Javantura v3 - Develop the right way with S-CASE – Marin Orlić

•Download as PPTX, PDF•

0 likes•1,347 views

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

The document discusses S-CASE, a software development tool that enables modeling requirements, creating prototypes, and generating RESTful code. S-CASE allows developing services from requirements in 3 to 5 steps, and can generate over 2,000 lines of Java code for a review service in 15 minutes with no bugs. The document outlines features of S-CASE including RESTful APIs, authentication, searching, code generation, and integration with external services. It also describes different versions of S-CASE for single developers, teams, and custom installations.

Technology

Develop the right way with S-CASE
Marin Orlić
marin.orlic@ericsson.com
Ericsson Nikola Tesla, Zagreb, Croatia

Background
Ericsson Nikola Tesla
Java community ~200 ppl + students
Wide range of projects and interests
Zagreb, 20 February, 2016Javantura v3 2
EU FP7 project
Final (3rd) year

Scaffolding
Scalable
Software services
Zagreb, 20 February, 2016Javantura v3 3

Prototyping
Scalable
Software services
Zagreb, 20 February, 2016Javantura v3 4

Prototyping
Cloud
Software services
Zagreb, 20 February, 2016Javantura v3 5

Prototyping
Cloud
Web services
Zagreb, 20 February, 2016Javantura v3 6

Software development
Requirements
Specifications
Design
Prototyping
Zagreb, 20 February, 2016Javantura v3 7
Concept

Software development
Requirements
Specifications
Design
Prototyping
Zagreb, 20 February, 2016Javantura v3 8
Existing
artefacts
Concept

Software development with S-CASE
Requirements
Prototyping
Zagreb, 20 February, 2016Javantura v3 9
Existing
artefacts
Concept

Features
• RESTful
– 3rd level Richardson Maturity Model
RESTful APIs
– Full Hypermedia As The Engine of
Application State (HATEOAS)
• Authentication
– Restrict access to selected resources
• Search
– Search functionality (Lucene) mapped
to resources of your choice
• Deploy ready
– Maven project ready to deploy to a
container of your choice
• Code Generation
– Automated code generation from multi-
modal requirements (English text, UML
diagrams, storyboards)
• External web services
– Call 3rd party REST web services
– Create mashups of web services
• Database
– Generate the schema and database
• Authorization
– Enable attribute based access control
(ABAC) – coming soon
Zagreb, 20 February, 2016Javantura v3 11

Working with S-CASE
• Create requirements
• Create service mashups
• Generate code
• Deploy WAR
• RESTReviews
– 2100 LoC in Java
– 15 mins from scratch
– 0 bugs
Zagreb, 20 February, 2016Javantura v3 12

1
Zagreb, 20 February, 2016Javantura v3 13
INSTALL

2
Zagreb, 20 February, 2016Javantura v3 14
CREATEPROJECT

3 DEFINE REQUIREMENTS
Zagreb, 20 February, 2016Javantura v3 15

3 DEFINE WORKFLOWS
Zagreb, 20 February, 2016Javantura v3 16

3 UML
Zagreb, 20 February, 2016Javantura v3 17

4 (AUTO)ANNOTATE REQUIREMENTS
Zagreb, 20 February, 2016Javantura v3 18

5 GENERATE
CODE
- REST resources
- Local DB
- (Hibernate)
- Search in DB
(Lucene)
- Call external
web services
- Authentication
- Authorization
(Basic, ABAC)
Zagreb, 20 February, 2016Javantura v3 19

5 CREATE MASHUPS
Zagreb, 20 February, 2016Javantura v3 20

Zagreb, 20 February, 2016Javantura v3 21

6 GENERATE MASHUP
SERVICE
• Resulting service can be used in
prototype as external service
Zagreb, 20 February, 2016Javantura v3 22

7 DEPLOY (MAVEN)
8 USE THE SERVICE
[INFO] Scanning for projects...
[INFO]
[INFO] Using the builder
org.apache.maven.lifecycle.internal.builder.singleth
readed.SingleThreadedBuilder with a thread count of
1
[INFO]
[INFO] ---------------------------------------------
---------------------------
[INFO] Building restreviews 1.0-SNAPSHOT
[INFO] ---------------------------------------------
---------------------------
[INFO]
[INFO] --- maven-resources-plugin:2.6:resources
(default-resources) @ restreviews ---
[WARNING] Using platform encoding (Cp1250 actually)
to copy filtered resources, i.e. build is platform
dependent!
[INFO] skip non existing resourceDirectory
C:WorkspaceTestWorkspaceMDEGeneratedCoderestrevi
ewssrcmainresources
[INFO]
[INFO] --- maven-compiler-plugin:2.5.1:compile
(default-compile) @ restreviews ---
[INFO] Nothing to compile - all classes are up to
date
[INFO]
[INFO] --- maven-resources-plugin:2.6:testResources
(default-testResources) @ restreviews ---
Zagreb, 20 February, 2016Javantura v3 23

There’s more...
• Analyze natural language (NLP)
• Find open source projects
– Using existing indices (MARKOS, Agora)
• Index and query (in English) over your projects
• Publish your own services
– YouREST service catalog, SAT tool, automatic
• Perceived reliability, maintainability & code metrics of the service
– Exceeds NASA standards
• Expand the platform
– 2D MDE – expandable code generation
Zagreb, 20 February, 2016Javantura v3 24

Flavours – ECLIPSE SOLO
ECLIPSE
SOLO
Single
developer
Smaller
projects
Simple
services
Zagreb, 20 February, 2016Javantura v3 25

Flavours – ECLIPSE PUBLIC
ECLIPSE
PUBLIC
Developer
teams
Quick
prototype
Large
projects
Reuse and
mashup
Zagreb, 20 February, 2016Javantura v3 26

Flavours – ECLIPSE SOLO
ECLIPSE
PRIVATE
Additional
security Protect IP
Customize
S-CASE
Zagreb, 20 February, 2016Javantura v3 27

Getting started
Javantura v3 Zagreb, 20 February, 2016 29
@scasefp7
marin.orlic@erisson.com
http://s-case.github.io
http://www.scasefp7.eu
S-Case
S-Case FP7
s-case

Zagreb, 20 February, 2016Javantura v3 30

This document provides an overview and demonstration of using the ELK stack (Elasticsearch, Logstash, Kibana) for log aggregation and visualization. It discusses the purpose and functionality of each component, and provides a real-world use case example where Logstash is used to collect and parse logs from multiple sources, Elasticsearch is used for storage and analysis, and Kibana is used to visualize the logs. Specific configurations for Logstash input, filtering, and output to Elasticsearch and Redis are also shown.

Javantura v3 - Logs – the missing gold mine – Franjo Žilić

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document discusses using ELK (Elasticsearch, Logstash, Kibana) to gain insights from logs. It describes the components of ELK - Elasticsearch as the database, Kibana as the UI, and Logstash to parse logs. Logstash can use GROK patterns to parse logs into structured data for analysis in Kibana. The document provides examples of using ELK to track web traffic, user activity, and API responses for benefits like reducing costs and monitoring performance. While custom solutions can be built, ELK is beneficial as it requires no software costs and allows full control over collected log data.

ELK Elasticsearch Logstash and Kibana Stack for Log Management

El Mahdi Benzekri

SpringPeople - Introduction to Cloud Computing

SpringPeople

Cloud computing is no longer a fad that is going around. It is for real and is perhaps the most talked about subject. Various players in the cloud eco-system have provided a definition that is closely aligned to their sweet spot –let it be infrastructure, platforms or applications. This presentation will provide an exposure of a variety of cloud computing techniques, architecture, technology options to the participants and in general will familiarize cloud fundamentals in a holistic manner spanning all dimensions such as cost, operations, technology etc

Real Time Data Processing With Spark Streaming, Node.js and Redis with Visual...

Brandon O'Brien

Contact: https://www.linkedin.com/in/brandonjobrien @hakczar Code examples available at https://github.com/br4nd0n/spark-streaming and https://github.com/br4nd0n/spark-viz A demo and explanation of building a streaming application using Spark Streaming, Node.js and Redis with a real time visualization. Includes discussion of internals of Spark and Spark streaming including RDD partitioning and code and data distribution and cluster resource allocation.

Running High-Speed Serverless with nuclio

iguazio

This document provides an overview and introduction to Nuclio, an open source serverless platform. Some key points: - Nuclio is described as a new real-time serverless platform that is comprehensive, open, super fast, and can run anywhere. It provides high performance for serverless functions. - It supports a variety of programming languages and event sources. Functions can be easily deployed and auto-scaled. - Nuclio is presented as a solution for complex serverless applications through its data integration capabilities and high performance. Examples of real-time applications using Nuclio are described like fleet management, surveillance, and more.

Introduction to the Processor API

confluent

The document introduces the Kafka Streams Processor API. It provides more fine-grained control over event processing compared to the Kafka Streams DSL. The Processor API allows access to state stores, record metadata, and scheduled processing via punctuators. It can be used to augment applications built with the Kafka Streams DSL by providing capabilities like random access to state stores and time-based processing.

Log management with ELK

Geert Pante

ELK (Elasticsearch, Logstash, Kibana) is an open source toolset for centralized logging, where Logstash collects, parses, and filters logs, Elasticsearch stores and indexes logs for search, and Kibana visualizes logs. Logstash processes logs through an input, filter, output pipeline using plugins. It can interpret various log formats and event types. Elasticsearch allows real-time search and scaling through replication/sharding. Kibana provides browser-based dashboards and visualization of Elasticsearch query results.

Elastic Stack is a suite of open source tools for log analytics and data processing including Beats, Logstash, Elasticsearch, Kibana, Curator, and hosted cloud solutions. Beats are lightweight data shippers that collect data from endpoints and send to Logstash or Elasticsearch. Logstash is used for data collection, transformation, and transport to Elasticsearch for storage and search. Kibana provides data visualization and dashboards. Curator manages Elasticsearch indices. The Elastic Stack can be self-hosted or used via cloud offerings.

Building a system for machine and event-oriented data with Rocana

Treasure Data, Inc.

In this session, we’ll follow the flow of data through an end-to-end system built to handle tens of terabytes an hour of event-oriented data, providing real-time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive can be stitched together to form the base platform; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality. Finally, a brief demo of Rocana Ops, an application for large scale data center operations, will be given, along with an explanation about how it uses the underlying platform.

Centralized Logging System Using ELK Stack

Rohit Sharma

Centralized Logging System using ELK Stack The document discusses setting up a centralized logging system (CLS) using the ELK stack. The ELK stack consists of Logstash to capture and filter logs, Elasticsearch to index and store logs, and Kibana to visualize logs. Logstash agents on each server ship logs to Logstash, which filters and sends logs to Elasticsearch for indexing. Kibana queries Elasticsearch and presents logs through interactive dashboards. A CLS provides benefits like log analysis, auditing, compliance, and a single point of control. The ELK stack is an open-source solution that is scalable, customizable, and integrates with other tools.

Change Data Capture - Scale by the Bay 2019

Petr Zapletal

The document discusses change data capture (CDC) in distributed systems. CDC allows capturing changes to data and notifying actors so they can react accordingly. Some considerations for CDC include reliability, scalability, performance, consistency, and fault tolerance. Common strategies involve using timestamps, triggers, diffs, or log scraping. CDC has various use cases such as caching, search indexing, offline processing, and simplifying monolithic applications. Popular frameworks for implementing CDC include pg2k4j, Maxwell, AWS Database Migration Service, SpinalTap, DataBus, and Debezium.

The Rise of Streaming SQL

Sriskandarajah Suhothayan

Elastic Stack Introduction

Vikram Shinde

Apache airflow

Purna Chander

This document provides an overview of building data pipelines using Apache Airflow. It discusses what a data pipeline is, common components of data pipelines like data ingestion and processing, and issues with traditional data flows. It then introduces Apache Airflow, describing its features like being fault tolerant and supporting Python code. The core components of Airflow including the web server, scheduler, executor, and worker processes are explained. Key concepts like DAGs, operators, tasks, and workflows are defined. Finally, it demonstrates Airflow through an example DAG that extracts and cleanses tweets.

Exploring Relay land

Stefano Masini

- Relay is a library that uses GraphQL as its query language and is designed to efficiently manage data fetching and caching for React applications. - GraphQL provides an alternative to REST APIs that focuses on only fetching the necessary data in a single request. It allows clients to define precisely the data they need through a type system. - Relay colocates queries and components to optimize data fetching. It caches data to improve performance and allows components to declare their data requirements through fragments.

Elk

Caleb Wang

Kibana + timelion: time series with the elastic stack

Sylvain Wallez

The document discusses Kibana and Timelion, which are tools for visualizing and analyzing time series data in the Elastic Stack. It provides an overview of Kibana's evolution and capabilities for creating dashboards. Timelion is introduced as a scripting language that allows users to transform, aggregate, and calculate on time series data from multiple sources to create visualizations. The document demonstrates Timelion's expression language, which includes functions, combinations, filtering, and attributes to process and render time series graphs.

Airflow presentation

Ilias Okacha

Airflow is a workflow management system for authoring, scheduling and monitoring workflows or directed acyclic graphs (DAGs) of tasks. It has features like DAGs to define tasks and their relationships, operators to describe tasks, sensors to monitor external systems, hooks to connect to external APIs and databases, and a user interface for visualizing pipelines and monitoring runs. Airflow uses a variety of executors like SequentialExecutor, CeleryExecutor and MesosExecutor to run tasks on schedulers like Celery or Kubernetes. It provides security features like authentication, authorization and impersonation to manage access.

Fluentd and Docker - running fluentd within a docker container

Treasure Data, Inc.

Fluentd is a data collection tool for unified logging that allows for extensible and reliable data collection. It uses a simple core with plugins to provide buffering, high availability, load balancing, and streaming data transfer based on JSON. Fluentd can collect log data from various sources and output to different destinations in a flexible way using its plugin architecture and configuration files. It is widely used in production for tasks like log aggregation, filtering, and forwarding.

Apache Airflow overview

NikolayGrishchenkov

Apache Airflow is an open-source workflow management platform developed by Airbnb and now an Apache Software Foundation project. It allows users to define and manage data pipelines as directed acyclic graphs (DAGs) of tasks. The tasks can be operators to perform actions, move data between systems, and use sensors to monitor external systems. Airflow provides a rich web UI, CLI and integrations with databases, Hadoop, AWS and others. It is scalable, supports dynamic task generation and templates, alerting, retries, and distributed execution across clusters.

Data Pipelines with Apache Airflow

Manning Publications

A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodge-podge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. Check out the contents on our browser-based liveBook reader here: https://livebook.manning.com/book/data-pipelines-with-apache-airflow/

Experience with C++11 in ArangoDB

Max Neunhöffer

ArangoDB is an open source, multi-model NoSQL database that is written in C++ and embeds Google's V8 engine to implement the higher levels of its functionality in JavaScript. Recently we decided to switch from C++03 to C++11 for the database kernel. In this talk I will first give a short overview of the software architecture of ArangoDB and proceed to tell you about our practical experiences with the switch to C++11. I will explain which of the parts of the "new" standard have been more important and which have been less useful, and I will report about the difficulties we encountered.

Airflow tutorials hands_on

pko89403

This document provides an overview of Airflow, an open-source workflow management platform for authoring, scheduling and monitoring data pipelines. It describes Airflow's key components including the web server, scheduler, workers and metadata database. It explains how Airflow works by parsing DAGs, instantiating tasks and changing their state as they are scheduled, queued, run and monitored. The document also covers concepts like DAGs, operators, dependencies, concurrency vs parallelism and advanced topics such as subDAGs, hooks, XCOM and branching workflows.

Workflow Engines + Luigi

Vladislav Supalov

Vladislav Supalov introduces data pipeline architecture and workflow engines like Luigi. He discusses how custom scripts are problematic for maintaining data pipelines and recommends using workflow engines instead. Luigi is presented as a Python-based workflow engine that was created at Spotify to manage thousands of daily Hadoop jobs. It provides features like parameterization, email alerts, dependency resolution, and task scheduling through a central scheduler. Luigi aims to minimize boilerplate code and make pipelines testable, versioning-friendly, and collaborative.

From business requirements to working pipelines with apache airflow

Derrick Qin

Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...

Andrii Vozniuk

My workshop at the Learning Analytics Summer Institute (LASI) 2016: http://lasi16.snola.es/#!/schedule/113 Educational data continues to grow in volume, velocity and variety. Making sense of the educational data in such conditions requires deployment and usage of appropriate scalable, real-time processing tools supporting a flexible data schema. Elasticsearch is one of the popular open-source tools meeting the enlisted requirements. Initially envisioned as a search engine capable of operating at scale and in real time, Elasticsearch is used by organisations such as Wikimedia and Github, which deal with big data on daily basis. In addition, Elasticsearch is used increasingly often as analytics platform thanks to its scalable architecture and expressive query language. Until recently, the exploitation of Elasticsearch for (learning) analytical purposes by practitioners was hindered by a high entrance barrier due to the complexity of the query language and the query specificities. This is currently changing with the ongoing development of Kibana, an open-source tool that allows to conduct analysis and build visualisations of Elasticsearch data through a graphical user interface. Kibana does not require the user to dive into technical details of the queries (although it is still possible) and hence makes big educational data visualisations accessible to regular users. The additional value of Kibana comes in play whenever several visualisations are combined on a single dashboard, enabling to use multiple coordinated views for an interactive explorative analysis. Both Elasticsearch and Kibana, together with Logstash are part of an analytics stack often referred to as ELK. Logstash supports data acquisition from multiple sources (including twitter, RSS, event logs) thanks to its rich set of available connectors. Custom connectors can be developed for case-specific sources. In addition to the mentioned values, ELK enables building analytics infrastructures decoupled from the learning platform, i.e., it allows to host separately the learning environment (with the analytics functionalities) and the data storage without affecting the end-user experience.

Javantura v3 - Just say it – using language to communicate with the computer ...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document discusses using natural language to communicate with computers. It notes that while graphical user interfaces are commonly used, language can also be an intuitive way to express complex ideas. The document outlines some current and potential applications of natural language interfaces, including data entry, text search, and supporting more formal grammars or domain-specific languages. It suggests areas where natural language processing could be improved, such as better understanding of grammar, synonyms and ontologies. The conclusion is that natural language can be used to communicate more efficiently and accessibly with computers, especially when complementing graphical user interfaces.

Javantura v3 - FIWARE – from ideas to real projects – Krunoslav Hrnjak

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document discusses FIWARE, an open source platform for developing smart applications and services. It provides several accelerators to support digital business, including a cloud portal, catalogue, and data portal. The document also mentions open data, phases of the Finodex project, a success story, and deliverables. It notes that the project is funded by the EU Seventh Framework Programme and discusses benefits of FIWARE.

What's hot

ELK - Stack - Munich .net UG

Steve Behrendt

Elastic Stack ELK, Beats, and Cloud

Joe Ryan

Building a system for machine and event-oriented data with Rocana

Treasure Data, Inc.

Centralized Logging System Using ELK Stack

Rohit Sharma

Change Data Capture - Scale by the Bay 2019

Petr Zapletal

The Rise of Streaming SQL

Sriskandarajah Suhothayan

Elastic Stack Introduction

Apache airflow

Exploring Relay land

Elk

Kibana + timelion: time series with the elastic stack

Sylvain Wallez

Airflow presentation

Ilias Okacha

Fluentd and Docker - running fluentd within a docker container

Treasure Data, Inc.

Apache Airflow overview

NikolayGrishchenkov

Data Pipelines with Apache Airflow

Manning Publications

Experience with C++11 in ArangoDB

Max Neunhöffer

Airflow tutorials hands_on

pko89403

Workflow Engines + Luigi

Vladislav Supalov

From business requirements to working pipelines with apache airflow

Derrick Qin

Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...

Andrii Vozniuk

What's hot (20)

ELK - Stack - Munich .net UG

Elastic Stack ELK, Beats, and Cloud

Building a system for machine and event-oriented data with Rocana

Centralized Logging System Using ELK Stack

Change Data Capture - Scale by the Bay 2019

The Rise of Streaming SQL

Elastic Stack Introduction

Apache airflow

Exploring Relay land

Elk

Kibana + timelion: time series with the elastic stack

Airflow presentation

Fluentd and Docker - running fluentd within a docker container

Apache Airflow overview

Data Pipelines with Apache Airflow

Experience with C++11 in ArangoDB

Airflow tutorials hands_on

Workflow Engines + Luigi

From business requirements to working pipelines with apache airflow

Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...

Viewers also liked

Javantura v3 - Just say it – using language to communicate with the computer ...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Javantura v3 - FIWARE – from ideas to real projects – Krunoslav Hrnjak

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Javantura v3 - Going Reactive with RxJava – Hrvoje Crnjak

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

RxJava allows building reactive applications by treating everything as a stream of messages. Observables represent message producers and observers consume messages. Observables provide asynchronous and parallel execution via operators like subscribeOn and observeOn. This makes applications resilient to failure, scalable under varying workloads, and responsive to clients. RxJava also promotes message-driven architectures, functional programming, and handling errors as regular messages to improve these characteristics. Developers must also unsubscribe to prevent leaking resources and ensure observables only run when needed.

Javantura v3 - Java & JWT Stateless authentication – Karlo Novak

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Javantura v3 - ES6 – Future Is Now – Nenad Pečanac

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

EcmaScript 6 introduces many new features to JavaScript including let and const block-scoped variables, arrow functions, classes, promises, and modules. Tooling like transpilers are currently needed to use ES6 in older browsers. Future versions will continue to evolve the language with proposals for ES7 including concurrency, streams, and better support for classes and metaprogramming. Overall ES6 and future versions aim to modernize JavaScript and better support object-oriented and asynchronous programming patterns.

Javantura v3 - The Internet of (Lego) Trains – Johan Janssen, Ingmar van der ...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Javantura v3 - Conquer the Internet of Things with Java and Docker – Johan Ja...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document discusses using Docker and Java on a Raspberry Pi. It outlines some advantages of Docker like enabling continuous delivery, provisioning environments quickly and easily, updating and synchronizing environments, disk space and memory efficiency. It then provides steps to install and run Docker on a Raspberry Pi, including building Dockerfiles and containers for Tomcat and a sample web application. It also demonstrates creating a local Docker registry to push and pull images.

Javantura v3 - CQRS – another view on application architecture – Aleksandar S...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Javantura v3 - Apache Spark revolution – what’s it all about – Petar Zečević

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Javantura v3 - Husky – (y)our tool for tracking value in data – Mladen Marovi...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

1. Husky is a tool developed by Combis for tracking and analyzing big data, especially for telecommunications companies. 2. It allows telcos to monetize customer data by generating additional revenue from data utilization and reducing costs through better decision making. 3. The tool provides a simple and easy to use interface for data integration, processing, and generating reports and heat maps to help with customer reachability, planning, and targeted marketing campaigns.

Javantura v3 - Microservice – no fluff the REAL stuff – Nakul Mishra

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document discusses microservices and provides an overview of common microservice concepts. It begins with discussing problems with monolithic architectures and then covers topics like service registration with Eureka, load balancing with Ribbon, edge services with Zuul, and failure management with Hystrix. Both pros and cons of the microservices approach are presented. The document concludes with an example demo of a microservices architecture using Spring Cloud and a request for any questions.

Javantura v3 - Rational Team Concert – integrated agile development and colla...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Javantura v3 - What really motivates developers – Ivan Krnić

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

The document discusses what really motivates developers and teams. It explains that autonomy, mastery, and purpose are the key motivators. Autonomy refers to self-direction and choice, mastery is getting better at tasks, and purpose is finding a goal larger than oneself. Agile frameworks like Scrum help provide these motivators by enabling self-organization, continuous improvement, and aligning work with a clear product vision. The document advocates giving teams the environment and support needed and trusting them to perform.

Javantura v3 - Real-time BigData ingestion and querying of aggregated data – ...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document discusses Infobip's journey towards enabling real-time querying of aggregated data. Initially, Infobip had a monolithic architecture with a single database that became a bottleneck. They introduced multiple databases and microservices but querying spanned databases and results had to be joined. A data warehouse (GREEN) provided reporting but was not real-time. To enable real-time queries, Infobip implemented a lambda architecture using Kafka as the real-time data pipeline and Druid for real-time querying and aggregations, achieving sub-second responses and less than 2 seconds of data delay. This allows real-time insights from ingested messaging data while GREEN remains the batch/serving layer.

Javantura v4 - Java or Scala – Web development with Playframework 2.5.x - Kre...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document compares Java and Scala for web development using the Playframework 2.5.x. It provides an overview of Scala and the Playframework, including how to set up a Play application with Scala. It demonstrates building a sample web application for a speaker database using Play and Scala, including setting up the project structure, models, database integration, JSON handling, testing, and deployment. The document aims to demonstrate how to develop a web application from start to finish using the Playframework and Scala.

Javantura v4 - CroDuke Indy and the Kingdom of Java Skills - Branko Mihaljevi...

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

The document provides an overview of the history and evolution of Java, including key events and releases. It discusses the original Java Man fossil discovery in 1891 and notes how 100 years later, a new generation of Java Men were discovered with the creation of the Java programming language in 1991. The summary also mentions that Indy, the famous film character, served as inspiration for CroDuke Indy, HUJAK's Java conference mascot.

Javantura v4 - DMN – supplement your BPMN - Željko Šmaguc

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document discusses decision modeling notation (DMN) and how it can be used to supplement business process modeling notation (BPMN). It provides an overview of DMN including decision requirements diagrams, decision tables, the FEEL expression language, and conformance levels. Examples are given of modeling price calculation decisions. Tools for DMN include the Drools business rules management system, which provides level 3 conformance for modeling executable decisions.

Javantura v4 - JVM++ The GraalVM - Martin Toshev

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

The document provides an overview of the Graal VM, which is a new just-in-time (JIT) compiler for the Java Virtual Machine (JVM) that brings high performance to scripting languages via the Truffle API. It discusses how Graal generates machine code from an optimized AST rather than bytecode, and how the Truffle API allows creation of language interpreters in Graal. Performance benchmarks show Graal improving performance for languages like JavaScript, Ruby, and R compared to other implementations. Future directions include supporting more languages and compiler optimizations in Graal's extensible framework.

Javantura v4 - FreeMarker in Spring web - Marin Kalapać

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

Javantura v4 - Getting started with Apache Spark - Dinko Srkoč

HUJAK - Hrvatska udruga Java korisnika / Croatian Java User Association

This document provides an introduction to Apache Spark and Scala. It discusses that Apache Spark is a general purpose cluster computing system that is faster than Hadoop MapReduce, runs locally and in the cloud. It has high-level APIs for Scala, Python, Java and R. The document outlines Spark's core components including SQL, MLlib, GraphX and streaming. It describes Spark's main data collections of RDDs for unstructured data and DataFrames/Datasets for structured data. Finally, it provides an overview of demonstrations that will be covered including the Spark shell, notebook, streaming and deploying a mini project to Google Cloud Dataproc.

Viewers also liked (20)