Maven is a project management and comprehension tool. Maven provides developers a complete build lifecycle framework. Development team can automate the project's build infrastructure in almost no time as Maven uses a standard directory layout and a default build lifecycle.
In case of multiple development teams environment, Maven can set-up the way to work as per standards in a very short time. As most of the project setups are simple and reusable, Maven makes life of developer easy while creating reports, checks, build and testing automation setups.
Here are slides from basic training for Gradle.
This training is aimed to help Java Developers to get hands-on experience to use Gradle as a primary build tool for Java source code starting from simple compilation continuing with different kinds of tests and finishing with code quality analysis and artefacts publishing.
Azul Product Manager Matt Schuetze's presentation on JVM memory details to the Philadelphia Java User Group.
This session dovetails with the March, 2014 PhillyJUG deep dive session topic focused on Java compiler code transformation and JVM runtime execution. That session exposes myths that Java is slow and Java uses too much memory. In this session we will take a deeper look at Java memory management. The dreaded Out of Memory (OOM) error is one problem. Garbage collector activity and spikes leading to long pauses is another. He covers the foundations of garbage collection and why historically Java gets a bad rap, even though GC provides a marvelous memory management paradigm.
The features released between Java 11 and Java 17 have brought a greater opportunity for developers to improve application development productivity as well and code expressiveness and readability. In this deep-dive session, you will discover all the recent Project Amber features added to the Java language such as Text blocks, Records (including Records serialization), Pattern Matching for instanceof, switch expression, sealed classes, and pattern matching for switch. The main goal of the Amber Project is to bring Pattern Matching to the Java platform, which will impact both the language and the JDK APIs. You will discover record patterns, array patterns, as well as deconstruction patterns, through constructors, factory methods, and deconstructors.
Maven is a project management and comprehension tool. Maven provides developers a complete build lifecycle framework. Development team can automate the project's build infrastructure in almost no time as Maven uses a standard directory layout and a default build lifecycle.
In case of multiple development teams environment, Maven can set-up the way to work as per standards in a very short time. As most of the project setups are simple and reusable, Maven makes life of developer easy while creating reports, checks, build and testing automation setups.
Here are slides from basic training for Gradle.
This training is aimed to help Java Developers to get hands-on experience to use Gradle as a primary build tool for Java source code starting from simple compilation continuing with different kinds of tests and finishing with code quality analysis and artefacts publishing.
Azul Product Manager Matt Schuetze's presentation on JVM memory details to the Philadelphia Java User Group.
This session dovetails with the March, 2014 PhillyJUG deep dive session topic focused on Java compiler code transformation and JVM runtime execution. That session exposes myths that Java is slow and Java uses too much memory. In this session we will take a deeper look at Java memory management. The dreaded Out of Memory (OOM) error is one problem. Garbage collector activity and spikes leading to long pauses is another. He covers the foundations of garbage collection and why historically Java gets a bad rap, even though GC provides a marvelous memory management paradigm.
The features released between Java 11 and Java 17 have brought a greater opportunity for developers to improve application development productivity as well and code expressiveness and readability. In this deep-dive session, you will discover all the recent Project Amber features added to the Java language such as Text blocks, Records (including Records serialization), Pattern Matching for instanceof, switch expression, sealed classes, and pattern matching for switch. The main goal of the Amber Project is to bring Pattern Matching to the Java platform, which will impact both the language and the JDK APIs. You will discover record patterns, array patterns, as well as deconstruction patterns, through constructors, factory methods, and deconstructors.
An Introduction to Makefile.
about 23 slides to present you a quick start to the make utility, its usage and working principles. Some tips/examples in order to understand and write your own
Makefiles.
In this presentation you will learn why this utility continues to hold its top position in project build software, despite many younger competitors.
Visit Do you know Magazine : https://www.facebook.com/douknowmagazine
The features released between Java 11 and Java 17 have brought a greater opportunity for developers to improve application development productivity as well and code expressiveness and readability. In this deep-dive session, you will discover all the recent Project Amber features added to the Java language such as Records (including Records serialization), Pattern Matching for `instanceof`, switch expression, sealed classes, and hidden classes. The main goal of the Amber Project is to bring Pattern Matching to the Java platform, which will impact both the language and the JDK APsI. You will discover record patterns, array patterns, as well as deconstruction patterns, through constructors, factory methods, and deconstructors.
You can find the code shown here: https://github.com/JosePaumard/devoxx-uk-2021
Maven is a build automation tool used primarily for Java projects. This presentation will cover the basics of Maven and its usage while developing Java application.This is for anyone interested to learn Maven especially the Java developers.
Percona Live 2022 - The Evolution of a MySQL Database SystemFrederic Descamps
From a single MySQL instance to multi-site high availability, this is what you will find out in this presentation. You will learn how to make this transition and which solutions best suit changing business requirements (RPO, RTO). Recently, MySQL has extended the possibilities for easy deployment of architecture with integrated tools. Come and discover these open source solutions that are part of MySQL.
This session will give attendees an overview of the new testing features in Spring 3.1 as well the new Spring MVC test support. Sam Brannen will demonstrate how to use the Spring TestContext Framework to write integration tests for Java-based Spring configuration using @Configuration classes. He'll then compare and contrast this approach with XML-based configuration and follow up with a discussion of the new testing support for bean definition profiles. Next, Rossen Stoyanchev will show attendees how testing server-side code with annotated controllers and client-side code with the RestTemplate just got a whole lot easier with the new Spring MVC test support. Come to this session to see these new Spring testing features in action and learn how you can get involved in the Spring MVC Test Support project.
Jenkins is a Continuous Integration tool to manage your environment that fires off jobs like cron or when a button is pushed. This talk will walk you through setting up a Jenkins site, complete with slave nodes on the servers doing the real work, and some simple jobs to get a feel for what it can do for you.
Real life examples included based on an actual migration between data centers requiring Jenkins had to be installed fresh.
This talk has been presented at:
2016-08-20 in Philadelphia
- https://fosscon.us/
2016-08-26 in Cluj-Napoca, Romania
- http://act.yapc.eu/ye2016/talk/6751
- https://youtu.be/Nj84bBCssps
A quick introduction about everything that's new in Java 11. Includes API changes, language changes and new tools in the JDK.
Demo's for this presentation can be found here: https://github.com/MichelSchudel/java11demo
An Introduction to Makefile.
about 23 slides to present you a quick start to the make utility, its usage and working principles. Some tips/examples in order to understand and write your own
Makefiles.
In this presentation you will learn why this utility continues to hold its top position in project build software, despite many younger competitors.
Visit Do you know Magazine : https://www.facebook.com/douknowmagazine
The features released between Java 11 and Java 17 have brought a greater opportunity for developers to improve application development productivity as well and code expressiveness and readability. In this deep-dive session, you will discover all the recent Project Amber features added to the Java language such as Records (including Records serialization), Pattern Matching for `instanceof`, switch expression, sealed classes, and hidden classes. The main goal of the Amber Project is to bring Pattern Matching to the Java platform, which will impact both the language and the JDK APsI. You will discover record patterns, array patterns, as well as deconstruction patterns, through constructors, factory methods, and deconstructors.
You can find the code shown here: https://github.com/JosePaumard/devoxx-uk-2021
Maven is a build automation tool used primarily for Java projects. This presentation will cover the basics of Maven and its usage while developing Java application.This is for anyone interested to learn Maven especially the Java developers.
Percona Live 2022 - The Evolution of a MySQL Database SystemFrederic Descamps
From a single MySQL instance to multi-site high availability, this is what you will find out in this presentation. You will learn how to make this transition and which solutions best suit changing business requirements (RPO, RTO). Recently, MySQL has extended the possibilities for easy deployment of architecture with integrated tools. Come and discover these open source solutions that are part of MySQL.
This session will give attendees an overview of the new testing features in Spring 3.1 as well the new Spring MVC test support. Sam Brannen will demonstrate how to use the Spring TestContext Framework to write integration tests for Java-based Spring configuration using @Configuration classes. He'll then compare and contrast this approach with XML-based configuration and follow up with a discussion of the new testing support for bean definition profiles. Next, Rossen Stoyanchev will show attendees how testing server-side code with annotated controllers and client-side code with the RestTemplate just got a whole lot easier with the new Spring MVC test support. Come to this session to see these new Spring testing features in action and learn how you can get involved in the Spring MVC Test Support project.
Jenkins is a Continuous Integration tool to manage your environment that fires off jobs like cron or when a button is pushed. This talk will walk you through setting up a Jenkins site, complete with slave nodes on the servers doing the real work, and some simple jobs to get a feel for what it can do for you.
Real life examples included based on an actual migration between data centers requiring Jenkins had to be installed fresh.
This talk has been presented at:
2016-08-20 in Philadelphia
- https://fosscon.us/
2016-08-26 in Cluj-Napoca, Romania
- http://act.yapc.eu/ye2016/talk/6751
- https://youtu.be/Nj84bBCssps
A quick introduction about everything that's new in Java 11. Includes API changes, language changes and new tools in the JDK.
Demo's for this presentation can be found here: https://github.com/MichelSchudel/java11demo
Faster java ee builds with gradle [con4921]Ryan Cuprak
JavaOne 2016
It is time to move your Java EE builds over to Gradle! Gradle continues to gain momentum across the industry. In fact, Google is now pushing Gradle for Android development. Gradle draws on lessons learned from both Ant and Maven and is the next evolutionary step in Java build tools. This session covers the basics of switching existing Java EE projects (that use Maven) over to Gradle and the benefits you will reap, such as incremental compiling, custom distributions, and task parallelization. You’ll see demos of all the goodies you’ve come to expect, such as integration testing and leveraging of Docker. Switching is easier than you think, and no refactoring is required.
Gradle is a general-purpose build automation tool. It combines the power and flexibility of Ant with the dependency management and conventions of Maven into a more effective way to build. Its powered by Groovy DSL. Presentation discusses what and why Gradle with demo for java, groovy, web, multi-project and grails projects.
In this session, Markus will explain how to build OpenCms with Gradle from Source.
He will explain the benefits and advantages as well as the difficulties in building OpenCms with Gradle. Markus will show how to build and import OpenCms modules with Gradle. This includes creating an OpenCms module automatically from source out of the repository (Nexus).
Markus will also talk about:
- Continuous Developement with OpenCms
- Building an OpenCms .war file with external configuration
August 7th, I attended a meetup of GDG Beijing, and give a presentation:Android Gradle Build System-Overview.
Mainly cover build system background knowledge, source code, interesting part of code, writing a plugin.
A general- ‐purpose build automation tool. It can automate building, testing, deployment, publishing, generate documentation etc.
Designed to take advantage of convention over configuration.
Combines the power and flexibility of Ant with the dependency management and
conventions of Maven into a more effective way to build.
How to integrate_custom_openstack_services_with_devstackSławomir Kapłoński
Presentation about how to configure and use DevStack to deploy all-in-one OpenStack cluster for development and testing purpose.
It also shows how to integrate own OpenStack service with DevStack using DevStack's plugins system
CDI portable extensions are one of greatest features of Java EE allowing the platform to be extended in a clean and portable way. But allowing extension is just part of the story. CDI opens the door to a whole new eco-system for Java EE, but it’s not the role of the specification to create these extensions.
Apache DeltaSpike is the project that leads this brand new eco-system by providing useful extension modules for CDI applications as well as tools to ease the creation of new ones.
In this session, we’ll start by presenting the DeltaSpike toolbox and show how it helps you to develop for CDI. Then we’ll describe the major extensions included in DeltaSpike, including 'configuration', 'scheduling' and 'data'.
CDI portable extensions are one of greatest features of Java EE allowing the platform to be extended in a clean and portable way. But allowing extension is just part of the story. CDI opens the door to a whole new eco-system for Java EE, but it’s not the role of the specification to create these extensions.
Apache DeltaSpike is the project that leads this brand new eco-system by providing useful extension modules for CDI applications as well as tools to ease the creation of new ones.
In this session, we’ll start by presenting the DeltaSpike toolbox and show how it helps you to develop for CDI. Then we’ll describe the major extensions included in Deltaspike, including 'configuration', 'scheduling' and 'data'.
Talk at RubyKaigi 2015.
Plugin architecture is known as a technique that brings extensibility to a program. Ruby has good language features for plugins. RubyGems.org is an excellent platform for plugin distribution. However, creating plugin architecture is not as easy as writing code without it: plugin loader, packaging, loosely-coupled API, and performance. Loading two versions of a gem is a unsolved challenge that is solved in Java on the other hand.
I have designed some open-source software such as Fluentd and Embulk. They provide most of functions by plugins. I will talk about their plugin-based architecture.
In these slides is given an overview of the different parts of Apache Spark.
We analyze spark shell both in scala and python. Then we consider Spark SQL with an introduction to Data Frame API. Finally we describe Spark Streaming and we make some code examples.
Topics:spark-shell, pyspark, HDFS, how to copy file to HDFS, spark transformations, spark actions, Spark SQL (Shark),
spark streaming, streaming transformation stateless vs stateful, sliding windows, examples
In these slides we analyze why the aggregate data models change the way data is stored and manipulated. We introduce MapReduce and its open source implementation Hadoop. We consider how MapReduce jobs are written and executed by Hadoop.
Finally we introduce spark using a docker image and we show how to use anonymous function in spark.
The topics of the next slides will be
- Spark Shell (Scala, Python)
- Shark Shell
- Data Frames
- Spark Streaming
- Code Examples: Data Processing and Machine Learning
In this lecture we analyze graph oriented databases. In particular, we consider TtitanDB as graph database. We analyze how to query using gremlin and how to create edges and vertex.
Finally, we presents how to use rexster to visualize the storeg graph/
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
In these slides we introduce Column-Oriented Stores. We deeply analyze Google BigTable. We discuss about features, data model, architecture, components and its implementation. In the second part we discuss all the major open source implementation for column-oriented databases.
In this lecture we analyze key-values databases. At first we introduce key-value characteristics, advantages and disadvantages.
Then we analyze the major Key-Value data stores and finally we discuss about Dynamo DB.
In particular we consider how Dynamo DB: How is implemented
1. Motivation Background
2. Partitioning: Consistent Hashing
3. High Availability for writes: Vector Clocks
4. Handling temporary failures: Sloppy Quorum
5. Recovering from failures: Merkle Trees
6. Membership and failure detection: Gossip Protocol
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me to download the slides
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me to download the slides
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...Fabio Fumarola
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
2. Java Project related questions
• How do we create a java project?
• How do we add dependencies?
• How do we create a runnable jar?
• How do we create a war?
• How do we execute tests?
2
4. What is Maven?
• Apache Maven is a software project management.
• It is based on the concept of a Project Object Model
(POM)
• Maven can mange a project’s build, reporting a
documentation form a central piece of information
But it isn’t a mere build tool
4
5. Maven features
• Dependency System
• Multi-module builds
• Consistent project structure
• Consistent build model
• Plugin oriented
• Project generated sites
5
6. Advantages over Ant
• Eliminate complicate scripts
• All the functionality required to build your project,
i.e., clean, compile, copy, resources, install, deploy
• Cross Project Reuse – Ant has no convenient way to
reuse target across projects.
6
9. POM
• Unit of work in Maven
• It is an XML file that contains the information and
the configuration details used by Maven to build the
project
9
10. Archetype
• It is a template of a project with is combined with
some user input to produce a working Maven project
• There are hundreds of archetypes to help us get
started
– $ mvn –version
– $ mvn archetype:generate
-DgroupId=com.mycompany.app -DartifactId=my-app
-DarchetypeArtifactId=maven-archetype-quickstart
-DinteractiveMode=true
– $ mvn eclipse:eclipse
10
11. Artifact
• An artifact is a module obtained by another artifact
deployed to a maven repository
• Each artifact belongs to a group
• Each group can have more artifacts
11
<dependency>
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>2.6</version>
</dependency>
12. Goals and Plugins
• Are an extension of the standard maven lifecycles
steps.
– Clean, compile, test, package, install and deploy
• There are plugin to do other steps such as
12
<plugins>
<plugin>
<artifactId>maven-assembly-plugin</artifactId>
<version>2.5.3</version>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
[...]
</plugin> </plugins>
13. What is Gradle?
• Gradle is a general purpose build system
• It comes with a rich build description
• language (DSL) based on Groovy
• It supports ”build-by-convention” principle
• But it is very flexible and extensible
• It has built-in plug-ins for Java, Groovy, Scala, Web
• Groovy as a base language allows imperative
programming in the build file.
13
14. Advanced features
• Parallel unit test execution
• Dependency building
• Incremental build support
• Dynamic tasks and task rules
• Gradle daemon
14
15. Hello, Gradle
• Create a file build.gradle
task hello << {
println 'Hello World'
}
• Run $ gradle hello
• Edit the file as below
task hello << {
print 'Hello '
}
task world(dependsOn: hello) << {
println 'World!"
}
• Run $ gradle –q world
15
29. Extending your build
• Any Gradle script can be a plug-in:
apply from: 'otherScript.gradle'
apply from: 'http://mycomp.com/otherScript.gradle’
• Use many of the standard or 3rd-party plug-ins:
apply plugin: 'java'
apply plugin: 'groovy'
apply plugin: 'scala'
apply plugin: 'war'
29
33. Sbt
• It is a tool to define task and then run them from the
shell
• To install on linux grab the
– rpm: https://dl.bintray.com/sbt/rpm/sbt-0.13.7.rpm
– deb: https://dl.bintray.com/sbt/rpm/sbt-0.13.7.deb
33
34. Starting with sbt
• It is based on the file build.sbt
• Create a directory hello_scala
• Edit a file and add
lazy val root = (project in file(".")).
settings(
name.:=("hello”),
version := "1.0",
scalaVersion := "2.11.4"
)
• Create an HelloWorld class
34
36. Running sbt
• It has a shell via $sbt
• Or we can run task in batch
– $ sbt clean compile "testOnly TestA TestB”
• It also support continuos delivery and run via
– > ~compile
– > ~run
36
37. Creating a project definition
• To create a project in the build.sbt file
lazy val root = (project in file(.)).
settings(
organization := "com.example",
version := "0.1.0",
scalaVersion := "2.11.4"
)
• The build.sbt define a Project which holds a list of
scala expression called settings
• Moreover a build.sbt can have vals, lazy vals and defs
37
38. SettingKey, TaskKey and InputKey
lazy val root = (project in file(".")).
settings(
name.:=("hello")
)
•.:= is a method that takes a parameter and return s
Setting[String]
lazy val root = (project in file(".")).
settings(
name := 42 // will not compile
)
•Does it run?
38
39. Types of key
• SettingKey[T]: a key for a value computed once (the
value is computed when loading the project, and
kept around).
• TaskKey[T]: a key for a value, called a task, that has
to be recomputed each time, potentially with side
effects.
• InputKey[T]: a key for a task that has command line
arguments as input. Check out Input Tasks for more
details.
• The built-in key are field of the object Keys
(http://www.scala-
sbt.org/0.13/sxr/sbt/Keys.scala.html) 39
40. Custom Keys
• Can be created with their:
– settingKey, taskKey and inputKey
lazy val hello = taskKey[Unit](“An example task”)
• A TaskKey[T] can be used to define task such as
compile or package.
40
41. Defining tasks and settings
• For example, to implement the hello task
lazy val hello = taskKey[Unit]("An example task”)
lazy val root = (project in file(".")).
settings(
hello := { println("Hello!") }
)
• Imports in build.sbt
import sbt._
import Process._
import Keys._
41
42. Adding library dependencies
val derby = "org.apache.derby" % "derby" % "10.4.1.3"
lazy val commonSettings = Seq(
organization := "com.example",
version := "0.1.0",
scalaVersion := "2.11.4"
)
lazy val root = (project in file(".")).
settings(commonSettings: _*).
settings(
name := "hello",
libraryDependencies += derby
)
42
43. Dependencies
• To add an unmanaged dependency there is the task
unmanagedBase := baseDirectory.value / "custom_lib”
• To add an managed dependency there is the task
libraryDependencies += groupID % artifactID % revision
• Or
libraryDependencies ++= Seq(
groupID % artifactID % revision,
groupID % otherID % otherRevision
)
43
44. Resolvers
• To add a resolver
1. resolvers += name at location
2. resolvers += Resolver.mavenLocal
3. resolvers ++= Seq(name1 at location1, name2 at
location2)
• Is it possible also to define if the dependency is for
test
libraryDependencies += "org.apache.derby" % "derby" %
"10.4.1.3" % Test
44