and select Java EE.
In the Application section, enter a name for your application and click Browse.
On the File Upload dialog box, select the employee-app.war file located in the target directory and click Open.
Keep the default values in the Instances and Memory fields and click Create.
Wait until the application is created. The URL is enabled when the creation is completed.
Click the URL of your application.
Presention on Facebook in f Distributed systemsAhmad Yar
Facebook is a social networking website where users can post comments, share photographs and post links to news or other interesting content on the web, chat live, and watch short-form video. You can even order food on Facebook if that's what you want to do. Shared content can be made publicly accessible, or it can be shared only among a select group of friends or family, or with a single person
This document provides an overview of cloud computing. It begins with defining cloud computing and outlining its key characteristics: broad network access, resource pooling, elasticity, measured service, and self-service. It then discusses the benefits of cloud computing for organizations, including reducing costs, improving scalability and agility. It also covers the main cloud service models of IaaS, PaaS and SaaS. The document concludes with an overview of common cloud products and services, deployment models of public, private and hybrid clouds, and a quick recap of the key topics.
In the times of rapid app development, we need better ways to quickly develop interactive web applications and that is where JavaScript frameworks such as angularJS come to the rescue. The slides discuss how the tech stack evolved, the architectural concepts behind them and the usage of such frameworks along-with few other technologies to use together
The document discusses building highly scalable Java applications on Windows Azure. It provides an overview of Windows Azure, including its compute and storage services. It then covers how to deploy and run Java applications on Azure, including using Tomcat, Jetty, GlassFish, and accessing SQL Azure and storage. It discusses current limitations and how the Eclipse tools will support Java development for Azure. Finally, it covers architectural approaches for scaling applications, comparing vertical to horizontal scaling.
The document discusses the history and concepts of NoSQL databases. It notes that traditional single-processor relational database management systems (RDBMS) struggled to handle the increasing volume, velocity, variability, and agility of data due to various limitations. This led engineers to explore scaled-out solutions using multiple processors and NoSQL databases, which embrace concepts like horizontal scaling, schema flexibility, and high performance on commodity hardware. Popular NoSQL database models include key-value stores, column-oriented databases, document stores, and graph databases.
This document contains a summary of Krishnakumar Rajendran's skills and experience. He has 6 years of experience developing responsive web applications using technologies like HTML, CSS, JavaScript, jQuery, AngularJS, and Bootstrap. He has expertise in full SDLC processes and agile methodologies. His experience includes developing single page applications, unit testing, and working with version control systems like Git and SVN. He has worked as a front end developer for clients in the US and India building web applications and user interfaces.
NoSQL databases get a lot of press coverage, but there seems to be a lot of confusion surrounding them, as in which situations they work better than a Relational Database, and how to choose one over another. This talk will give an overview of the NoSQL landscape and a classification for the different architectural categories, clarifying the base concepts and the terminology, and will provide a comparison of the features, the strengths and the drawbacks of the most popular projects (CouchDB, MongoDB, Riak, Redis, Membase, Neo4j, Cassandra, HBase, Hypertable).
Web Component Development Using Servlet & JSP Technologies (EE6) - Chapter 1...WebStackAcademy
Let's see take an example:
Deploy Your Application to Oracle Application Container Cloud Service
Extract the content of the employees-app.zip file in your local system.
Log in to Oracle Cloud at http://cloud.oracle.com/. Enter your account credentials in the Identity Domain, User Name, and Password fields.
In the Oracle Cloud Services dashboard, click the Action menu Menu, and select Application Container.
In the Applications list view, click Create Application and select Java EE.
In the Application section, enter a name for your application and click Browse.
On the File Upload dialog box, select the employee-app.war file located in the target directory and click Open.
Keep the default values in the Instances and Memory fields and click Create.
Wait until the application is created. The URL is enabled when the creation is completed.
Click the URL of your application.
Presention on Facebook in f Distributed systemsAhmad Yar
Facebook is a social networking website where users can post comments, share photographs and post links to news or other interesting content on the web, chat live, and watch short-form video. You can even order food on Facebook if that's what you want to do. Shared content can be made publicly accessible, or it can be shared only among a select group of friends or family, or with a single person
This document provides an overview of cloud computing. It begins with defining cloud computing and outlining its key characteristics: broad network access, resource pooling, elasticity, measured service, and self-service. It then discusses the benefits of cloud computing for organizations, including reducing costs, improving scalability and agility. It also covers the main cloud service models of IaaS, PaaS and SaaS. The document concludes with an overview of common cloud products and services, deployment models of public, private and hybrid clouds, and a quick recap of the key topics.
Do you need Ops in your new startup? If not now, then when? And...what is Ops?
Learn how to scale ruby-based distributed software infrastructure in the cloud to serve 4,000 requests per second, handle 400 updates per second, and achieve 99.97% uptime – all while building the product at the speed of light.
Unimpressed? Now try doing the above altogether without the Ops team, while growing your traffic 100x in 6 months and deploying 5-6 times a day!
It could be a dream, but luckily it's a reality that could be yours.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
ZK MVVM, Spring & JPA On Two PaaS CloudsSimon Massey
1) The document discusses deploying a Java MVVM sample application called ZkToDo2 to two Platform as a Service (PaaS) clouds: Heroku and Openshift.
2) The application uses ZK, Spring, and JPA with a relational database and follows the MVVM pattern. Data bindings in ZK allow the view to be updated automatically based on changes to the view model.
3) Maven build profiles are used to swap Spring configurations to deploy the same codebase to different platforms like JBoss or clouds. The document demonstrates committing changes locally and deploying to both clouds with a single command.
I'm from California and often the only database administrator (DBA) that tech startups in the area will consult with. This document discusses the use of multiple database technologies at different companies, including MySQL, Oracle, Hadoop, and various NoSQL databases. It argues that while a relational database can solve most problems, different specialized databases are needed for the remaining 10% of problems, and that database experts should expand their skills beyond a single database.
Cloud Foundry is an open platform as a service (PaaS) that supports building, deploying, and scaling applications. It uses a loosely coupled, distributed architecture with no single point of failure. The core components include cloud controllers, stagers, routers, execution agents, and services that communicate asynchronously through messaging. This allows the components to be scaled independently and provides a self-healing system.
Lessons from Large-Scale Cloud Software at DatabricksMatei Zaharia
1) Building cloud software presents unique challenges compared to on-premise software, such as the need for faster release cycles, upgrades without regressions, and multitenancy.
2) Scaling issues are a major cause of outages for cloud systems, including problems reaching resource limits and insufficient isolation between users.
3) Testing cloud systems requires evaluating how they scale and handling varying loads, and failures can indicate problems with dimensions like output size or number of tasks.
From Obvious to Ingenius: Incrementally Scaling Web Apps on PostgreSQLKonstantin Gredeskoul
In this exciting and informative talk, presented at PgConf Sillicon Valley 2015, Konstantin cut through the theory to deliver a clear set of practical solutions for scaling applications atop PostgreSQL, eventually supporting millions of active users, tens of thousands concurrently, and with the application stack that responds to requests with a 100ms average. He will share how his team solved one of the biggest challenges they faced: effectively storing and retrieving over 3B rows of "saves" (a Wanelo equivalent of Instagram's "like" or Pinterest's "pin"), all in PostgreSQL, with highly concurrent random access.
Over the last three years, the team at Wanelo optimized the hell out of their application and database stacks. Using PostgreSQL version 9 as their primary data store, Joyent Public Cloud as a hosting environment, the team re-architected their backend for rapid expansion several times over, as the unrelenting traffic kept climbing up. This ultimately resulted in a highly efficient, horizontally scalable, fault tolerant application infrastructure. Unimpressed? Now try getting there without the OPS or DBA teams, all while deploying seven times per day to production, with an application measuring 99.999% uptime over the last 6 months.
Non-relational databases were developed to address the problems that traditional relational databases have in handling web-scale applications with massive amounts of data and users. They sacrifice consistency to gain availability and partition tolerance. Examples include BigTable, HBase, Dynamo, and Cassandra. They provide benefits like massive scalability, high availability, and elasticity through techniques like consistent hashing, replication, and MapReduce processing.
Developing Distributed Web Applications, Where does REST fit in?Srinath Perera
This document discusses distributed web applications and the roles of SOA and REST architectures. It defines distributed applications as those composed of many machines to handle load and provide high availability. SOA uses stateless processing units and a shared data store, while REST (Representational State Transfer) realizes ROA (Resource Oriented Architecture) through resources that support GET, PUT, POST, DELETE operations. The document uses an example of a network management application to illustrate how each approach would structure resources and operations. It also discusses REST principles and implementation, as well as when each approach is most appropriate.
The document discusses cloud computing and designing applications for scalability and availability in the cloud. It covers key considerations for moving to the cloud like design for failure, building loosely coupled systems, implementing elasticity, and leveraging different storage options. It also discusses challenges like application scalability and availability and how to address them through patterns like caching, partitioning, and implementing elasticity. The document uses examples like MapReduce to illustrate how to build applications that can scale horizontally across infrastructure in the cloud.
Writing simple web services in java using eclipse editorSantosh Kumar Kar
This is a simple steps showing how you can write a simple web service, host into a server, write a client class to access the service on web server. Just for a beginners...
The document discusses the typical 7 stages of scaling a web application as it grows in popularity and usage. Stage 1 involves a simple initial architecture. Stage 2 adds more redundant components to improve performance and availability as usage grows. Stages 3-5 involve significant pain as the application is pushed to its limits, requiring re-architecting and partitioning of databases and services. Stages 6-7 enter more unknown territory as major bottlenecks are addressed and capabilities are expanded to replicate data across geographies. Key practices for scaling include designing for it from the start, isolating services, optimizing after stability is ensured, and establishing processes for releases and change management.
CloudConnect 2011 - Building Highly Scalable Java Applications on Windows AzureDavid Chou
This document discusses building highly scalable Java applications on Windows Azure. It provides an overview of Windows Azure, including its infrastructure and services. It then covers how to deploy and run Java applications on Azure, including using various Java application servers like Tomcat, Jetty, and GlassFish. It also discusses some considerations for architecting applications to scale on Azure.
The NoSQL movement has introduced four new database architectural patterns that complement, but not replace, traditional relational and analytical databases. This presentation will introduce these four patterns and discuss their relative strengths and weaknesses for solving a variety of business problems. These problems include Big Data (scalability), search, high availability and agility. For each type of problem we look at how NoSQL databases take different approaches to solving these problems and how you can use this knowledge to find the right database architecture for your business challenges.
The document discusses choosing between SQL and NoSQL databases. It covers the evolution of data architectures from traditional client-server models to newer distributed NoSQL solutions. It provides an overview of different data store types like SQL, NoSQL, key-value, document, column family, and graph databases. The document advises picking the right data model based on business needs, use cases, data storage requirements, and growth patterns then evaluating solutions based on pros and cons. It concludes that for large, growing data, both SQL and NoSQL solutions may be needed.
Mahmoud Abdallah Mahmoud is the head of the developer vertical at the Microsoft Tech Club at Sohag University Faculty of Engineering. He gave a presentation on databases and Microsoft SQL Server that covered relational database features including tables, primary keys, and defining relationships. The presentation included a demonstration of SQL queries and an overview of career opportunities with Microsoft SQL Server certifications.
Sql vs NO-SQL database differences explainedSatya Pal
This document compares SQL and NoSQL databases. It outlines key differences between the two types of databases such as their data structures (tables vs documents/key-value pairs), schemas (strict vs dynamic), scalability (vertical vs horizontal), and query languages (SQL vs unstructured). Examples of popular SQL databases discussed are MySQL, MS-SQL Server, and Oracle. Examples of NoSQL databases discussed are MongoDB, CouchDB, and Redis. The document provides an overview of each example database's features and benefits.
Why NBC Universal Migrated to MongoDB AtlasDatavail
NBCUniversal, a worldwide mass media corporation, was looking for a more affordable and easier way to manage their database solution that hosts their extensive online digital assets. With Datavail’s assistance, NBCUniversal made the move from MongoDB 3.6 to MongoDB Atlas on AWS.
In this presentation, learn how making this move enabled the entertainment titan to reduce overhead and labor costs associated with managing its database environment.
This document discusses developing Azure solutions for different audiences including web developers, corporate developers, and ISV developers. It covers key aspects of developing Azure solutions such as cloud service anatomy, the differences in developing for Azure, worker and web role call order, migrating data and services to Azure, diagnostics, and best practices. The conclusion emphasizes that Azure provides flexibility in development with specific APIs, casual development scenarios, best practices, and supporting technologies.
This document discusses NoSQL databases and compares them to relational databases. It provides information on different types of NoSQL databases, including key-value stores, document databases, wide-column stores, and graph databases. The document outlines some use cases for each type and discusses concepts like eventual consistency, CAP theorem, and polyglot persistence. It also covers database architectures like replication and sharding that provide high availability and scalability.
Do you need Ops in your new startup? If not now, then when? And...what is Ops?
Learn how to scale ruby-based distributed software infrastructure in the cloud to serve 4,000 requests per second, handle 400 updates per second, and achieve 99.97% uptime – all while building the product at the speed of light.
Unimpressed? Now try doing the above altogether without the Ops team, while growing your traffic 100x in 6 months and deploying 5-6 times a day!
It could be a dream, but luckily it's a reality that could be yours.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
ZK MVVM, Spring & JPA On Two PaaS CloudsSimon Massey
1) The document discusses deploying a Java MVVM sample application called ZkToDo2 to two Platform as a Service (PaaS) clouds: Heroku and Openshift.
2) The application uses ZK, Spring, and JPA with a relational database and follows the MVVM pattern. Data bindings in ZK allow the view to be updated automatically based on changes to the view model.
3) Maven build profiles are used to swap Spring configurations to deploy the same codebase to different platforms like JBoss or clouds. The document demonstrates committing changes locally and deploying to both clouds with a single command.
I'm from California and often the only database administrator (DBA) that tech startups in the area will consult with. This document discusses the use of multiple database technologies at different companies, including MySQL, Oracle, Hadoop, and various NoSQL databases. It argues that while a relational database can solve most problems, different specialized databases are needed for the remaining 10% of problems, and that database experts should expand their skills beyond a single database.
Cloud Foundry is an open platform as a service (PaaS) that supports building, deploying, and scaling applications. It uses a loosely coupled, distributed architecture with no single point of failure. The core components include cloud controllers, stagers, routers, execution agents, and services that communicate asynchronously through messaging. This allows the components to be scaled independently and provides a self-healing system.
Lessons from Large-Scale Cloud Software at DatabricksMatei Zaharia
1) Building cloud software presents unique challenges compared to on-premise software, such as the need for faster release cycles, upgrades without regressions, and multitenancy.
2) Scaling issues are a major cause of outages for cloud systems, including problems reaching resource limits and insufficient isolation between users.
3) Testing cloud systems requires evaluating how they scale and handling varying loads, and failures can indicate problems with dimensions like output size or number of tasks.
From Obvious to Ingenius: Incrementally Scaling Web Apps on PostgreSQLKonstantin Gredeskoul
In this exciting and informative talk, presented at PgConf Sillicon Valley 2015, Konstantin cut through the theory to deliver a clear set of practical solutions for scaling applications atop PostgreSQL, eventually supporting millions of active users, tens of thousands concurrently, and with the application stack that responds to requests with a 100ms average. He will share how his team solved one of the biggest challenges they faced: effectively storing and retrieving over 3B rows of "saves" (a Wanelo equivalent of Instagram's "like" or Pinterest's "pin"), all in PostgreSQL, with highly concurrent random access.
Over the last three years, the team at Wanelo optimized the hell out of their application and database stacks. Using PostgreSQL version 9 as their primary data store, Joyent Public Cloud as a hosting environment, the team re-architected their backend for rapid expansion several times over, as the unrelenting traffic kept climbing up. This ultimately resulted in a highly efficient, horizontally scalable, fault tolerant application infrastructure. Unimpressed? Now try getting there without the OPS or DBA teams, all while deploying seven times per day to production, with an application measuring 99.999% uptime over the last 6 months.
Non-relational databases were developed to address the problems that traditional relational databases have in handling web-scale applications with massive amounts of data and users. They sacrifice consistency to gain availability and partition tolerance. Examples include BigTable, HBase, Dynamo, and Cassandra. They provide benefits like massive scalability, high availability, and elasticity through techniques like consistent hashing, replication, and MapReduce processing.
Developing Distributed Web Applications, Where does REST fit in?Srinath Perera
This document discusses distributed web applications and the roles of SOA and REST architectures. It defines distributed applications as those composed of many machines to handle load and provide high availability. SOA uses stateless processing units and a shared data store, while REST (Representational State Transfer) realizes ROA (Resource Oriented Architecture) through resources that support GET, PUT, POST, DELETE operations. The document uses an example of a network management application to illustrate how each approach would structure resources and operations. It also discusses REST principles and implementation, as well as when each approach is most appropriate.
The document discusses cloud computing and designing applications for scalability and availability in the cloud. It covers key considerations for moving to the cloud like design for failure, building loosely coupled systems, implementing elasticity, and leveraging different storage options. It also discusses challenges like application scalability and availability and how to address them through patterns like caching, partitioning, and implementing elasticity. The document uses examples like MapReduce to illustrate how to build applications that can scale horizontally across infrastructure in the cloud.
Writing simple web services in java using eclipse editorSantosh Kumar Kar
This is a simple steps showing how you can write a simple web service, host into a server, write a client class to access the service on web server. Just for a beginners...
The document discusses the typical 7 stages of scaling a web application as it grows in popularity and usage. Stage 1 involves a simple initial architecture. Stage 2 adds more redundant components to improve performance and availability as usage grows. Stages 3-5 involve significant pain as the application is pushed to its limits, requiring re-architecting and partitioning of databases and services. Stages 6-7 enter more unknown territory as major bottlenecks are addressed and capabilities are expanded to replicate data across geographies. Key practices for scaling include designing for it from the start, isolating services, optimizing after stability is ensured, and establishing processes for releases and change management.
CloudConnect 2011 - Building Highly Scalable Java Applications on Windows AzureDavid Chou
This document discusses building highly scalable Java applications on Windows Azure. It provides an overview of Windows Azure, including its infrastructure and services. It then covers how to deploy and run Java applications on Azure, including using various Java application servers like Tomcat, Jetty, and GlassFish. It also discusses some considerations for architecting applications to scale on Azure.
The NoSQL movement has introduced four new database architectural patterns that complement, but not replace, traditional relational and analytical databases. This presentation will introduce these four patterns and discuss their relative strengths and weaknesses for solving a variety of business problems. These problems include Big Data (scalability), search, high availability and agility. For each type of problem we look at how NoSQL databases take different approaches to solving these problems and how you can use this knowledge to find the right database architecture for your business challenges.
The document discusses choosing between SQL and NoSQL databases. It covers the evolution of data architectures from traditional client-server models to newer distributed NoSQL solutions. It provides an overview of different data store types like SQL, NoSQL, key-value, document, column family, and graph databases. The document advises picking the right data model based on business needs, use cases, data storage requirements, and growth patterns then evaluating solutions based on pros and cons. It concludes that for large, growing data, both SQL and NoSQL solutions may be needed.
Mahmoud Abdallah Mahmoud is the head of the developer vertical at the Microsoft Tech Club at Sohag University Faculty of Engineering. He gave a presentation on databases and Microsoft SQL Server that covered relational database features including tables, primary keys, and defining relationships. The presentation included a demonstration of SQL queries and an overview of career opportunities with Microsoft SQL Server certifications.
Sql vs NO-SQL database differences explainedSatya Pal
This document compares SQL and NoSQL databases. It outlines key differences between the two types of databases such as their data structures (tables vs documents/key-value pairs), schemas (strict vs dynamic), scalability (vertical vs horizontal), and query languages (SQL vs unstructured). Examples of popular SQL databases discussed are MySQL, MS-SQL Server, and Oracle. Examples of NoSQL databases discussed are MongoDB, CouchDB, and Redis. The document provides an overview of each example database's features and benefits.
Why NBC Universal Migrated to MongoDB AtlasDatavail
NBCUniversal, a worldwide mass media corporation, was looking for a more affordable and easier way to manage their database solution that hosts their extensive online digital assets. With Datavail’s assistance, NBCUniversal made the move from MongoDB 3.6 to MongoDB Atlas on AWS.
In this presentation, learn how making this move enabled the entertainment titan to reduce overhead and labor costs associated with managing its database environment.
This document discusses developing Azure solutions for different audiences including web developers, corporate developers, and ISV developers. It covers key aspects of developing Azure solutions such as cloud service anatomy, the differences in developing for Azure, worker and web role call order, migrating data and services to Azure, diagnostics, and best practices. The conclusion emphasizes that Azure provides flexibility in development with specific APIs, casual development scenarios, best practices, and supporting technologies.
This document discusses NoSQL databases and compares them to relational databases. It provides information on different types of NoSQL databases, including key-value stores, document databases, wide-column stores, and graph databases. The document outlines some use cases for each type and discusses concepts like eventual consistency, CAP theorem, and polyglot persistence. It also covers database architectures like replication and sharding that provide high availability and scalability.
Big data is generated from a variety of sources at a massive scale and high velocity. Hadoop is an open source framework that allows processing and analyzing large datasets across clusters of commodity hardware. It uses a distributed file system called HDFS that stores multiple replicas of data blocks across nodes for reliability. Hadoop also uses a MapReduce processing model where mappers process data in parallel across nodes before reducers consolidate the outputs into final results. An example demonstrates how Hadoop would count word frequencies in a large text file by mapping word counts across nodes before reducing the results.
The document describes an experiment comparing three big data analysis platforms: Apache Hive, Apache Spark, and R. Seven identical analyses of clickstream data were performed on each platform, and the time taken to complete each operation was recorded. The results showed that Spark was faster for queries involving transformations of big data, while R was faster for operations involving actions on big data. The document provides details on the hardware, software, data, and specific analytical tasks used in the experiment.
ارائه در زمینه کلان داده،
کارگاه آموزشی "عصر کلان داده، چرا و چگونه؟" در بیست و دومین کنفرانس انجمن کامپیوتر ایران csicc2017.ir
وحید امیری
vahidamiry.ir
datastack.ir
Big data is characterized by 3 V's - volume, velocity, and variety. It refers to large and complex datasets that are difficult to process using traditional database management tools. Key technologies to handle big data include distributed file systems, Apache Hadoop, data-intensive computing, and tools like MapReduce. Common tools used are infrastructure management tools like Chef and Puppet, monitoring tools like Nagios and Ganglia, and analytics platforms like Netezza and Greenplum.
In this paper we describe NoSQL, a series of non-relational database
technologies and products developed to address the current problems the
RDMS system are facing: lack of true scalability, poor performance on high
data volumes and low availability. Some of these products have already been
involved in production and they perform very well: Amazon’s Dynamo,
Google’s Bigtable, Cassandra, etc. Also we provide a view on how these
systems influence the applications development in the social and semantic Web
sphere.
In this paper we describe NoSQL, a series of non-relational database technologies and products developed to address the current problems the RDMS system are facing: lack of true scalability, poor performance on high data volumes and low availability. Some of these products have already been involved in production and they perform very well: Amazon’s Dynamo, Google’s Bigtable, Cassandra, etc. Also we provide a view on how these systems influence the applications development in the social and semantic Web sphere.
The document discusses the rise of NoSQL databases. It notes that NoSQL databases are designed to run on clusters of commodity hardware, making them better suited than relational databases for large-scale data and web-scale applications. The document also discusses some of the limitations of relational databases, including the impedance mismatch between relational and in-memory data structures and their inability to easily scale across clusters. This has led many large websites and organizations handling big data to adopt NoSQL databases that are more performant and scalable.
The document provides an overview of Big Data technology landscape, specifically focusing on NoSQL databases and Hadoop. It defines NoSQL as a non-relational database used for dealing with big data. It describes four main types of NoSQL databases - key-value stores, document databases, column-oriented databases, and graph databases - and provides examples of databases that fall under each type. It also discusses why NoSQL and Hadoop are useful technologies for storing and processing big data, how they work, and how companies are using them.
The document provides an overview of big data and Hadoop, discussing what big data is, current trends and challenges, approaches to solving big data problems including distributed computing, NoSQL, and Hadoop, and introduces HDFS and the MapReduce framework in Hadoop for distributed storage and processing of large datasets.
Les mégadonnées représentent un vrai enjeu à la fois technique, business et de société
: l'exploitation des données massives ouvre des possibilités de transformation radicales au
niveau des entreprises et des usages. Tout du moins : à condition que l'on en soit
techniquement capable... Car l'acquisition, le stockage et l'exploitation de quantités
massives de données représentent des vrais défis techniques.
Une architecture big data permet la création et de l'administration de tous les
systèmes techniques qui vont permettre la bonne exploitation des données.
Il existe énormément d'outils différents pour manipuler des quantités massives de
données : pour le stockage, l'analyse ou la diffusion, par exemple. Mais comment assembler
ces différents outils pour réaliser une architecture capable de passer à l'échelle, d'être
tolérante aux pannes et aisément extensible, tout cela sans exploser les coûts ?
Le succès du fonctionnement de la Big data dépend de son architecture, son
infrastructure correcte et de son l’utilité que l’on fait ‘’ Data into Information into Value ‘’.
L’architecture de la Big data est composé de 4 grandes parties : Intégration, Data Processing
& Stockage, Sécurité et Opération.
Big data analytics: Technology's bleeding edgeBhavya Gulati
There can be data without information , but there can not be information without data.
Companies without Big Data Analytics are deaf and dumb , mere wanderers on web.
Hadoop - Architectural road map for Hadoop Ecosystemnallagangus
This document provides an overview of an architectural roadmap for implementing a Hadoop ecosystem. It begins with definitions of big data and Hadoop's history. It then describes the core components of Hadoop, including HDFS, MapReduce, YARN, and ecosystem tools for abstraction, data ingestion, real-time access, workflow, and analytics. Finally, it discusses security enhancements that have been added to Hadoop as it has become more mainstream.
This document provides an overview of architecting a first big data implementation. It defines key concepts like Hadoop, NoSQL databases, and real-time processing. It recommends asking questions about data, technology stack, and skills before starting a project. Distributed file systems, batch tools, and streaming systems like Kafka are important technologies for big data architectures. The document emphasizes moving from batch to real-time processing as a major opportunity.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
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Devise and implement a test strategy in order to perform a comparative analysis of the capabilities of two database management systems (Cassandra and HBase) in terms of performance.
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Findings: HBase performs insertion, reading, and updating of records faster than Cassandra but only when the operations count is less. At heavier loads, Cassandra performs better than Hbase.
Tools: Hbase, Cassandra, Hadoop, Tableau, YCSB
Similar to CouchBase The Complete NoSql Solution for Big Data (20)
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2. CAP Theorum
Before we get into big data and the role of NOSQL, we must first
understand the CAP theorem. In theoretical computer science, the
CAP theorem, also known as Brewer's theorem, states that it is
impossible for a distributed computer system to simultaneously
provide all three of the following guarantees
1. Consistency (all nodes see the same data at the same time)
2. Availability (a guarantee that every request receives a response about
whether it succeeded or failed)
3. Partition tolerance (the system continues to operate despite arbitrary
message loss or failure of part of the system)
Although all three are impossible to achieve, any two can be achieved
by the systems. That means in order to get high availability and
partition tolerance, you need to sacrifice consistency
3.
4. The 5 Vs of Big Data
• Big data is a broad term for data sets so large or complex that
traditional data processing applications are inadequate.
• Challenges include analysis, capture, data curation,
search, sharing, storage, transfer, visualization,
and information privacy.
• We currently only see the beginnings of a transformation into a
big data economy. Any business that doesn’t seriously
consider the implications of Big Data runs the risk of being left
behind.
• To get a better understanding of what Big Data is, it is often
described using 5 Vs: Volume Velocity Variety Veracity Value
5. Volume
Volume Refers to the vast amounts of data generated
every second. We are not talking Terabytes but
Zettabytes or Brontobytes. If we take all the data
generated in the world between the beginning of time
and 2008, the same amount of data will soon be
generated every minute. This makes most data sets too
large to store and analyse using traditional database
technology. New big data tools use distributed systems
so that we can store and analyse data across databases
that are dotted around anywhere in the world
6. Variety
Variety Refers to the different types of data we
can now use. In the past we only focused on
structured data that neatly fitted into tables or
relational databases, such as financial data. In
fact, 80% of the world’s data is unstructured
(text, images, video, voice, etc.) With big data
technology we can now analyse and bring
together data of different types such as
messages, social media conversations, photos,
sensor data, video or voice recordings.
7. Velocity
Velocity Refers to the speed at which new data
is generated and the speed at which data moves
around. Just think of social media messages
going viral in seconds. Technology allows us
now to analyze the data while it is being
generated (sometimes referred to as in-memory
analytics), without ever putting it into databases.
8. Veracity & Value
Veracity refers to truthfulness, correctness
of the data.
Value! Having access to big data is no
good unless we can turn it into value.
Companies are starting to generate
amazing value from their big data.
9. Big Data and Human Brain
To understand how big data could be solution architected, let’s try to
understand how human brain is architected.
So the key is parallel processing. Hureeyyyyyy!!!
10. Hadoop & MapReduce
• In 2004, Google published a paper on a process called MapReduce that
used such an architecture.
• The MapReduce framework provides a parallel processing model and
associated implementation to process huge amounts of data. With
MapReduce, queries are split and distributed across parallel nodes and
processed in parallel (the Map step). The results are then gathered and
delivered (the Reduce step). The framework was very successful, so others
wanted to replicate the algorithm. Therefore, an implementation of the
MapReduce framework was adopted by an Apache open source project
named Hadoop.
• But Hadoop is only for processing the data. How can we store this huge
data?
11. NoSql Database
• A NoSQL (often interpreted as Not only SQL) database often used in big data-centric
real-time web applications, provides a mechanism for storage and retrieval of data
that is modeled in means other than the tabular relations used in relational
databases.
• Motivations for this approach include simplicity of design, horizontal scaling, and finer
control over availability. The data structures used by NoSQL databases (e.g. key-
value, graph, or document) differ from those used in relational databases, making
some operations faster in NoSQL and others faster in relational databases.
• The particular suitability of a given NoSQL database depends on the problem it must
solve.
12. Types of NoSQL databases
• There have been various approaches to classify NoSQL databases, each
with different categories and subcategories. Because of the variety of
approaches and overlaps it is difficult to get and maintain an overview of
non-relational databases. Nevertheless, a basic classification is based on
data model. A few examples in each category are:
• Column: Accumulo, Cassandra, Druid, HBase, Vertica
• Document: Lotus Notes, Clusterpoint, Apache CouchDB, Couchbase,
MarkLogic, MongoDB, OrientDB, Qizx
• Key-value: CouchDB, Dynamo, FoundationDB, MemcacheDB, Redis, Riak,
FairCom c-treeACE, Aerospike, OrientDB, MUMPS
• Graph: Allegro, Neo4J, InfiniteGraph, OrientDB, Virtuoso, Stardog
• Multi-model: OrientDB, FoundationDB, ArangoDB, Alchemy Database,
CortexDB
13. Graph Database
• This kind of database is designed for data
whose relations are well represented as a
graph (elements interconnected with an
undetermined number of relations
between them). The kind of data could be
social relations, public transport links, road
maps or network topologies.
14. Key-value stores
• In this model, data is represented as a
collection of key-value pairs, such that
each possible key appears at most once
in the collection. The key-value model is
one of the simplest non-trivial data
models, and richer data models are often
implemented on top of it.
15. Document-oriented databases
• The central concept of a document store is the notion of a
"document". While each document-oriented database
implementation differs on the details of this definition, in general,
they all assume that documents encapsulate and encode data (or
information) in some standard formats or encodings. Encodings in
use include XML, JSON as well as binary forms like BSON.
• The most widely used solutions in no-sql are MongoDB and
CouchBase and both of them are document-oriented databases.
• Here is a sample document:
{
'_id' : '5897g42s0245afo4o473ai1e7',
'firstname': 'John',
'lastname': 'Doe',
'age': 26,
'sex': 'M',
'interests': [ 'Reading', 'Running', 'Hacking' ]
}
19. Scalability
• In Couchbase, you can easily add servers to do clustering and
obtain a distributed system, Couchbase is flexible enough to avoid
downtime. Indeed, it relies on the power of the Erlang language, a
functional and fault-tolerant language that manages hot changes.
• For MongoDB, the configuration is a bit more complicated. For
example, once you have defined the shard key (the key to distribute
documents within a sharded cluster), it becomes difficult to change it
afterwards. The system is not as flexible, so you have to think
carefully about your data modeling before you move your
application into production.
• Scalability is why Couchbase is widely used in social gaming, where
millions of players can play and their numbers can increase
exponentially overnight.
20. Monitoring tool
Couchbase comes with a turnkey package while MongoDB requires an
additional subscription to a monitoring service. You can monitor MongoDB
using the command line, but a monitoring tool without graphical interface is
relatively restrictive.
21. Introducing CouchBase
• Couchbase provides the world’s most complete, most scalable and
best performing NoSQL database.
• Based on a share nothing architecture, a single node-type, a built in
caching layer, true auto-sharding and the world’s first NoSQL mobile
offering: Couchbase Mobile, a complete NoSQL mobile solution
comprised of Couchbase Server, Couchbase Sync Gateway and
Couchbase Lite.
• Clients: AT&T, Amadeus, Bally’s, Beats Music, Cisco, Comcast,
Concur, Disney, eBay / PayPal, Neiman Marcus, Orbitz, Rakuten /
Viber, Sky, Tencent, Tesco, Verizon and Willis Group, as well as
hundreds of other household names worldwide
22. Real life Use Cases
Couchbase Server’s unique combinations could be 1) linear, horizontal
scalability, 2) sustained low latency and high throughput performance, and
3) the extensibility of the system.
Few usecases:
• Session store: User sessions are easily stored and managed in
Couchbase, for instance, by using the document ID naming scheme,
“user:USERID”. With Couchbase Server, you can flag items for deletion
after a certain amount of time, and therefore you have the option of having
Couchbase automatically delete old sessions.
• Social gaming: You can model and store game state, property state, time
lines, conversations and chats with Couchbase Server. The asynchronous
persistence algorithms of Couchbase were designed, built and deployed to
support some of the highest scale social games.
• Ad, offer, and content targeting: The same attributes which serve
Couchbase in the gaming context also apply well for real-time ad and
content targeting. For example, Couchbase provides a fast storage
capability for counters. Counters are useful for tracking visits, associating
users with various targeting profiles, tracking ad-offers, and for tracking ad-
inventory.
23. Buckets
• Couchbase Server stores all of your application data in either RAM or on disk. The
data containers used in Couchbase Server are called buckets; there are two bucket
types in Couchbase, which reflect the two types of data storage that we use in
Couchbase Server. Buckets also serve as namespaces for documents and are used
to look up a document by key:
• Couchbase Buckets
• Memcached Buckets
• You can customize the properties of each bucket, within limits using Couchbase
Admin Console, Couchbase Command Line Interface (CLI), or the Couchbase REST
Admin API. Quotas for RAM and disk space can be configured per bucket so you can
manage usage across a cluster
• Couchbase Server is best suited for fast-changing data items of relatively small size.
For in-memory storage, using Couchbase Memcached buckets, the memcached
standard 1 megabyte limit applies to each value. Items suitable for storage include
shopping carts, user profile, user sessions, time lines, game states, pages,
conversations and product catalog. Items that are less suitable include large audio or
video media files.
• On that note, some Couchbase SDKs offer the additional feature of optionally
compressing/decompressing objects stored into Couchbase. The CPU-time versus
space trade-off here should be considered
24. Couchbase Buckets
• Couchbase Buckets: provide data persistence and data replication. Data
stored in Couchbase Buckets is highly-available and reconfigurable without
server downtime. They can survive node failures and restore data plus allow
cluster reconfiguration while still fulfilling service requests. The main
features are:
– Supports items up to 20MB in size.
– Persistence, including data sets that are larger than the allocated memory size
for a bucket. You can configure persistence per bucket and Couchbase Server
will persist data asynchronously from RAM to disk
– Fully supports replication and server rebalancing. You can configure one or more
replica servers for a Couchbase bucket. If a node fails, a replica node can be
promoted to be the host node.
– Full range of statistics supported.
25. Memcached Buckets
• Memcached Buckets: provides in-memory document storage. Memcache
buckets cache frequently-used data in memory, thereby reducing the
number of queries a database server must perform in response to web
application requests. Memcached buckets can work alongside relational
database technology, not only NoSQL databases.
– Item size limited to 1 MByte.
– No persistence.
– No replication; no rebalancing.
– Statistics about Memcached Buckets are on RAM usage and client-side
operations.
26. Keys & Metadata
• All information that you store in Couchbase Server are documents with keys, unique identifiers
for a document, and values are either JSON documents or if you choose the data you want to
store can be byte stream, data types, or other forms of serialized objects.
• Keys are also known as document IDs and serve the same function as a SQL primary key. A key
in Couchbase Server can be any string and is unique.
• By default, all documents contain metadata that is provided by the Couchbase Server. The
metadata is stored with the document and is used to change how the document is handled.
• CAS Value—Also called CAS token or CAS ID, this value is a unique identifier associated with a
document that is verified by the Couchbase Server before a document is deleted or changed and
provides a form of basic optimistic concurrency. When Couchbase Server checks a CAS value
before changing data, it effectively prevents data loss without having to lock records. Couchbase
Server prevents a document from being altered by an operation if another process alters the
document and its CAS value, in the meantime.
• Time to Live (TTL)—This is an expiration for a document typically specified in seconds. By
default, any document created in Couchbase Server that does not have a given TTL will have an
indefinite life span and will remain in Couchbase Server unless an explicit delete call from a client
removes it. The Couchbase Server will delete values during regular maintenance if the TTL for an
item has expired.
Note: The expiration value deletes information from the entire database. It has no effect on when
the information is removed from the RAM caching layer.
• Flags—These are SDK- specific flags which are used to provides a variety of options during
storage, retrieval, update, and removal of documents. Typically flags are optional metadata used
by a Couchbase client library to perform additional processing of a document. An example of
flags include the ability to specify that a document be formatted a specific way before it is stored.
27. Creating First Application
Components for your development environment:
• Couchbase Server: installed on a virtual or physical machine separate from the machine
containing your web application server. Download the appropriate version for your environment
here http://www.couchbase.com/download
• Couchbase SDK: installed for runtime on the machine containing your web application server.
You will also need to make the SDKs available in your development environment in order to
compile/interpret your client-side code. The SDKs are programming-language and platform-
specific. You will use your SDK to communicate with the Couchbase Server from your web
application. Downloads for your chosen SDK are here: http://www.couchbase.com/develop
• Couchbase Admin Console: administering your Couchbase Server is done via the Couchbase
Admin Console, a web application viewable in most modern browsers. Your development
environment should therefore have the latest version of Mozilla Firefox 3.6+, Apple Safari 5+,
Google Chrome 11, or Internet Explorer 8, or higher. You should set your browser preference to
be JavaScript enabled.
The development languages supported by the Couchbase Client SDK Libraries are Java, .NET,
PHP, Ruby, C
28. Connecting A Bucket
• After you have your Couchbase Server up and running, and your
chosen Couchbase Client libraries installed on a web server, you
create the code that connects to the server from the client.
1. Make a new bucket request to the REST endpoint for buckets and
provide the new bucket settings as request parameters:
shell> curl -u Administrator:password
2. -d name=newBucket -d ramQuotaMB=100 -d authType=none
3. -d replicaNumber=1 -d proxyPort=11215
http://localhost:8091/pools/default/buckets
29. Connecting to Couchbase Server
The following shows a basic steps for creating a connection:
• Include, import, link, or require Couchbase SDK libraries into your program
files. In the example that follows, we require 'couchbase'.
• Provide connection information for the Couchbase cluster. Typically this
includes URI, bucket ID, a password and optional parameters and can be
provided as a list or string. To avoid failure to initially connect, you should
provide and try at least two URL’s for two different nodes. In the following
example, we provide connection information as"http://<host>:<port>/pools".
In this case there is no password required.
• Create an instance of a Couchbase client object. In the example that
follows, we create a new client instance in the client =
Couchbase.connect statement.
• Perform any database operations for your applications, such as read, write,
delete, or query.
• If needed, destroy the client, and therefore disconnect.
30. Connecting to Couchbase Server..
• The below example in Java we demonstrate how it is safest to
create at least two possible node URIs while creating an initial
connection with the server. This way, if your application attempts to
connect, but one node is down, the client automatically re-attempts
to connect with the second node URL:
// Set up at least two URIs in case one server fails
List<URI> servers = new ArrayList<URI>();
servers.add("http://<host>:8091/pools");
servers.add("http://<host>:8091/pools");
// Create a client talking to the default bucket
CouchbaseClient cbc = new CouchbaseClient(servers, "default", "");
// Create a client talking to the default bucket
CouchbaseClient cbc = new CouchbaseClient(servers, "default", "");
System.err.println(cbc.get(“thisname") + " is off developing with Couchbase!");