Java Tools and Technologies Landscape for 2014 (image gallery)

May 21, 2014507 likes259,390 views

**See the full version here: http://zeroturnaround.com/rebellabs/java-tools-and-technologies-landscape-for-2014/ -- In a recent survey, RebelLabs asked 2164 Java professionals about their technology usage in 14 tool categories, and found some interesting trends…

1All rights reserved. 2014 © ZeroTurnaround OÜ
A GLOBAL SURVEY OF 2164 JAVA PROFESSIONALS
JAVA TOOLS AND
TECHNOLOGIES
LANDSCAPE FOR 2014
Oh yeah, and each survey
donated to charity!

2All rights reserved. 2014 © ZeroTurnaround OÜ
Sample population of 2164 Java professionals, sample error 2.1%

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Sample population of 2164 Java professionals, sample error 2.1%

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24All rights reserved. 2014 © ZeroTurnaround OÜ
We would like to thank the subject matter experts (SMEs) quoted
in this report, the authors, editors & reviewers from ZeroTurnaround,
our sponsor that made it possible to create this report, and finally our readers
and the 2164 engineers+ out there that spent 5 minutes to help sick kids
in hospitals have a bit of imaginative gameplay. We hope our next survey
gets even more participants and goes to another worthy cause!
Stay tuned for more from RebelLabs, and tell us what you think on
Twitter @RebelLabs.
Check out all our reports here: http://rebellabs.org
THANKS FOR READING!
NO DEVELOPERS WERE HARMED IN THE MAKING OF THIS REPORT : )

25All rights reserved. 2014 © ZeroTurnaround OÜ

Este documento ofrece una introducción a las bases de datos NoSQL y Apache Cassandra. Explica que las bases de datos NoSQL difieren de las bases de datos relacionales tradicionales en que no requieren esquemas fijos, evitan operaciones JOIN y escalan horizontalmente. Luego describe las características clave de Cassandra, incluyendo su modelo de datos orientado a columnas y su capacidad de alta disponibilidad y tolerancia a fallos a través de la replicación distribuida de datos. Finalmente, contrasta el modelo de datos y consultas de Cassandra con las bases de datos rel

Comparación entre Oracle y Mysqlstefakoka

Este documento compara las bases de datos Oracle y MySQL en términos de sus características, licenciamiento, interfaces soportadas, y soporte técnico. MySQL es de código abierto y licencia GPL, mientras que Oracle es propietario. Ambos soportan muchas interfaces de programación, aunque Oracle tiene más características avanzadas. El soporte de Oracle incluye asistencia las 24 horas, mientras que MySQL ofrece diferentes niveles de soporte pago.

Data Warehouse Modernization: Accelerating Time-To-Action MapR Technologies

Data warehouses have been the standard tool for analyzing data created by business operations. In recent years, increasing data volumes, new types of data formats, and emerging analytics technologies such as machine learning have given rise to modern data lakes. Connecting application databases, data warehouses, and data lakes using real-time data pipelines can significantly improve the time to action for business decisions. More: http://info.mapr.com/WB_MapR-StreamSets-Data-Warehouse-Modernization_Global_DG_17.08.16_RegistrationPage.html

Cassandra NoSQL TutorialMichelle Darling

I don't think it's hyperbole when I say that Facebook, Instagram, Twitter & Netflix now define the dimensions of our social & entertainment universe. But what kind of technology engines purr under the hoods of these social media machines? Here is a tech student's perspective on making the paradigm shift to "Big Data" using innovative models: alphabet blocks, nesting dolls, & LEGOs! Get info on: - What is Cassandra (C*)? - Installing C* Community Version on Amazon Web Services EC2 - Data Modelling & Database Design in C* using CQL3 - Industry Use Cases

Data lake benefitsRicky Barron

Building Real-Time Data Pipeline for Diabetes Medication Recommender System U...Databricks

American Diabetic association states that 29.1 million Americans and 300+ million people all over the world have diabetes. Diabetic medication management is always challenging. Based on doctor’s prescription, patients take insulin dosage one hour before breakfast, lunch or dinner. But the real world scenario insulin intake can be changed based on the blood glucose level, calorie intake on a specific day, etc. This talk explains how a real-time Big Data pipeline recommendation engine can be used to suggest insulin intake for diabetic patients in near real time. Based on calorie intake and blood glucose level from patients as well as generated dataset, insulin dosage can be recommended which will help patients to avoid over/under dosage. Designing medication recommender system is a need for the Healthcare industry. There is a growing trend for the applications to help doctors by recommending medication based on patient’s historic data. This also helps facilitate a doctor friendly and hospital free atmosphere for all users all over the world. This talk would delve into a Diabetes medication recommender system using Databricks and Spark. Databricks supports HIPAA compliant deployment for processing PHI data. This talk would cover building a secure pipeline with encrypted data and the end-to-end recommendation system using Structured streaming and IoT data flowing from sensor.

Accelerate and modernize your data pipelinesPaul Van Siclen

In this session you will learn how Qlik’s Data Integration platform (formerly Attunity) reduces time to market and time to insights for modern data architectures through real-time automated pipelines for data warehouse and data lake initiatives. Hear how pipeline automation has impacted large financial services organizations ability to rapidly deliver value and see how to build an automated near real-time pipeline to efficiently load and transform data into a Snowflake data warehouse on AWS in under 10 minutes.

Active DirectorySmall World Group L.L.C

Active Directory (AD) is Microsoft's directory service that provides a centralized hierarchical view and management of network resources. As an index of files on a computer, AD allows users to be granted permissions to access resources. It delegates authority through a centralized administration mechanism that automates network management and enables different systems to work together. Basic AD networks consist of forests, domains, organizational units, sites, and domains which are collections of computers that share policies, authentication, and a database maintained by domain controllers. Everything tracked in AD is considered an object, while stale references are known as phantoms. LDAP (Lightweight Directory Access Protocol) enables locating resources and should be mastered through LDAP search queries.

Azure Cosmos DBMohamed Tawfik

Microsoft Azure Cosmos DB is a multi-model database that supports document, key-value, wide-column and graph data models. It provides high throughput, low latency and global distribution across multiple regions. Cosmos DB supports multiple APIs including SQL, MongoDB, Cassandra and Gremlin to allow developers to use their preferred API based on their application needs and skills. It also provides automatic scaling of throughput and storage across all data partitions.

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Microsoft Azure Networking BasicsSai Kishore Naidu

An Azure virtual network (VNet) provides connectivity and security for virtual machines and allows access to the public internet and other VMs. Network security groups contain rules that allow or deny network traffic, and Azure load balancers distribute incoming internet traffic across VMs. Availability sets distribute VMs across update and fault domains for redundancy and high availability. Virtual network gateways connect Azure VNets and on-premises networks, while Traffic Manager controls traffic distribution across endpoints in different datacenters.

Azure CosmosDbMarco Parenzan

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Oracle data integrator projectAmit Sharma

This document provides instructions for creating an Oracle Data Integrator (ODI) project and interface to export data from one flat file to another flat file. It outlines the steps to create a new physical schema for the flat file model, a new ODI model for the flat file source, new ODI source and target datastores, and a new ODI interface to perform the flat file to flat file transformation. The interface can then be executed using the ODI Operator to verify the data export.

Migrating Oracle database to CassandraUmair Mansoob

This document discusses migrating Oracle databases to Cassandra. Cassandra offers lower costs, supports more data types, and can scale to handle large volumes of data across multiple data centers. It also allows for more flexible data modeling and built-in compression. The document compares Cassandra and Oracle on features, provides examples of companies using Cassandra, and outlines best practices for data modeling in Cassandra. It also discusses strategies for migrating data from Oracle to Cassandra including using loaders, Sqoop, and Spark.

Características de MySQLAlejandro Librado Acosta Cons

Este documento describe MySQL, un sistema de gestión de base de datos. Explica que MySQL permite a los usuarios agregar, acceder y procesar datos estructurados almacenados en una computadora. También describe algunas características clave como el soporte para múltiples procesadores, tipos de datos, lenguajes de programación, portabilidad y seguridad. Finalmente, introduce dos entornos de trabajo comunes para MySQL: Appserv y PhpMyAdmin.

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This document provides an overview of upgrading, migrating, and consolidating to Oracle Database 12c and 11gR2. It discusses new features in Oracle 12c such as automatic data optimization, extreme availability enhancements like Active Data Guard Far Sync, and security features. The document also covers preparing for an upgrade, migration cases, fallback strategies, performance management, and multitenant architecture concepts.

Azure data bricks by Eugene PolonichkoAlex Tumanoff

This document provides an overview of Azure Databricks, including: - Azure Databricks is an Apache Spark-based analytics platform optimized for Microsoft Azure cloud services. It includes Spark SQL, streaming, machine learning libraries, and integrates fully with Azure services. - Clusters in Azure Databricks provide a unified platform for various analytics use cases. The workspace stores notebooks, libraries, dashboards, and folders. Notebooks provide a code environment with visualizations. Jobs and alerts can run and notify on notebooks. - The Databricks File System (DBFS) stores files in Azure Blob storage in a distributed file system accessible from notebooks. Business intelligence tools can connect to Databricks clusters via JDBC

Organización aleatoria o indirectaJuan Antonio Membrives Polo

Este documento describe las organizaciones aleatoria e indirecta de archivos y los árboles B y B+. Explica que un archivo es un conjunto de bytes organizados en registros y campos, y que la organización aleatoria usa funciones hash para asignar claves alfanuméricas a direcciones físicas. También describe ventajas e inconvenientes de esta organización, así como algoritmos para inserción, lectura y borrado. Finalmente, explica que los árboles B y B+ son índices multinivel dinámicos usados para ficheros de datos, y describe sus

Sistemas Gestores de Bases de Datosalexmerono

Este documento describe los sistemas gestores de bases de datos. Explica que una base de datos es un conjunto de datos relacionados organizados y estructurados con información sobre un tema. Los sistemas gestores de bases de datos proporcionan servicios como la creación y definición de la base de datos, la manipulación y el acceso controlado a los datos, y el mantenimiento de la integridad y consistencia de los datos. También describe los componentes clave de los sistemas gestores de bases de datos como los lenguajes, el diccionario de datos, la seguridad

Bases de Datos 1.pptssuser6c30de1

El documento presenta una introducción a las bases de datos, definiendo este concepto como un conjunto de datos relacionados entre sí y con significado implícito. Explica algunos conceptos básicos como entidades, claves primarias, relaciones y restricciones de integridad referencial. Además, introduce el concepto de Sistema de Gestión de Base de Datos y su importancia en el diseño de sistemas de información.

OMG Data-Distribution Service (DDS) Tutorial - 2009Gerardo Pardo-Castellote

The document discusses middleware technologies and provides an overview of the Data Distribution Service (DDS) model and its applicability. It begins by explaining how middleware abstracts common interaction patterns and network middleware in particular. It then contrasts different communication models used in middleware like point-to-point, client-server, publish-subscribe messaging, and replicated data. The document dives deeper into comparing request-response with pub-sub/messaging/data distribution and explaining the differences between message queues and publish-subscribe. Finally, it outlines the DDS service model and how it provides a global data space that is accessible to all interested applications through subscriptions that are decoupled from publishers.

1. Modelo de DatosMSc Aldo Valdez Alvarado

Que Es Un Data Warehouseguest10616d

El documento proporciona una introducción al concepto de data warehouse. Explica que un data warehouse es un almacén de datos de fácil acceso alimentado por múltiples fuentes de datos transformadas para permitir nuevas consultas, análisis y la toma de decisiones. También describe los procesos básicos de extracción, transformación y carga de datos, así como los elementos clave de un sistema de data warehouse como las fuentes de datos, el área de tráfico de datos y el servidor de presentación.

ELOGIM: Administración de servicios e indicadores de bibliotecas como apoyo a...Sistema de Servicios de Información y Bibliotecas SISIB

ELOGIM es un sistema web que administra estadísticas de uso de recursos electrónicos en bibliotecas para apoyar la toma de decisiones. Provee indicadores como rankings de recursos y usuarios más populares, perfiles de uso por facultad y programa, y efectividad de recursos. Esto permite caracterizar usuarios, definir necesidades de conocimiento, y gestionar recursos de forma autónoma y eficiente.

How queries work with shardingMongoDB

The document discusses how queries work in sharded MongoDB environments. It explains that MongoDB collections are partitioned into chunks based on a shard key, and each chunk is assigned to a particular shard. When a query is executed, the mongos process routes it to the correct shard(s) based on the shard key range in the query. Queries involving only the shard key are efficient, targeting specific shards. Queries on non-shard keys require scattering and gathering across all shards, but secondary indexes can help efficiency on each shard.

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Top Reasons Why Java Rocks (report preview) - http:0t.ee/java-rocksZeroTurnaround

In this report we take a look at the top 10 reasons Java is the choice for so many development teams, all around the world, and why you should consider it for your next project. One of the main qualities Java possesses since the early design days is its simplicity. In that spirit we laid out in plain simple terms why Java outperforms other languages when it comes to popularity, performance, its amazing ecosystem and community. For the less technical, this report explains the value Java brings to your projects. For fellow developers, we included some other projects in the Java ecosystem that you might want to look at. Check them out, have a play with them. It will make you a better developer. Continue to the full report on RebelLabs: http:0t.ee/java-rocks

Top Java IDE keyboard shortcuts for Eclipse, IntelliJIDEA, NetBeans (report p...ZeroTurnaround

This document summarizes and compares keyboard shortcuts across three popular Java IDEs: Eclipse, IntelliJ IDEA, and NetBeans. It divides common actions into eight categories such as search, file navigation, and code completion. While the default shortcuts differ between IDEs, they can all be customized. The document aims to help developers learn shortcuts and features of their IDE or potential alternatives.

Getting Started with IntelliJ IDEA as an Eclipse UserZeroTurnaround

**Note: This is a sneak preview of the full report, which you can get on RebelLabs: http://zeroturnaround.com/rebellabs/getting-started-with-intellij-idea-as-an-eclipse-user/ --- My name is Anton, and I’m an IntelliJ IDEA addict. Whew, it feels good to say it out loud. The choice of IDE for developers is one of the most contentious debates in the software game. But why? After all, aren’t all IDEs more or less the same? Perhaps you are Eclipse users who are interested in trying out IntelliJ IDEA, or perhaps considering the migration. Moving from Eclipse to IDEA can be quite overwhelming. My main purpose in writing this report is to show Eclipse users, specifically, how to get started using IDEA faster and with less headaches. For the full report, check it out on RebelLabs: http://zeroturnaround.com/rebellabs/getting-started-with-intellij-idea-as-an-eclipse-user/

DevOps vs Traditional IT Ops (DevOps Days ignite talk by Oliver White)ZeroTurnaround

Lazy Coder's Visual Guide to RebelLabs' Developer Productivity Report 2013ZeroTurnaround

For a long time, developers have known that the tools we use and the methodologies we practice have an effect on the quality of our software and our ability to delivery releases predictably. However, this is often a gut feeling based on anecdotal evidence with no numbers to support this theory. Wouldn't be cool to see actual numbers on how doing Code Quality analysis, Pairing Up and using tools for VCS and Continuous Integration significantly improve our ability to delivery quality software? Then look no further. RebelLabs' Developer Productivity Report 2013 is here: http://zeroturnaround.com/rebellabs/developer-productivity-report-2013-how-engineering-tools-practices-impact-software-quality-delivery/

The State of Managed Runtimes 2013, by Attila SzegediZeroTurnaround

There’s JVM, and that’s it, right? Well, not exactly. Even within JVM, there’s an increasing support for running all kinds of non-Java languages: we have invokedynamic, but it’s being improved, and new layers of functionality are emerging on top of it, making JVM a better home for all kinds of programming languages. There’s life outside of JVM too. JavaScript seems to be a new assembler-lever compilation target even for C programs (I’ll show some amusing examples of what exactly you can run these days in a browser) , and there are some independent efforts at managed runtimes in various stages of completion that seem promising – PyPy, Topaz, Rubinius, Parrot VM (it’s alive again!). This talk is admittedly a language-runtime-enthusiast’s walk-through the things he finds interesting happening this year. Recorded at GeekOut 2013.

Language Design Tradeoffs - Kotlin and Beyond, by Andrey BreslavZeroTurnaround

The document discusses some of the trade-offs that language designers consider when developing a new programming language, such as elegance vs convenience, simplicity vs features, and compatibility vs innovation. It provides Kotlin as an example of a language that aims to strike a good balance across these trade-offs by being safe, compatible with existing libraries and tools, and addressing issues like null safety at compile time rather than runtime.

Runtime Innovation - Nextgen Ninja Hacking of the JVM, by Ryan SciampaconeZeroTurnaround

This document discusses innovations for improving multitenancy in the Java Virtual Machine (JVM). It introduces the concept of "packed objects" which allow multiple objects to be stored contiguously in memory without object headers, improving locality and reducing overhead. This approach provides seamless access to data from both Java and native code. The document also discusses challenges of separating static state for multitenant applications and how the JVM could address this through isolated treatment of static fields per tenant.

Easy Scaling with Open Source Data Structures, by Talip OzturkZeroTurnaround

This document discusses Hazelcast, an open source in-memory data grid for distributed caching and processing. It provides distributed map, queue, and other data structures. Hazelcast partitions and replicates data across nodes for high availability and scalability. It can dynamically add or remove nodes and migrate data between nodes. The document provides code examples of using Hazelcast's distributed maps, queues, locks, and executor service. It also discusses querying, persistence, and differences from other distributed caching solutions.

Blast your app with Gatling! by Stephane LandelleZeroTurnaround

This document discusses load testing tools and introduces Gatling as an easy to use and high performance load testing tool. It notes issues with other tools like JMeter having poor performance with many threads and blocking I/O. Gatling uses an asynchronous actor model with non-blocking I/O to reach new performance limits. It has a Scala DSL for defining tests and supports features like CSV and JDBC feeders, error handling, and reporting plugins. A demo of Gatling is provided.

JVM JIT compilation overview by Vladimir IvanovZeroTurnaround

The document provides an overview of JVM JIT-compilers, including: - JIT-compilers in the HotSpot JVM dynamically compile bytecode to native machine code during program execution for improved performance compared to interpretation alone. - JIT-compilers use profiling information gathered during execution to perform aggressive optimizations like inlining and devirtualization. - The monitoring and debugging of JIT-compilers in the HotSpot JVM can be done using options like -XX:+PrintCompilation, -XX:+PrintInlining, and -XX:+PrintAssembly.

How To Do Kick-Ass Software Development, by Sven PetersZeroTurnaround

The document discusses how to do kick-ass software development through agile practices like using Java, improving as a team, collaborating well, automating tasks, and building a kick-ass culture. It emphasizes delivering high quality software quickly through a co-located team with simple workflows, chat for communication, continuous integration, handling flaky tests, and deploying with a single button press. The overall message is that focusing on developer happiness, customer satisfaction, and continual improvement will result in kick-ass software development.

Level Up Your Git and GitHub Experience by Jordan McCullough and Brent BeerZeroTurnaround

The document summarizes tips and techniques for maximizing use of GitHub, including linking to issues by number in text or commits, auto-updating issues with commit references, resolving auto-merge conflicts either on GitHub or via the command line, keeping forked repos up to date, retrieving pull requests and prototype branches via the command line, and using rebase interactively and cherry-pick to integrate selected commits or work from other branches. The document also provides shortcuts for searching, blaming, and cleaning up local and remote branches.

AST Transformations: Groovy’s best kept secret by Andres AlmirayZeroTurnaround

The document discusses AST (Abstract Syntax Tree) transformations in Groovy, which allow modifying code structure at compile time. It covers the standard AST transformations included in Groovy, how to build local and global AST transformations, and additional tools for working with ASTs. The speaker is an experienced Java and Groovy developer who is a Groovy committer and project lead for the Griffon framework.

Tap into the power of slaves with Jenkins by Kohsuke KawaguchiZeroTurnaround

Language Design Tradeoffs (Kotlin and Beyond) by Andrey BreslavZeroTurnaround

Spring 4 on Java 8 by Juergen HoellerZeroTurnaround

This document discusses Spring Framework 4.0 and its support for Java 8 features. Spring 4.0 will include first-class support for Java 8 language features like lambda expressions and the new date/time API. It will also support upcoming Java EE 7 specifications. Some initial challenges in supporting Java 8 included differences in bytecode versions and hash algorithm changes. The document provides examples of using Java 8 lambda expressions with Spring's JdbcTemplate. It also discusses the state of Java 8 and tool support as Spring 4.0 development progresses.

On Inherent Complexity of Computation, by Attila SzegediZeroTurnaround

The system you just recently deployed is likely an application processing some data, likely relying on some configuration, maybe using some plugins, certainly relying on some libraries, using services of an operating system running on some physical hardware. The previous sentence names 7 categories into which we compartmentalise various parts of a computation process that’s in the end going on in a physical world. Where do you draw the line of functionality between categories? From what vantage points do these distinctions become blurry? Finally, how does it all interact with the actual physical world in which the computation takes place? (What is the necessary physical minimum required to perform a computation, anyway?) Let’s make a journey from your AOP-assembled, plugin-injected, YAML-configured, JIT compiled, Hotspot-executed, Linux-on-x86 hosted Java application server talking JSON-over-HTTP-over-TCP-over-IP-over-Ethernet all the way down to electrons. And then back. Recorded at GeekOut 2013.