March 10, 2016 - Hadoop Meeting. Rajiv presentation on starting Scala @airisdata. An introduction to programming in SCALA from the REPL with live examples.
This document provides an introduction to functional programming and Scala. It discusses key concepts of functional programming like immutable data, recursion, and higher order functions. It also outlines some pros like improved parallelization and modularity, and cons like a steeper learning curve. Finally, it introduces Scala as a functional language for the JVM that combines OOP and FP, and Akka as a library for building distributed and concurrent applications in Scala using an actor model.
The document summarizes a Scala for Java developers meetup that covered why Scala, an introduction to basic Scala concepts, and a quick demo of Akka actors. Some key differences between Scala and Java highlighted include Scala's support for both object-oriented and functional programming, its ability to reduce lines of code through type inference, and its treatment of functions and numbers as objects. The meetup also covered Scala classes, case classes, pattern matching, traits, generics, and the Akka framework for building highly concurrent distributed applications on the JVM.
This document discusses dependency injection in functional programming and Swift. It explains that dependency injection means providing an object its instance variables. It discusses why dependency injection is used such as clear declaration of dependencies, composition, and testability. It also discusses different frameworks that can be used for dependency injection in Swift like Swiftz and Swift Injection.
Functional programming has started (re)gaining prominence in recent years, and with good reason too. Functional programs lend an elegant solution to the concurrency problem, result in more modular systems, are more concise and are easier to test. While modern languages like Scala and Clojure have embraced the functional style whole-heartedly, Java has lagged a bit behind in its treatment of functions as first-class citizens. With the advent of Java 8 and its support for lambdas, however, Java programmers can finally start reaping the power of functional programs as well. Even without Java 8, it is possible to adopt a functional style with the aid of excellent libraries such as Guava.
This document provides an overview of the Scala programming language. Some key points:
- Scala combines object-oriented and functional programming which allows it to be used for both small scripts and large systems. It runs on the Java Virtual Machine.
- Scala supports immutable and mutable data. Functions are first-class values that can be passed as arguments or returned from other functions.
- Scala code is more concise than Java by omitting semicolons and not requiring explicit data types. Variables start with 'var' and values start with 'val'.
- Scala is compatible with Java but provides additional features like richer type systems and built-in functional programming constructs.
The document introduces Lambda Behave, a new testing framework for Java 8 that aims to make testing a pleasant experience. It uses Java 8 features like lambda expressions to provide a neat environment for writing tests. Some key features include fluent specification writing, data-driven testing, integration with IDEs and ScalaTest. Specifications can be written in fluent English-like style, generated automatically, or parameterized based on data inputs. The framework was recently launched and is gaining popularity for its features and benefits over tools like JUnit. SPEC India offers Java development services including projects using Lambda Behave.
This document provides an introduction to the Scala programming language. It discusses that Scala was created by Martin Odersky in 2003 as a hybrid functional and object-oriented language that runs on the Java Virtual Machine. Some key points made include that Scala code compiles to Java bytecode, allows importing of Java libraries, and supports features like functions as first-class objects. The document also provides examples of Hello World programs in Scala and Java for comparison, and demonstrates basic Scala concepts like variables, conditionals, loops, and lists.
The document discusses introducing Scala for web development. It provides an overview of Scala's features like static typing, functional programming support, and frameworks. It then recommends a path for easing into Scala web development by starting with an existing Java/Spring project and gradually adopting Scala. Alternatives like Lift and Akka are also briefly mentioned.
New in Spring Framework 5.0: Functional Web FrameworkVMware Tanzu
In Spring Framework 5.0, we introduced a new, functional web framework, next to the existing annotation-driven programming model. In this talk, we will discuss this new framework: how it is used, what its goals are, how it compares to the annotation model, and other related topics.
Presenter : Arjen Poutsma, Pivotal
Scala is a statically typed programming language that runs on the Java Virtual Machine and interoperates seamlessly with Java. It unifies object-oriented and functional programming in one concise, high-level language. The presentation demonstrated Scala's powerful and lightweight syntax through examples of object-oriented code, collections, parallel programming and a game of life simulation, encouraging questions from the audience.
Ruby on Rails is a web application framework that follows the model-view-controller (MVC) architecture. It is built on Ruby, an object-oriented programming language. Rails emphasizes conventions over configuration, making common tasks simpler. The framework includes features like ActiveRecord for object-relational mapping, generators for scaffolding code, and plugins for extending functionality. Many large companies use Rails for their web applications.
Relay is a JavaScript library developed by Facebook that provides a framework for fetching and managing data for React applications. It uses GraphQL to allow clients to request specific data from an API in a declarative way. Relay handles data fetching, caching, normalization of responses, and passing the data as props to React components. Some key aspects of Relay include its use of containers to fetch data and translate GraphQL responses to JSON, its separation of remote and local data storage, and its composable architecture. While Relay speeds up development and keeps applications flexible, it also has some limitations as an early-stage project such as lack of documentation and examples for some use cases.
Implementing GraphQL API in Elixir – Victor DeryaginElixir Club
The document discusses Teachers Pay Teachers' experience implementing a GraphQL API in Elixir. Some key points:
- TpT has used Elixir and GraphQL for around 2 years to power their API, handling 2000 requests per second and significant backend work.
- Their tech stack includes Phoenix, Absinthe, Ecto, React/Apollo Client. Challenges included access control, evolving schemas, batching queries, and distributed tracing.
- Absinthe provided features like middleware, introspection and separation of schemas. Elixir tools like OTP processes helped with scalability and robustness.
- Overall they are happy with Elixir and GraphQL but note ongoing effort is
Spark UDFs are EviL, Catalyst to the rEsCue!Adi Polak
Processing data at scale usually involves struggling with performance, strict SLA, limited hardware capabilities and more. After struggling with Spark SQL query run-time, I found the felon! In this lecture, I would like to share with you the change in perspective and process we had to go through in order to find the felon (and the solution!). Today in the world of Big Data and Spark we are processing high volume transactions. Catalyst is the Spark SQL query optimizer, in this talk, we will reveal how you can fully utilize Catalyst’s optimization power in order to make queries run as fast as possible, by pushing down actions and avoiding UDFs as much as possible, while still maximizing performance.
GraphQL is a wonderful abstraction for describing and querying data. Apollo is an ambitious project to help you build apps with GraphQL. In this talk, we'll go over how all the parts—Client, Server, Dev Tools, Codegen, and more—create an end-to-end experience for building apps on top of any data.
## Detailed description
In today's development ecosystem, there are tons of options for almost every part of your application development process: UI rendering, styling, server side rendering, build systems, type checking, databases, frontend data management, and more. However, there's one part of the stack that hasn't gotten as much love in the last decade, because it usually falls in the cracks between frontend and backend developers: Data fetching.
The most common way to load data in apps today is to use a REST API on the server and manage the data manually on the client. Whether you're using Redux, MobX, or something else, you're usually doing everything yourself—deciding when to load data, how to keep it fresh, updating the store after sending updates to the server, and more. But if you're trying to develop the best user experience for your app, all of that gets in the way; you shouldn't have to become a systems engineer to create a great frontend. The Apollo project is based on the belief that data loading doesn't have to be complicated; instead, you should be able to easily get the data you want, when you want it, and it should be managed for you just like React manages updating your UI.
Because data loading touches both the frontend and backend of your app, GraphQL and Apollo have to include many parts to fulfill that promise of being able to seamlessly connect your data together. First, we need client libraries not only for React and JavaScript, but also for native iOS and Android. Then, we must bring server-side support for GraphQL queries, mutations, and most recently subscriptions to every server technology and make those servers easier to write. And finally, we want not only all of the tools that people are used to with REST APIs, but many more thanks to all of the capabilities enabled by GraphQL.
In this talk, we'll go over all of the parts of a GraphQL-oriented app architecture, and how different GraphQL and Apollo technologies come together to solve all of the parts of data loading and management for React developers.
Zen and the Art of REST API documentation - MuCon London 2015Steve Judd
Struggling to document your APIs? Still using a wiki page to do it? Watch this presentation and learn the secrets of documenting REST APIs!
One of the secrets to a successful Microservices application is the quality of its APIs - so using a decent tool to help develop them is essential; without the right tool, producing APIs can become as tedious as reading through a dump file. This presentation introduces a selection of opensource tools intended to help with not just documenting APIs but designing and testing them as well. Tools covered will include: Swagger, RAML, Spring RestDocs and Apiary.
Why should a Java programmer shifts towards Functional Programming ParadigmTech Triveni
Functional programming, a buzz word these days. Everyone is trying to learn it. A few functional programming languages are getting attraction and are getting used in the production too such as Scala, Haskell, Clojure and so on. And Even Java has already introduced a few concepts to support functional programming which are Lambdas, Functional Interfaces, Streams, etc. Now the question comes, why should a Java programmer needs to shift towards functional programming.
Java has a very big community and using imperative programming from the last 2 decades. It needs a very solid reason to shift towards the functional programming paradigm. The functional programming paradigm focuses on some key concepts like immutability, higher-order functions and so on and it has a moderate learning curve, but if we can get benefit out of it, then there is no harm in learning functional.
In this talk we will see, what are the benefits we get when we follow the functional programming paradigm in Java, how it makes the developer life easy. We will discuss the comparison between the imperative code vs functional code with some examples.
We will discuss the transition from imperative coding to functional coding with some code examples. This talk will be good for the people who really want to switch to functional programming and want to know how to use that in Java.
This document provides an overview of new features in Java 8, including lambda expressions, streams, date/time APIs, Nashorn JavaScript engine, and improved string joining capabilities. Key points covered include lambda expressions allowing anonymous functions, streams supporting parallel processing of collections, immutable date/time classes, using Nashorn to execute JavaScript from Java code, and the StringJoiner class and String join method for concatenating strings.
This document discusses using static types with GraphQL applications. It begins with an introduction from James Baxley. It then covers describing data with GraphQL's robust type system, using Apollo Client and React Apollo to manage data loading from a GraphQL server, and bringing the server types into the application. It discusses type systems like Flow and TypeScript that can be used, advantages of static types like reduced bugs, and tools that integrate types like Apollo Codegen, Flow Coverage, and VSCode. It emphasizes that GraphQL is strongly typed and tools like the Relay compiler and Apollo Codegen can generate types for GraphQL queries.
Java 8 introduced parallel streams to allow collections to be processed in parallel using multiple threads. While parallel streams can improve performance by splitting work across CPU cores, they also introduce challenges. Parallel streams use the ForkJoinPool by default, so long-running or blocking tasks could impact other parallel operations. Side effects must also be avoided. Parallel streams may not provide benefits with a single CPU or for I/O-bound work. Developers must consider hardware resources and task dependencies to determine if parallel streams are applicable.
This document discusses using FreeMarker as a template engine with Spring MVC instead of JSP. It provides an overview of FreeMarker, how to configure Spring MVC to use it, and examples of FreeMarker syntax including variables, collections, includes, and macros. Commonly used FreeMarker components are demonstrated along with comparisons to JSP syntax.
Lessons Learned: Scala and its EcosystemPetr Hošek
This presentation is motivated by the continuous growth of Scala language popularity thanks to many new concepts it offers. Therefore, it makes a perfect sense to take a further insight on this language. Beside the language itself, its ecosystem is also very important. That is why I will focus on the Scala ecosystem in this presentation.
Automatic Forecasting using Prophet, Databricks, Delta Lake and MLflowDatabricks
As Atlassian continues to scale to more and more customers, the demand for our legendary support continues to grow. Atlassian needs to maintain balance between the staffing levels needed to service this increasing support ticket volume with the budgetary constraints needed to keep the business healthy – automated ticket volume forecasting is at the centre of this delicate balance
This document provides an introduction and overview of LINQ (Language Integrated Query). It defines LINQ as allowing developers to write SQL queries using a simple language like C# without needing to learn SQL. LINQ provides a unified syntax for querying multiple data sources. The document outlines the structure of a LINQ query, shows how to write one, and lists the stages of executing a query. Advantages include a consistent syntax across data sources and syntax highlighting. Examples demonstrate creating a data context class and querying data from a database and objects.
This document discusses Java 8 parallel streams. It begins with an introduction to lambda expressions and streams in Java 8. It then covers parallel streams and how they can automatically parallelize processing. The document demonstrates parallel stream code examples. It also discusses the ForkJoinPool for parallel execution in Java 7. Finally, it notes some potential issues with parallel streams, such as performance degradation on single-core machines or with tasks involving I/O. It recommends only using parallel streams on multi-core machines without significant I/O.
Apresentado na React Conf Brasil, em São Paulo, 7 de Outubro de 2017 #reactconfbr
Entusiasta da open web e software livre. Software Developer na globo.com e membro da JS foundation. Já trabalhou como desenvolvedor em empresas como Videolog e Petrobras. Em 2015, foi um dos desenvolvedores de software mais notáveis no GitHub, tendo feito contribuições significativas para projetos de código aberto por 500 dias seguidos. Orador em mais de vinte conferências dando palestras sobre tecnologias web como JavaScript / ES6, Web Performance, React e Canvas / WebGL.
http://raphamorim.io
@raphamundi
- Patrocínio: Pipefy, Globo.com, Meteor, Apollo, Taller, Fullcircle, Quanto, Udacity, Cubos, Segware, Entria
- Apoio: Concrete, Rung, LuizaLabs, Movile, Rivendel, GreenMile, STQ, Hi Platform
- Promoção: InfoQ, DevNaEstrada, CodamosClub, JS Ladies, NodeBR, Training Center, BrazilJS, Tableless, GeekHunter
- Afterparty: An English Thing
Used this slide at Tokyo Rubyist Meetup (https://trbmeetup.doorkeeper.jp/events/46318) on June 9 2016.
Introduced Rails 5 new features, especially ActionCable.
Tried live coding to build a new application using ActionCable in the presentation.
This document provides an overview and comparison of the Avro and Parquet data formats. It begins with introductions to Avro and Parquet, describing their key features and uses. The document then covers Avro and Parquet schemas, file structures, and includes code examples. Finally, it discusses considerations for choosing between Avro and Parquet and shares experiences using the two formats.
Predict Repeat Shoppers with H20 and SparkairisData
1. The document discusses predicting repeat shoppers of online stores using gradient boosted machines in H2O and Spark.
2. It describes the repeat shopper problem motivation of targeting repeat purchasers who are more lucrative customers.
3. Feature generation steps are outlined using user and seller data like monthly purchase counts, category interactions, and seller similarity metrics to create a large sparse matrix for modeling.
New in Spring Framework 5.0: Functional Web FrameworkVMware Tanzu
In Spring Framework 5.0, we introduced a new, functional web framework, next to the existing annotation-driven programming model. In this talk, we will discuss this new framework: how it is used, what its goals are, how it compares to the annotation model, and other related topics.
Presenter : Arjen Poutsma, Pivotal
Scala is a statically typed programming language that runs on the Java Virtual Machine and interoperates seamlessly with Java. It unifies object-oriented and functional programming in one concise, high-level language. The presentation demonstrated Scala's powerful and lightweight syntax through examples of object-oriented code, collections, parallel programming and a game of life simulation, encouraging questions from the audience.
Ruby on Rails is a web application framework that follows the model-view-controller (MVC) architecture. It is built on Ruby, an object-oriented programming language. Rails emphasizes conventions over configuration, making common tasks simpler. The framework includes features like ActiveRecord for object-relational mapping, generators for scaffolding code, and plugins for extending functionality. Many large companies use Rails for their web applications.
Relay is a JavaScript library developed by Facebook that provides a framework for fetching and managing data for React applications. It uses GraphQL to allow clients to request specific data from an API in a declarative way. Relay handles data fetching, caching, normalization of responses, and passing the data as props to React components. Some key aspects of Relay include its use of containers to fetch data and translate GraphQL responses to JSON, its separation of remote and local data storage, and its composable architecture. While Relay speeds up development and keeps applications flexible, it also has some limitations as an early-stage project such as lack of documentation and examples for some use cases.
Implementing GraphQL API in Elixir – Victor DeryaginElixir Club
The document discusses Teachers Pay Teachers' experience implementing a GraphQL API in Elixir. Some key points:
- TpT has used Elixir and GraphQL for around 2 years to power their API, handling 2000 requests per second and significant backend work.
- Their tech stack includes Phoenix, Absinthe, Ecto, React/Apollo Client. Challenges included access control, evolving schemas, batching queries, and distributed tracing.
- Absinthe provided features like middleware, introspection and separation of schemas. Elixir tools like OTP processes helped with scalability and robustness.
- Overall they are happy with Elixir and GraphQL but note ongoing effort is
Spark UDFs are EviL, Catalyst to the rEsCue!Adi Polak
Processing data at scale usually involves struggling with performance, strict SLA, limited hardware capabilities and more. After struggling with Spark SQL query run-time, I found the felon! In this lecture, I would like to share with you the change in perspective and process we had to go through in order to find the felon (and the solution!). Today in the world of Big Data and Spark we are processing high volume transactions. Catalyst is the Spark SQL query optimizer, in this talk, we will reveal how you can fully utilize Catalyst’s optimization power in order to make queries run as fast as possible, by pushing down actions and avoiding UDFs as much as possible, while still maximizing performance.
GraphQL is a wonderful abstraction for describing and querying data. Apollo is an ambitious project to help you build apps with GraphQL. In this talk, we'll go over how all the parts—Client, Server, Dev Tools, Codegen, and more—create an end-to-end experience for building apps on top of any data.
## Detailed description
In today's development ecosystem, there are tons of options for almost every part of your application development process: UI rendering, styling, server side rendering, build systems, type checking, databases, frontend data management, and more. However, there's one part of the stack that hasn't gotten as much love in the last decade, because it usually falls in the cracks between frontend and backend developers: Data fetching.
The most common way to load data in apps today is to use a REST API on the server and manage the data manually on the client. Whether you're using Redux, MobX, or something else, you're usually doing everything yourself—deciding when to load data, how to keep it fresh, updating the store after sending updates to the server, and more. But if you're trying to develop the best user experience for your app, all of that gets in the way; you shouldn't have to become a systems engineer to create a great frontend. The Apollo project is based on the belief that data loading doesn't have to be complicated; instead, you should be able to easily get the data you want, when you want it, and it should be managed for you just like React manages updating your UI.
Because data loading touches both the frontend and backend of your app, GraphQL and Apollo have to include many parts to fulfill that promise of being able to seamlessly connect your data together. First, we need client libraries not only for React and JavaScript, but also for native iOS and Android. Then, we must bring server-side support for GraphQL queries, mutations, and most recently subscriptions to every server technology and make those servers easier to write. And finally, we want not only all of the tools that people are used to with REST APIs, but many more thanks to all of the capabilities enabled by GraphQL.
In this talk, we'll go over all of the parts of a GraphQL-oriented app architecture, and how different GraphQL and Apollo technologies come together to solve all of the parts of data loading and management for React developers.
Zen and the Art of REST API documentation - MuCon London 2015Steve Judd
Struggling to document your APIs? Still using a wiki page to do it? Watch this presentation and learn the secrets of documenting REST APIs!
One of the secrets to a successful Microservices application is the quality of its APIs - so using a decent tool to help develop them is essential; without the right tool, producing APIs can become as tedious as reading through a dump file. This presentation introduces a selection of opensource tools intended to help with not just documenting APIs but designing and testing them as well. Tools covered will include: Swagger, RAML, Spring RestDocs and Apiary.
Why should a Java programmer shifts towards Functional Programming ParadigmTech Triveni
Functional programming, a buzz word these days. Everyone is trying to learn it. A few functional programming languages are getting attraction and are getting used in the production too such as Scala, Haskell, Clojure and so on. And Even Java has already introduced a few concepts to support functional programming which are Lambdas, Functional Interfaces, Streams, etc. Now the question comes, why should a Java programmer needs to shift towards functional programming.
Java has a very big community and using imperative programming from the last 2 decades. It needs a very solid reason to shift towards the functional programming paradigm. The functional programming paradigm focuses on some key concepts like immutability, higher-order functions and so on and it has a moderate learning curve, but if we can get benefit out of it, then there is no harm in learning functional.
In this talk we will see, what are the benefits we get when we follow the functional programming paradigm in Java, how it makes the developer life easy. We will discuss the comparison between the imperative code vs functional code with some examples.
We will discuss the transition from imperative coding to functional coding with some code examples. This talk will be good for the people who really want to switch to functional programming and want to know how to use that in Java.
This document provides an overview of new features in Java 8, including lambda expressions, streams, date/time APIs, Nashorn JavaScript engine, and improved string joining capabilities. Key points covered include lambda expressions allowing anonymous functions, streams supporting parallel processing of collections, immutable date/time classes, using Nashorn to execute JavaScript from Java code, and the StringJoiner class and String join method for concatenating strings.
This document discusses using static types with GraphQL applications. It begins with an introduction from James Baxley. It then covers describing data with GraphQL's robust type system, using Apollo Client and React Apollo to manage data loading from a GraphQL server, and bringing the server types into the application. It discusses type systems like Flow and TypeScript that can be used, advantages of static types like reduced bugs, and tools that integrate types like Apollo Codegen, Flow Coverage, and VSCode. It emphasizes that GraphQL is strongly typed and tools like the Relay compiler and Apollo Codegen can generate types for GraphQL queries.
Java 8 introduced parallel streams to allow collections to be processed in parallel using multiple threads. While parallel streams can improve performance by splitting work across CPU cores, they also introduce challenges. Parallel streams use the ForkJoinPool by default, so long-running or blocking tasks could impact other parallel operations. Side effects must also be avoided. Parallel streams may not provide benefits with a single CPU or for I/O-bound work. Developers must consider hardware resources and task dependencies to determine if parallel streams are applicable.
This document discusses using FreeMarker as a template engine with Spring MVC instead of JSP. It provides an overview of FreeMarker, how to configure Spring MVC to use it, and examples of FreeMarker syntax including variables, collections, includes, and macros. Commonly used FreeMarker components are demonstrated along with comparisons to JSP syntax.
Lessons Learned: Scala and its EcosystemPetr Hošek
This presentation is motivated by the continuous growth of Scala language popularity thanks to many new concepts it offers. Therefore, it makes a perfect sense to take a further insight on this language. Beside the language itself, its ecosystem is also very important. That is why I will focus on the Scala ecosystem in this presentation.
Automatic Forecasting using Prophet, Databricks, Delta Lake and MLflowDatabricks
As Atlassian continues to scale to more and more customers, the demand for our legendary support continues to grow. Atlassian needs to maintain balance between the staffing levels needed to service this increasing support ticket volume with the budgetary constraints needed to keep the business healthy – automated ticket volume forecasting is at the centre of this delicate balance
This document provides an introduction and overview of LINQ (Language Integrated Query). It defines LINQ as allowing developers to write SQL queries using a simple language like C# without needing to learn SQL. LINQ provides a unified syntax for querying multiple data sources. The document outlines the structure of a LINQ query, shows how to write one, and lists the stages of executing a query. Advantages include a consistent syntax across data sources and syntax highlighting. Examples demonstrate creating a data context class and querying data from a database and objects.
This document discusses Java 8 parallel streams. It begins with an introduction to lambda expressions and streams in Java 8. It then covers parallel streams and how they can automatically parallelize processing. The document demonstrates parallel stream code examples. It also discusses the ForkJoinPool for parallel execution in Java 7. Finally, it notes some potential issues with parallel streams, such as performance degradation on single-core machines or with tasks involving I/O. It recommends only using parallel streams on multi-core machines without significant I/O.
Apresentado na React Conf Brasil, em São Paulo, 7 de Outubro de 2017 #reactconfbr
Entusiasta da open web e software livre. Software Developer na globo.com e membro da JS foundation. Já trabalhou como desenvolvedor em empresas como Videolog e Petrobras. Em 2015, foi um dos desenvolvedores de software mais notáveis no GitHub, tendo feito contribuições significativas para projetos de código aberto por 500 dias seguidos. Orador em mais de vinte conferências dando palestras sobre tecnologias web como JavaScript / ES6, Web Performance, React e Canvas / WebGL.
http://raphamorim.io
@raphamundi
- Patrocínio: Pipefy, Globo.com, Meteor, Apollo, Taller, Fullcircle, Quanto, Udacity, Cubos, Segware, Entria
- Apoio: Concrete, Rung, LuizaLabs, Movile, Rivendel, GreenMile, STQ, Hi Platform
- Promoção: InfoQ, DevNaEstrada, CodamosClub, JS Ladies, NodeBR, Training Center, BrazilJS, Tableless, GeekHunter
- Afterparty: An English Thing
Used this slide at Tokyo Rubyist Meetup (https://trbmeetup.doorkeeper.jp/events/46318) on June 9 2016.
Introduced Rails 5 new features, especially ActionCable.
Tried live coding to build a new application using ActionCable in the presentation.
This document provides an overview and comparison of the Avro and Parquet data formats. It begins with introductions to Avro and Parquet, describing their key features and uses. The document then covers Avro and Parquet schemas, file structures, and includes code examples. Finally, it discusses considerations for choosing between Avro and Parquet and shares experiences using the two formats.
Predict Repeat Shoppers with H20 and SparkairisData
1. The document discusses predicting repeat shoppers of online stores using gradient boosted machines in H2O and Spark.
2. It describes the repeat shopper problem motivation of targeting repeat purchasers who are more lucrative customers.
3. Feature generation steps are outlined using user and seller data like monthly purchase counts, category interactions, and seller similarity metrics to create a large sparse matrix for modeling.
Getting Spark ready for real-time, operational analyticsairisData
SnappyData provides a unified cluster for operational analytics that can handle OLTP, OLAP and streaming workloads. It integrates an in-memory columnar store with Spark to allow for real-time queries and analytics. SnappyData aims to drastically reduce the cost and complexity of modern big data systems by combining streaming, interactive and batch processing workloads in a single cluster. It offers high performance and scalability for use cases such as market surveillance, real-time recommendations and sensor analytics.
Apache Mahout is an open source machine learning library built in Java. It provides algorithms for recommendation, clustering, and classification. Some key benefits of Mahout include its Apache license, active community support, good documentation, and ability to scale to large datasets using Hadoop. It supports many common machine learning algorithms such as collaborative filtering, k-means clustering, logistic regression, and neural networks. While other options like Weka and R exist, Mahout is preferable for its scalability on big data using Hadoop.
This document introduces Mahout Scala and Spark bindings, which aim to provide an R-like environment for machine learning on Spark. The bindings define algebraic expressions for distributed linear algebra using Spark and provide optimizations. They define data types for scalars, vectors, matrices and distributed row matrices. Features include common linear algebra operations, decompositions, construction/collection functions, HDFS persistence, and optimization strategies. The goal is a high-level semantic environment that can run interactively on Spark.
Distributed Machine Learning with Apache MahoutSuneel Marthi
This document discusses Apache Mahout, an open source machine learning library. It provides examples of using Mahout for tasks like linear regression, dimensionality reduction, and data visualization. Key points covered include loading and manipulating distributed datasets, fitting regression models, evaluating predictions, and visualizing high-dimensional data in 2D and 3D plots.
Apache Mahout is a scalable machine learning library built on Hadoop. It provides algorithms for recommendation engines, clustering, classification and other machine learning tasks. Some key algorithms include user-based and item-based collaborative filtering for recommendations, k-means and fuzzy k-means clustering, logistic regression for classification. Mahout is well suited for large datasets and allows machine learning tasks to be easily parallelized across a Hadoop cluster. It has advantages of being open source, scalable, and built on production-quality libraries.
Tim Spann will present on learning Apache Spark. He is a senior solutions architect who previously worked as a senior field engineer and startup engineer. airis.DATA, where Spann works, specializes in machine learning and graph solutions using Spark, H20, Mahout, and Flink on petabyte datasets. The agenda includes an overview of Spark, an explanation of MapReduce, and hands-on exercises to install Spark, run a MapReduce job locally, and build a project with IntelliJ and SBT.
Why Scala Is Taking Over the Big Data WorldDean Wampler
The document discusses how Scala is becoming increasingly popular for big data applications. It provides context about the evolution of Hadoop and MapReduce as the main computing framework for big data in 2008. It notes that implementing algorithms with just MapReduce steps was difficult, and that the Hadoop API was low-level and tedious to work with. Scala is taking over as it allows for more complex algorithms to be implemented more easily compared to the MapReduce model and Hadoop's API.
Sebastian Schelter – Distributed Machine Learing with the Samsara DSLFlink Forward
The document discusses Samsara, a domain specific language for distributed machine learning. It provides an algebraic expression language for linear algebra operations and optimizes distributed computations. An example of linear regression on a cereals dataset is presented to demonstrate how Samsara can be used to estimate regression coefficients in a distributed fashion. Key steps include loading data as a distributed row matrix, extracting feature and target matrices, computing the normal equations, and solving the linear system to estimate coefficients.
Escaping Flatland: Interactive High-Dimensional Data Analysis in Drug Discove...Spark Summit
This document describes a project at Novartis to use Apache Spark for high-dimensional data analysis from drug screening. Large datasets from various screening technologies were analyzed using Spark pipelines for quality control, normalization, and classification. Visualizations were built using WebGL. The goals were to speed up multi-day batch jobs, create a unified analysis workflow, and build an application for scientists. Future work includes elastic infrastructure, supervised learning of cell phenotypes, and contributing methods to open source.
This document provides an overview of big data processing with Scala and Spark. It introduces big data concepts and defines Spark as an Apache project that provides a general-purpose cluster computing system. The document outlines why Spark is useful, such as its speed, APIs, and tools. It also describes Scala as a functional programming language integrated with Java that is well-suited for big data frameworks like Spark. The remainder covers Spark's ecosystem of modules and concludes with an invitation to try a basic "Hello World" Spark program.
This speaker is a serial language enthusiast who has programmed in many languages for both work and personal projects. For their work at Twitter, they were looking to move away from primarily using Ruby due to performance and cultural issues it presented for building large systems. They settled on adopting Scala at Twitter as it met their personal criteria of being fast, functional, expressive, statically typed, concurrent, and beautiful to work with.
Slides from my talk at the Feb 2011 Seattle Tech Startups meeting. More info here (along with powerpoint slides): http://www.startupmonkeys.com/2011/02/scala-frugal-mechanic/
A Quick Tutorial on Mahout’s Recommendation Engine (v 0.4)Jee Vang, Ph.D.
Mahout is a machine learning API built on Hadoop that includes recommender algorithms. Mahout's recommendation engine transforms input data into predicted recommendations using a series of MapReduce phases. It first creates user preference vectors and item similarity matrices, then performs partial matrix multiplications to calculate item recommendations for each user. The final phase aggregates and filters the recommendations to produce the predicted items for each user.
Introduction to Functional Programming with Scalapramode_ce
The document provides an introduction to functional programming with Scala. It outlines the following topics that will be covered: learning Scala syntax and writing simple programs; important functional programming concepts like closures, higher-order functions, purity, lazy evaluation, currying, tail calls, immutability, and type inference; and understanding the functional programming paradigm through Scala. It also provides some background information on Scala and examples of Scala code demonstrating various concepts.
This document discusses new features in Java 8 including stream API, lambdas, default methods, optional values, date and time API, stamped locks, concurrent adders, improved annotations, new file operations, overflow operations, and the Nashorn JavaScript engine. It provides code examples and explanations of how to use these new features in Java 8.
Learn scala and it's componenents learn itsiddharth30121
The document discusses Scala, a programming language that unifies object-oriented and functional programming. Some key points:
- Scala was created by Martin Odersky and first released in 2003. It runs on the Java Virtual Machine.
- Scala is statically typed and supports type inference, allowing developers to avoid specifying types in most cases.
- Scala code compiles to Java bytecode, allowing it to easily integrate with Java. It also supports functional programming concepts like higher-order functions.
- The document provides examples of using Scala in the REPL (Run Evaluate Print Loop) as well as features like val/var variables, type inference, and multiple assignments.
Nexthink Library - replacing a ruby on rails application with Scala and SprayMatthew Farwell
Applications without tests (legacy applications) present particular problems to developers when changes are required. Unless the change is very small, they require a lot of manual testing. This is even more true when we wish to completely replace such an application. At Nexthink, we replaced a legacy Ruby on Rails application with a web application written using Spray and Slick. There were two parts, a Rest API, accessed from lots of different version of our products, and an administration web interface to allow editing of the data. In replacing this application, we had to ensure that the new implementation returned exactly the same answers to requests as the old application for the same input criteria, without knowing in advance what the requests were. This presentation shows how a combination of technologies was used to achieve this goal, including: spray - for the reactive http container proxy - the new application was used as a proxy to the old application, and the results were compared with the new application (in production) scalacheck - to generate structured queries on the old and new application, for testing gatling - for performance tests This presentation covers both how the replacement was done, to ensure the minimum number of bugs in production, along with our subsequent experience in production.
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Ruby on Rails was presented to the Agile Atlanta group. The presentation provided an overview of Ruby, demonstrated the Rails framework, and described its principles and architecture in 3 sentences or less:
Ruby is an agile language and Ruby on Rails is its popular web application framework that promotes agile practices like convention over configuration and includes everything needed to build database-driven web apps according to the MVC pattern.
Scala is a functional/object-oriented hybrid programming language for the JVM that integrates with existing Java libraries. It aims to remove roadblocks from Java through features like closures, pattern matching, and implicit functions while retaining compatibility. Some key aspects include static typing, concise syntax, native support for XML/JSON, and treating functions as objects.
This document provides an introduction to Scala programming concepts covered in Unit 1, including:
1. Scala is a general-purpose programming language that supports object-oriented, functional, and imperative programming. It has features like type inference, singleton objects, immutability, lazy computation, case classes, pattern matching, and higher-order functions.
2. The document discusses installing Scala, the Scala environment, basic syntax like classes, objects, and methods. It also covers comments, variables, and basic data types in Scala.
3. Examples of a simple "Hello World" program are provided to demonstrate running Scala in both interactive and script modes.
This document provides an overview of functional JavaScript concepts including: type system, primitive values, objects, inheritance, functions, closures, pure functions, higher order functions, composition, currying/partial application, and functional techniques like filter, map and reduce. It recommends resources for learning more about functional programming in JavaScript like libraries, books, and workshops.
This document provides an introduction and overview of the Scala programming language. It begins with a brief history of Scala's creation at EPFL by Martin Odersky in 2001. It then discusses some of the key reasons for using Scala, including its ability to scale with demands through a combination of object-oriented and functional programming concepts. The document outlines some of Scala's main features, such as its static typing, interoperability with Java, and support for modularity. It also provides examples of Scala's data types, operations, control structures, functions, and pattern matching capabilities. Overall, the summary provides a high-level introduction to Scala's origins, design philosophy, and programming paradigms.
Sviluppare applicazioni nell'era dei "Big Data" con Scala e Spark - Mario Car...Codemotion
Scala è un linguaggio di programmazione general purpose multi-paradigma pensato per realizzare applicazioni ad alte prestazioni che girano all'interno della Java Virtual Machine. Spark è il framework "Big Data", basato su Scala, più flessibile e performante disponibile oggi sul mercato. Durante il talk verrà introdotto il linguaggio Scala e verranno mostrate le potenzialità legate al suo utilizzo nell'ambito dello sviluppo di applicazioni web di ultima generazione compresa la possibilità di processamento parallelo di grandi quantità di dati attraverso l'utilizzo del framework Spark.
This document provides an overview of a presentation on Ruby on Rails given to the Agile Atlanta group in May 2005. The presentation covered an introduction to Ruby on Rails, a demonstration of Rails, a description of the Rails framework including its Model-View-Controller architecture, ActiveRecord implementation, and other features like helpers, caching and testing. Examples were provided of how to define models, views, controllers and web services in Rails. The presenter emphasized how Rails promotes agile practices through conventions, its focus on human factors and succinct code.
Scala is a good choice for building Web 2.0 applications because it runs on the JVM like Java and other dynamic languages, has features that make concurrency simple and predictable, and has a large and growing community and ecosystem of tools, libraries, and frameworks. While Scala adds some complexity, its benefits of performance, flexibility, and productivity make it worth learning.
The document discusses why Scala is a good language choice for Web 2.0 applications. It provides a brief history of languages used for Web development and introduces Scala as a functional, object-oriented language that runs on the JVM. The document outlines several features of Scala like actors for concurrency, type inference, and pattern matching that make it suitable for building scalable web applications. It also discusses the Scala community and various tools and libraries available that help develop and test Scala applications.
The document discusses why Scala is a good language choice for Web 2.0 applications. It provides a brief history of languages used for Web development and introduces Scala as a functional, object-oriented language that runs on the JVM. The document outlines several features of Scala like actors for concurrency, type inference, and pattern matching that make it suitable for building scalable web applications. It also discusses the Scala community and various tools and libraries available that help develop and test Scala applications.
Learn about SPARK tool and it's componemtssiddharth30121
This document discusses Apache Spark, a fast and general engine for large-scale data processing. It provides three key advantages over MapReduce: in-memory processing which is 10-100x faster, support for interactive queries, and integration of streaming, SQL, machine learning, and graph processing. The core abstraction in Spark is the Resilient Distributed Dataset (RDD), which allows data to be partitioned across clusters and cached in memory for faster shared access compared to MapReduce's disk-based approach.
The presentation I had for Stockholm Knowledge Network. For demo code please refer to https://github.com/anderseriksson/yesscalacan
Discussion on ECMAScript 6 to TypeScript selective features before jump into Angular and brushing your JS programming concepts. Whats new with ECMAScript 6, why ES6 superset - TypeScript adopted by the Angular community and warming up JavaScript philosophy.
Fluid mechanics is the branch of physics concerned with the mechanics of fluids (liquids, gases, and plasmas) and the forces on them. Originally applied to water (hydromechanics), it found applications in a wide range of disciplines, including mechanical, aerospace, civil, chemical, and biomedical engineering, as well as geophysics, oceanography, meteorology, astrophysics, and biology.
It can be divided into fluid statics, the study of various fluids at rest, and fluid dynamics.
Fluid statics, also known as hydrostatics, is the study of fluids at rest, specifically when there's no relative motion between fluid particles. It focuses on the conditions under which fluids are in stable equilibrium and doesn't involve fluid motion.
Fluid kinematics is the branch of fluid mechanics that focuses on describing and analyzing the motion of fluids, such as liquids and gases, without considering the forces that cause the motion. It deals with the geometrical and temporal aspects of fluid flow, including velocity and acceleration. Fluid dynamics, on the other hand, considers the forces acting on the fluid.
Fluid dynamics is the study of the effect of forces on fluid motion. It is a branch of continuum mechanics, a subject which models matter without using the information that it is made out of atoms; that is, it models matter from a macroscopic viewpoint rather than from microscopic.
Fluid mechanics, especially fluid dynamics, is an active field of research, typically mathematically complex. Many problems are partly or wholly unsolved and are best addressed by numerical methods, typically using computers. A modern discipline, called computational fluid dynamics (CFD), is devoted to this approach. Particle image velocimetry, an experimental method for visualizing and analyzing fluid flow, also takes advantage of the highly visual nature of fluid flow.
Fundamentally, every fluid mechanical system is assumed to obey the basic laws :
Conservation of mass
Conservation of energy
Conservation of momentum
The continuum assumption
For example, the assumption that mass is conserved means that for any fixed control volume (for example, a spherical volume)—enclosed by a control surface—the rate of change of the mass contained in that volume is equal to the rate at which mass is passing through the surface from outside to inside, minus the rate at which mass is passing from inside to outside. This can be expressed as an equation in integral form over the control volume.
The continuum assumption is an idealization of continuum mechanics under which fluids can be treated as continuous, even though, on a microscopic scale, they are composed of molecules. Under the continuum assumption, macroscopic (observed/measurable) properties such as density, pressure, temperature, and bulk velocity are taken to be well-defined at "infinitesimal" volume elements—small in comparison to the characteristic length scale of the system, but large in comparison to molecular length scale
The Fluke 925 is a vane anemometer, a handheld device designed to measure wind speed, air flow (volume), and temperature. It features a separate sensor and display unit, allowing greater flexibility and ease of use in tight or hard-to-reach spaces. The Fluke 925 is particularly suitable for HVAC (heating, ventilation, and air conditioning) maintenance in both residential and commercial buildings, offering a durable and cost-effective solution for routine airflow diagnostics.
ELectronics Boards & Product Testing_Shiju.pdfShiju Jacob
This presentation provides a high level insight about DFT analysis and test coverage calculation, finalizing test strategy, and types of tests at different levels of the product.
Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking a volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.
its all about Artificial Intelligence(Ai) and Machine Learning and not on advanced level you can study before the exam or can check for some information on Ai for project
This paper proposes a shoulder inverse kinematics (IK) technique. Shoulder complex is comprised of the sternum, clavicle, ribs, scapula, humerus, and four joints.
π0.5: a Vision-Language-Action Model with Open-World GeneralizationNABLAS株式会社
今回の資料「Transfusion / π0 / π0.5」は、画像・言語・アクションを統合するロボット基盤モデルについて紹介しています。
拡散×自己回帰を融合したTransformerをベースに、π0.5ではオープンワールドでの推論・計画も可能に。
This presentation introduces robot foundation models that integrate vision, language, and action.
Built on a Transformer combining diffusion and autoregression, π0.5 enables reasoning and planning in open-world settings.
"Feed Water Heaters in Thermal Power Plants: Types, Working, and Efficiency G...Infopitaara
A feed water heater is a device used in power plants to preheat water before it enters the boiler. It plays a critical role in improving the overall efficiency of the power generation process, especially in thermal power plants.
🔧 Function of a Feed Water Heater:
It uses steam extracted from the turbine to preheat the feed water.
This reduces the fuel required to convert water into steam in the boiler.
It supports Regenerative Rankine Cycle, increasing plant efficiency.
🔍 Types of Feed Water Heaters:
Open Feed Water Heater (Direct Contact)
Steam and water come into direct contact.
Mixing occurs, and heat is transferred directly.
Common in low-pressure stages.
Closed Feed Water Heater (Surface Type)
Steam and water are separated by tubes.
Heat is transferred through tube walls.
Common in high-pressure systems.
⚙️ Advantages:
Improves thermal efficiency.
Reduces fuel consumption.
Lowers thermal stress on boiler components.
Minimizes corrosion by removing dissolved gases.
2. Agenda
Functional Programming Vs Imperative Programming
Why Functional Programming?
Scala Programming Fundamentals
REPL
Scala Runnable Program
Scala classes and objects
Tuples
Scala Collections Vs Java Collections
Apache Spark Scala API Intro
3. Imperative Versus Functional Programming
In imperative approach, a developer writes code that describes in exacting detail the
steps that the computer must take to accomplish the goal.
Functional approach involves composing the problem as a set of functions to be
executed. “Pure Functions” makes the order of execution irrelevant - since no side-
effect can change the value of an expression, it can be evaluated at any time.
Characteristic Imperative approach Functional approach
Programmer focus
How to perform tasks (algorithms) and how
to track changes in state.
What information is desired and what
transformations are required.
State changes Important. Non-existent.
Order of execution Important. Low importance.
Primary flow control
Loops, conditionals, and function (method)
calls.
Function calls, including recursion.
Primary manipulation unit Instances of structures or classes. Functions as first-class objects and data collections.
4. Why Functional Programming?
Describe what you want, rather than how to get it.
Instead of creating a for-loop with an iterator variable and marching through an collection
doing something to each cell, pass a function and it will be executed for all the elements in
the collection which can be optimized
More concise and expressive code
Functional programming moves more basic programming ideas into the compiler.
Syntactic Sugar – case classes, pattern matching
Type Inference
List comprehension
conversion to primitives, tail recursion
Implicit conversions
Concurrency
No Shared mutable state – so no locking required
Pure functional languages support lazy evaluation.
Can create infinitely large collections
Value will be calculated only when required and function call be substituted in place of
functional value (Referential integrity)
5. Scala Programming Fundamentals
Scala brings together concepts from both Functional Programming and
Objected Oriented Programming.
Provides a more comprehensive API, a superior compiler but stills runs
on the JVM.
Demos:
REPL
Runnable Program in Scala
Scala classes and Objects
Tuples