#JFreeChart

[jackbtree]

Big Data and Data Science with Java

Java has become an increasingly popular programming language for Big Data and Data Science applications, thanks to its performance, scalability, and rich set of libraries and frameworks. Here are some key points to keep in mind when developing Big Data and Data Science applications with Java:

Hadoop is the most popular Big Data framework and is written in Java. Hadoop provides a distributed file system (HDFS) and a distributed processing system (MapReduce) for large-scale data processing.

Spark is a popular distributed computing engine that can be used for Big Data processing. Spark is also written in Java and provides APIs for Java, Scala, Python, and R.

Apache Storm is another popular real-time distributed computing framework for Big Data processing. It is written in Java and provides APIs for Java and other languages.

Java has several powerful libraries for Data Science, including Weka, Apache Mahout, and Deeplearning4j. These libraries provide tools for machine learning, data mining, and deep learning.

Java has a strong presence in the database management space, and many popular databases, such as MySQL, PostgreSQL, and Oracle, are written in Java.

Java offers several powerful data visualization libraries, including JFreeChart, JavaFX, and Apache ECharts.

Java has several libraries for data processing and manipulation, including Apache Commons CSV, Apache Commons Math, and Apache Commons Text.

Java offers several powerful tools for working with big data in the cloud, including Apache Hadoop, Apache Spark, and Apache Cassandra.

In summary, Java offers a powerful platform for Big Data and Data Science applications, thanks to its performance, scalability, and rich set of libraries and frameworks. Developers working with Big Data and Data Science should be proficient in Java programming language, distributed computing frameworks, database management systems, data visualization libraries, and cloud computing. They should also have a good understanding of modern data processing practices and principles to build scalable, secure, and maintainable applications.

[mmorgmediagroup]

Opensource bi tools

OPENSOURCE BI TOOLS INSTALL

OPENSOURCE BI TOOLS SOFTWARE

OPENSOURCE BI TOOLS FREE

Metabase only works well with a single SQL data source.Have your business users ever found Metabase’s “Ask a question” too limiting for complex queries, and end up coming back to the analytics team to ask for custom reports?.Yet when your analytics needs grow, you might face some of these pain points when using Metabase:

OPENSOURCE BI TOOLS INSTALL

Metabase is a good, popular open-source BI tool that anyone can quickly install on your local environment to get a simple BI system up and running.Ĭhoosing the right BI tool for your organization can be extremely tricky, so we dedicate another article on this topic, check it out here: A Walk Down Business Intelligence Space You can build powerful Business Intelligence solutions with Mondrian as your Online Analytical Processing (OLAP) engine, enabling multidimensional queries against your business data, using the powerful MDX query language.Problems with Metabase Metabase Dashboard Mondrian is an Open Source Business Analytics engine that enables organizations of any size to give business users access to their data for interactive analysis. NextReports Server is a reporting server that helps automating production and delivery of business reports. NextReports Engine is a lightweight java reporting engine that could be embedded in any application or used on their own.

OPENSOURCE BI TOOLS SOFTWARE

NextReports Designer is an AdHoc Query and Reporting software that promotes simple, fast and powerful report creation, which runs on any operating system that supports Java 1.7. NextReports provides NextReports Designer, NextReports Engine and NextReports Server. It combines static reports (based on JasperReports), a Swing pivot table for OLAP analysis, and charts (based on JFreeChart). The open source project JMagallanes is an end user application for Olap and Dynamic Reports written in Java/J2EE. Results are exportable to spreadsheet/pdf, can be reversed to a database table (cached), can be embedded in a portal-like page or retrieved via a smartphone. It supports tabular, crosstab, charts, scheduling, email alerts, drill down, among other features. SQL queries can be published in a few minutes. ART is a lightweight, multiplatform, web based query tool and reporting environment.

OPENSOURCE BI TOOLS FREE

"We're basically dumping data on a 1 minute, 5 minute, or 15 minute basis into Splunk.ĪRT stands for A Reporting Tool and is a free Open Source Business Intelligence solution. Brian Gilmore, Product Manager for Enterprise Intelligence at McKenney's, described how Splunk enables this engineering's customers (including multi-site organizations) to manage their facilities with greater efficiency. This enterprise solution allows businesses to collect, index, and search their machine data with ease. Splunk stands apart from other BI tools in its focus on embedded technology. The community edition is open source which can be downloaded from website.RapidMiner Server lets to run processes on enterprise hardware from anywhere, without restrictions. Rapidminer is the leader in open source provider for data mining and business analytics. Version 2.7 of the SpagoBI server was released last month and includes an interactive dashboard and integration with other reporting tools. The BI suite consists of some 15 components – from reporting, OLAP and data mining to charting, dashboards and ETL. Its developers proclaim SpagoBI to be the only “entirely open source” business intelligence suite. SpagoBI is part of the SpagoWorld open source initiative founded by Italian IT services company Engineering Group. JasperReports Server is optimized to share, secure, and centrally manage your Jaspersoft reports and analytic views. It provides reporting and analytics that can be embedded into a web or mobile application as well as operate as a central information hub for the enterprise by delivering mission critical information on a real-time or scheduled basis to the browser, mobile device, printer, or email inbox in a variety of file formats. JasperReports Server is a stand-alone and embeddable reporting server. The products offered via the BIRT architecture are the report designer, chart builder, and example viewer. Each engine is OSGi compatible, but can be run without OSGi if desired. It's designed primarily for Java web applications and consists of a design engine, report engine, and chart engine. With its strong workflow automation capabilities, this business intelligence (BI) suite can be a good choice for enterprises that want to build strong reporting functionality into their business processes.īusiness Intelligence and Reporting Tools (BIRT) is an open source project sponsored by Actuate. At the same time, it has some limitations that make it a better fit for IT than for the average business user. It is best known for data integration and data mining that help it support complex business analytics. Pentaho is built on Java Enterprise Edition and runs on the JBoss web server.

[draegerit]

Eine kleine Oracle Java Anwendung welche die Daten eines DHT11 Sensors in einem Diagramm anzeigt. #wemosd1mini #dht11 #oracle #java #jfreechart #maker #electronic #components #diy #tech #techy #technology #DraegerIT (hier: Stefan Draeger Software) https://www.instagram.com/p/B91xhaIo2CR/?igshid=1cenq6y9ql8qz

[trolling-blogs]

(via What is ER Modeling? Learn with Example)

ER Modeling, or Entity-Relationship Modeling, is a data modeling technique that tries to discover the entities and relationships that are reflected in the real world. It was first developed at IBM Research in the 1970s by Peter Chen. There are many ER modeling tools. The most popular one is probably ERwin, but there are also open-source ones like JFreeChart.

[vytcdc-course]

What is the future of Java?

What is the future of Java?

The global community of developers is expected to increase by 28.7 million in 2024. Also, the corporate giants like Amazon, Microsoft, Google are still using java in their software development process.

Java is an object-oriented programming language. It has several versions the latest one is Java SE 15 was released on 15 September 2020.

Who invented Java?

Mike Sheridan together with James Gosling and Patrick Naughton created Java in June 1991. Their key goal is to invent a simple, Secure, and general-purpose programming language that could be run in various platforms like mobile and desktop.

The Main Features of Java

Simplicity

Java offers an easy user experience for developers as well as the end-users. Java's inbuilt design libraries are the most advanced ones when compared to the other libraries.

Portability:

Java can be run at any time and anywhere in all the hardware and software.

Allocation:

Features like stack provisioning, garbage collection, and automatic memory differentiate java way apart from other programming languages

Distribute:

Java is highly networking component. Usually, Java receives and sends files informally

Extremely Secure:

Java enforces strong security standards with safe programming language.

Why does Java remain a popular language in the future?

Java has wider applications than most of its peer languages like Asp.Net, PHP, Python etc. Here, we have compiled a list of the most popular applications of Java.

Java applications in Big Data

Java is a dominant programming language in Big data, along with Python. It is used in various frameworks for handling large-scale data.

Apache Hadoop

Hadoop is a Java-based programming framework that processes large datasets in a distributed computing environment. Hadoop runs in master-slave architecture, where the master controls the working of the entire distributed computing stack. Hadoop has become very popular among companies in storing massive data and performing analysis.

Companies using Hadoop framework: IBM, Amazon web services, Intel, etc

Apache Spark

Spark is another popular Java framework using in Big data. Spark is basically a cluster computing framework and can run on thousands of machines (distributed clusters). Spark is used in large ETL (extract, transform, and load) operation, predictive analytics, and reporting applications

Companies using Spark framework: Uber, Slack, Hubspot, Shopifyetc

Apache Mahout

Apache Mahout is a Machine Learning library and big data tool, built using Java. It is used for performing machine learning functions like classifications, clustering, and recommendations.

Companies using Apache Mahout: Adobe, Facebook, LinkedIn, Foursquare, Twitter, and Yahoo

Java JFreechart

Jfreechart is a popular open-source big data tool, used for visualization and seeing behavior of data. It helps in making different graphs and charts like Pie charts, bar charts, line charts, scatter plots, box plots, histograms, etc.

Deeplearning4j

Deeplearning4j is a Java library used in Deep Learning. It is used to build different types of neural networks for processing data. Deeplearning4j can be integrated with Apache Spark and can run on GPUs.

Companies using Deeplearning4j: JPMorgan Chase, Wells Fargo, Interactive Brokers

Apache Storm

Apache Storm is an open-source distributed real-time computational system for processing data streams. Apache storm can process millions of jobs on a node in a fraction of a second.

Examples of the storm in real-world (Source: Apache Storm website)

Twitter: Storm powers a wide variety of Twitter systems, ranging in applications from discovery, real-time analytics, rationalization, search, revenue optimization, and many more.

Groupon: Storm powers real-time data integration systems. Storm helps in analyzing, cleaning, normalize, and resolve large amounts of non-unique data points with low latency and high throughput.

Weather: Weather Channel uses several Storm topologies to ingest and persist weather data.

Enroll in Java courses with institutes like VyTCDC.

[onclickonload]

JFreechart dark

[sciforce]

Best Libraries and Platforms for Data Visualization

Image credit: unslpash.com

In one of our previous posts we discussed data visualization and the techniques used both in regular projects and in Big Data analysis.

However, knowing the plot does not let you go beyond theoretical understanding of what toll to apply for certain data. With the abundance of techniques, the data visualization world can overwhelm the newcomer. Here we have collected some best data visualization libraries and platforms.

Data visualization libraries

Though all of the most popular languages in Data Science have built-in functions to create standard plots, building a custom plot usually requires more efforts. To address the necessity to plot versatile formats and types of data. Some of the most effective libraries for popular Data Science languages include the following:

R

The R language provides numerous opportunities for data visualization — and around 12,500 packages in the CRAN repository of R packages. This means there are packages for practically any data visualization task regardless the discipline. However, if we choose several that suit most of the task, we’d select the following:

ggplot2

ggplot2 is based on The Grammar of Graphics, a system for understanding graphics as composed of various layers that together create a complete plot. Its powerful model of graphics simplifies building complex multi-layered graphics. Besides, the flexibility it offers allows you, for example, to start building your plot with axes, then add points, then a line, a confidence interval, and so on.

ggplot2 is slower than base R and rather difficult to master, it pays huge dividends for any data scientist working in R.

Lattice

Lattice is a system of plotting inspired by Trellis graphics. It helps visualize multi-variate data, creating tiled panels of plots to compare different values or subgroups of a given variable. Lattice is built using the grid package for its underlying implementation and it inherits many grid’s features. Therefore, the logic of Lattice should feel familiar to many R users making it easier to work with.

RGL

rgl package is used to create interactive 3D plots. Like Lattice, it’s inspired by the grid package, though it’s not compatible with it. RGL features a variety of 3D shapes to choose from, lighting effects, various “materials” for the objects, as well as the ability to make an animation.

Python

The Python Package Index has libraries for practically every data visualization need, however, the most popular ones offering the broadest range of functionalities are the following:

Matplotlib

Matplotlib is the first Python data visualization and the most widely-used library for generating simple and powerful visualizations in the Python community. The library allows building a wide range of graphs from histograms to heat plots to line plots. Matplotlib is the basis for many other libraries that are designed to work in conjunction with analysis. For instance, libraries like pandas and matplotlib are “wrappers” over Matplotlib allowing access to a number of Matplotlib’s methods with less code.

Seaborn

An example of a popular library, built on top of Matplotlib, is Seaborn. Seaborn’s default styles and color palettes are much more sophisticated than Matplotlib. Beyond that, Seaborn is a higher-level library, so it is easier to generate certain kinds of plots, including heat maps, time series, and violin plots.

Bokeh

Similar to the ggplot library for R, Bokeh is based on The Grammar of Graphics. It supports streaming, and real-time data. Unlike the majority of other data visualization libraries, Bokeh can create interactive, web-ready plots, which can easily output as JSON objects, HTML documents, or interactive web applications. Bokeh has three interfaces with varying degrees of control to accommodate different types of users from users wishing to create simple charts quickly to developers and engineers who wish to define every element of the chart.

Scala

Python and R remain the leading languages for rapid data analysis, however, Scala is becoming the key language in the development of functional products that work with big data, as the latter need stability, flexibility, high speed, scalability, etc

Vegas

Probably, the most functional Scala library for data visualization, Vegas allows plotting specifications such as filtering, transformations, and aggregations. It is similar in structure to Python’s Bokeh and Plotly. Vegas provides declarative visualization, so that the user can focus on specifying what needs to be done with the data, without having to worry about the code implementation.

Breeze-viz

Breeze-viz is based on the prominent Java charting library JFreeChart and has a MATLAB-like syntax. Although Breeze-viz has much fewer opportunities than MATLAB, matplotlib in Python, or R, it is still quite helpful in the process of developing and establishing new models.

Javascript

Javascript may not be among languages adopted for Data Science, but it offers vast opportunities for data visualization, and many libraries for other languages are actually wrappers for JS packages.

D3.js

D3 is called the mother of all visualization libraries, since it is the basis for many libraries. Being the oldest library, it remains the most popular and extensive Javascript data visualization library. It uses web standards and is framework agnostic, working smoothly with any Javascript framework. D3 is built for manipulating documents based on data and bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the capabilities of modern browsers without coupling to a proprietary framework, combining visualization components and a data-driven approach to DOM manipulation.

Chart.js

Chart.js is a lightweight library that has fully responsive charts including Bar, Bubble, Doughnut, Line, PolarArea, Radar, Scatter. This is an open-source library based on HTML5. V.2 provides mixed chart-types, new chart axis types, and beautiful animations. Designs are simple and elegant with 8 basic chart types, and you can combine the library with moment.js for time axis.

Online platforms for Data Visualization

At a certain time, especially at the beginning of a project, it is important to generate a lot of charts quickly, in order to explore the scope, depth and the texture of the data and find interesting stories to develop further. There are quite a lot of online platforms to generate data visualization.

Plotly

Plotly as an online platform for data visualization, that is, among all can be accessed from an R or Python Notebook. Plotly is an advanced, online data visualization program with a colorful design. Its forte is making interactive plots, but it offers some charts you won’t find in most packages, like contour plots, candlestick charts, and 3D charts. You can use the chart studio to create web-based reporting templates. You can also modify your own dashboards and interactive graphs for your collaborators to comprehend better.

Tableau

Tableau is a business intelligence system that has a new approach to data analysis. Tableau lets you create charts, graphs, maps and many other graphics. A big advantage of Tableau is availability of several versions: desktop, server, and a cloud one. You can create and publish dashboards, share them with colleagues, and analyze using different methods. We recommend it because of its simplified drag-and-drop system, all day technical support, and flexible package fees.

Of course, it is just a small fraction of all platforms, tools and libraries available for you to visualize your data in the most effective and transparent way. The data itself, as well as the project goals — be it scientific analysis, business intelligence or creating a website that should incorporate some charts — will prompt you the approach, or, most usually a combination of approaches from quick online plotting to base functions and specialized packages.

[davidnick321-blog]

1. Introduction As we saw in another tutorial it is very easy to export data to a spreadsheet using Apache POI . However, spreadsheets are not the only solution for data export. In certain cases it can be very interesting to translate all this information into a graph. In this tutorial we will see how to generate graphs with …

[holytheoristtastemaker]

Working with BDA in Java relies on a number of tools. Most of these are open source, and when used together they form a BDA stack that provides a powerful level of functionality. This article examines some of the top tools.

If you think that Big Data Analytics (BDA) is a buzzword, think again. Almost every industry is now using Big Data, from healthcare providers to finance. Big Data is now having a huge impact on mobile app development, and leaders in the field are exploring how to use AI for big data analysis.

All this said, working with BDA in Java has been somewhat overlooked. This is strange because many of the tools used by developers working with BDA are written natively in Java. In our guide to big data, in a nutshell, we looked at some high-level tools for BDA, but in this article, we’ll take a more fundamental approach, and give you some tools to work with Big Data directly from Java.

Big Data Analytics in Java

Many developers working with BDA don’t touch Java. That’s a shame because Java offers a number of advantages for working with Big Data. Foremost among these is that the Java runtime is inherently portable, and so can be run anywhere, and on any hardware or software platform. The stack provisioning that Java offers, and particularly its garbage collection and automatic memory distribution also make it a natural choice for working with BDA.

Java can be used to undertake BDA for (almost) any complex data acquisition system, but the most common application is to analyze data from eCommerce stores. Used in conjunction with high-performance web hosting providers, developers can segment and analyze visits and sales to an unparalleled level of granularity. These data can then be used to underpin highly targeted marketing strategies, and to make predictions on future sales.

Alongside this type of deployment, many developers also find that using Java for BDA can be used to improve the security of their systems. One of the major lessons to be drawn from the high profile data breaches of the past few years is that the sheer amount of data that the average developer now has access to – and responsibility for – means that it is difficult to retain oversight on it.

This is particularly true during scaling processes. Many businesses, after reaching the limit of what they can achieve through their current data infrastructure and eCommerce store, will migrate to an interconnected group of systems that handle eCommerce, website analytics, and marketing statistics independently.

For most businesses, given the popularity of WordPress, the first instance of this will be when they migrate their WordPress site, but any migration process of this kind can lead to an explosion of data for developers to deal with. By working with Big Data at a more fundamental level, through Java, it is easier to keep control of the data generated by acquisition systems.

Big Data Tools for Java

Working with BDA in Java relies on a number of tools. Most of these are open source, and when used together they form a BDA stack that provides a powerful level of functionality. Here are the most commonly used tools.

Hadoop

Most developers looking to implement BDA in Java will start with Hadoop. This tool has been built (and made freely available) by the Apache Software Foundation and offers a Java-based programming framework for working with Big Data across a distributed computing environment.

Because of this, the tool has become extremely popular for organizations who want to store huge amounts of data on one system, and perform analysis on another. In addition, Hadoop offers a whole ecosystem of tools for working with Big Data through Java: everything from machine learning systems to advanced search functionality:

Source: https://www.whizlabs.com/blog/big-data-tools-for-java-developers/

Apache Spark

Apache Spark is similar to the MapReduce component that is found in Hadoop but is becoming more popular than it’s rival due to improved performance and resilience. Spark makes use of an RDD (Resilient Distributed Dataset) that not only makes working with Big Data more efficient but can also improve cybersecurity.

The language that underpins Spark is Scala, which itself is based on Java. Because of this, Spark offers an extensive Java API and is easy to work with for Java developers. And just like Hadoop, in recent years Spark has expanded to offer a complete ecosystem of tools for working with Big Data.

Apache Mahout

Apache Mahout is a more narrowly focused tool for working with Big Data. It provides a machine learning framework that can be used for recommendations, clustering, and classification. It runs on Hadoop, and can, therefore, be integrated easily into distributed environments.

Java Jfreechart

Jfreechart is focused on a different part of the BDA workflow. It provides data visualization tools, written natively in Java, that can be used to generate a wide range of charts and plots from your data. When used in conjunction with data analysis tools like Hadoop and Apache Spark, Jfreechart can be configured to automate the process of visualization and produce dashboards that will quickly show you key trends in your data.

Deeplearning4j

Deeplearning4j is a Java library that is used to design neural networks for use in BDA. It can be integrated with Apache Spark or Hadoop and can be easily scaled on either. It can run on distributed networks, and can even be configured to run on GPUs, making the best use of the computing resources available in the average organization.

Apache Storm

Apache Storm is an alternative to Apache Spark. At first glance, both systems appear to function in a similar way. However, Storm is focused on providing true streaming functionality through Java; whilst Spark appears to offer this, in reality, Spark acts as a wrapper around batch processes. Because it is based on a true streaming model, Storm has become popular for online systems that require lightning-fast data analysis.

Going Further

As a Java developer, it makes sense to use your skills when working in BDA. Using Java APIs in your Big Data systems gives you more control over them than using high-level tools, which is important for both system resilience and cybersecurity. The tools we’ve shown you above are all based on Java, and so can be picked up quickly by anyone with experience in the language.

If you want to go further, though, you should read our guide on leveraging big data, where we explain just how much you can get out of BDA systems.

[omaccounting]

JAVA Developer (Delhi-NCR)

#OMaccounting.in NEX G Exuberant Solutions – Date posted: 06 Oct 2017 Develop, test software based on :- – Java / J2EE / JEE – HTML 4/5, Spring, JQuery, Ajax – JPA / Hibernate, Oracle – WebServices – AWT, JFreeCharts, High Charts Maven, SVN, Eclipse / STS … #OMaccounting.in #JobsinJaipur #JobsinAjmer #JobsinJodhpur #JobsinUdaipur #JobsinKota #JobsinBikaner #JobsinAlwar #JobsinRajasthan…

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JAVA Developer (Delhi-NCR)

#OMaccounting.in NEX G Exuberant Solutions – Date posted: 06 Oct 2017Develop, test software based on :- – Java / J2EE / JEE – HTML 4/5, Spring, JQuery, Ajax – JPA / Hibernate, Oracle – WebServices – AWT, JFreeCharts, High Charts Maven, SVN, Eclipse / STS … #OMaccounting.in #JobsinJaipur #JobsinAjmer #JobsinJodhpur #JobsinUdaipur #JobsinKota #JobsinBikaner #JobsinAlwar #JobsinRajasthan…

[adipatel92]

In this JFreeChart tutorial, we will go through JFreeChart download and 3 JFreeChart examples (JFreeChart pie chart, JFreeChart bar chart and JFreeChart timeseries chart).

[jerzyseweryn-blog]

Using JFreeChart with Seam

Graphic statistics in Seam with JFreeChart. [code lang="java"] package example.chartprocesser; @Name("chartprocesser") public class ChartProcesserBean { @In EntityManager em; // Entity Manager to access the database byte[] chart; // chart image (.png) as a byte array @Factory(value="chart") public void createChart() { DefaultCategoryDataset ds = this.getData; JFreeChart chart = ChartFactory.createLineChart( "TITLE", "Category Label", "Axis Lable", ds, PlotOrientation.HORIZONTAL, false, false, false ); try{ this.chart = ChartUtilities.encodeAsPNG(chart.createBufferedImage(400, 400)); } catch (IOException e){ e.printStackTrace(); } } private DefaultCategoryDataset getData(){ //get the data and put into DefaultCategoryDataset //Then return it. } } [/code] In your JSF page: [code lang="xml"] [/code]