Java Consulting

  • Java is a cross-platform, development framework open-sourced by Oracle. A survey conducted by the TIOBE Index in 2021 ranked Java as the second most popular programming language in the world, with a market share of 17.2% According to the 2021 Stack Overflow Developer Survey, Java is the third most popular programming language, with a popularity rating of 28.3%.
  • Free and paid: The open-source version of the JDK, known as the OpenJDK, is free to use in production environments. However, organizations that wish to use the commercially licensed version of the JDK, known as the Oracle JDK, in production must pay a fee.
  • Platform agnostic: Java was designed following the 'Write Once Run Anywhere' principle, which allows writing code once and running it on any platform.
  • Top clients: At Cazton, we help Fortune 500, large, mid-size and startup companies with Java development, deployment, consulting, recruiting services and hands-on training services. Our clients include Microsoft, Broadcom, Thomson Reuters, Bank of America, Macquarie, Dell and many others.

Introduction

  • Millions of developers run more than 60 billion Java Virtual Machines worldwide.
  • 50% of Fortune 100 companies are on Oracle Java SE Subscription.
  • Over 3 billion phones run on a platform built using Java.

Java indeed is one of the most popular object-oriented programming languages to build high-performance, high-scale applications on cloud, web, desktop, mobile, gaming, IoT and AI platforms. Existing for almost 30 years now, Java has one of the oldest running programming language communities in the world. Not to mention that the number of libraries and frameworks available in its ecosystem has many advantages. Java continues to be the development platform of choice for enterprises and developers.

At Cazton, our experts have been using Java for different facets of application development. We help Fortune 500, large and mid-size companies with Java development, deployment, consulting, recruiting services and hands-on training services. With an expert-led team like ours, we bring the benefit of our network to you. We save our clients a serious amount of effort on tasks they shouldn't be doing by providing them with a streamlined strategy and improvising it as we gather more information. We learn what works and what doesn't work and incorporate that in our feedback to our clients to make sure their projects are successful.

Features

  • Big data: With the introduction of mobile and IoT devices, data consumption has been ever-increasing. Enterprises need to not only manage and analyze data, but also make predictions and real-time decisions. To stay competitive, companies need to understand the latest market trends, customer preferences, and other requirements, thus making understanding large amounts of data imperative. When it comes to big data, Java has been the backbone for many big data frameworks, as major core modules of popular big data tools are written in Java.
  • Distributed: Java supports distributed computing as it enables us to develop complex applications that make remote procedure calls, access remote databases, and collaborate with multiple nodes in a data center to increase throughput and/or reduce latency of selected applications. A distributed object-based system is a collection of objects that isolates clients from servers by a well-defined encapsulating interface. We can perform distributed operations using different frameworks like Remote Method Invocation (RMI), Common Object Request Broker Architecture (CORBA), Message Passing Interface (MPI), Spark in-memory Map-Reduce, and many others.
  • Multithreading: Multithreading is a technique in which a process is divided into multiple subprocesses, each of which can perform different tasks at the same time in parallel. Each subprocess in Java is called a thread. By writing multithreaded applications, the CPU can switch between threads to execute the processes simultaneously. Multithreaded applications share a common memory area and reduce memory occupancy for each thread.
  • Platform agnostic: Java, being a higher-level programming language, is clearly less complex compared to C and C++. Its syntax is elementary, understandable, and easy to maintain. Many concepts like storage classes, operator overloading, and pointers have either been removed or simplified in Java. Its garbage collector cleans up unreferenced memory periodically, thus allowing developers to focus on application development rather than memory management. This is quite a productivity hack. Java also offers a lot of APIs, enabling developers to build complex applications.
  • Wide application: Java, being a very robust, simple, secure, and high-performance programming language, makes it a great option for building a variety of applications, including embedded systems, desktop GUIs, mobile, web, gaming, business, and cloud-based applications. It is also famous in the world of big data, artificial intelligence, and useful for building scientific applications.
  • Object oriented: Everything in Java is an object with data and behavior. Due to its object model, it can be easily extended by using object-oriented programming concepts like inheritance, encapsulation, abstraction, and polymorphism.

Machine Learning and Java

The major advantages of using JVM for machine learning are its high compatibility, high performance, scalability, and the rich ecosystem of libraries, frameworks, and development tools. Most big data and machine learning tools support JVM, either being written in Java or Scala, or at least making sure they officially support Java.

There are several popular machine learning frameworks that support JVM (Java and Scala), including:

  • Apache Spark: Apache Spark, written in Scala, is a widely used distributed computing platform that is designed for big data processing. It includes a machine learning library known as MLlib, which offers a range of machine learning algorithms, including classification, regression, decision trees, random forests, and gradient-boosted trees, recommendation, clustering, topic modeling, frequent itemsets, association rules, and sequential pattern mining.

    MLlib contains high-quality algorithms that leverage iteration and can yield better results than the one-pass approximations sometimes used on MapReduce. Therefore, MLlib is 100 times faster than MapReduce. Databricks is built on top of Apache Spark, and Data lakehouse redesigns data warehouses for the modern world.

    Machine learning pipeline
  • Deeplearning4j: Deeplearning4j is an open-source deep learning library that is written in Java and designed to be used in business environments. It supports the development of machine learning models using artificial neural networks and can be integrated with other Java libraries and frameworks.
  • Apache Mahout: Apache Mahout is an open-source machine learning library that is designed to be scalable and easy to use. It was originally written in Java, but it has since been largely rewritten in Scala. It includes a wide range of machine learning algorithms and tools for collaborative filtering, clustering, and classification.
  • Flink: Apache Flink is an open-source stream processing framework written in Scala and designed for real-time data processing. FlinkML is a machine learning library that supports supervised and unsupervised learning, data preprocessing, outlier selection and recommendation engines.
  • scikit-learn: scikit-learn is an open-source machine learning library written in Python but supports Scala via a wrapper library called "ScalaLearn". It offers a wide range of machine learning algorithms and tools for classification, regression, clustering, dimensionality reduction, model selection and preprocessing.
  • H2O: H2O is an open-source machine learning platform written in Scala and used for big data processing and real-time scoring.

Big Data and Java

Data has become more valuable than oil. Now, more than ever, big data analysis is necessary for a company to excel and grow. Big data analytics helps you innovate and deliver new products and services based on market trends and customer requirements, which can improve business performance. When it comes to big data, Java has been the backbone for many big data frameworks as major core modules of popular big data tools are written in Java.

Big data tools like Hadoop, Apache Spark, Apache Kafka, Apache Storm, Apache Mahout, Apache Ignite, and others are written in Java or Scala, which means they can run on the JVM and get the benefits of both languages, Java being object-oriented and Scala being functional.

Another way Java can be used for big data is using NoSQL databases, which are designed to handle large amounts of unstructured data. Most NoSQL databases can be used with Java, but some are written in Java, such as Cassandra, HBase, Elasticsearch, Solr, Hive, and Neo4j. These databases allow for the storage and retrieval of large amounts of data in a distributed fashion, making them well-suited for big data applications.

Microservices and Java

In the recent years, microservices architecture and cloud native architectures have become very popular. Cloud native architecture refers to the design of applications and infrastructure that are built specifically to take advantage of the benefits provided by cloud computing platforms. These benefits include the ability to scale resources up or down as needed, pay only for what is used, and take advantage of the various services and APIs provided by the cloud provider. A cloud native architecture typically includes a number of components, such as: microservices, a containerization platform, automation using CI/CD tools, monitoring, logging, telemetry, and resilience.

Microservices architecture is the natural progression from SOA towards a more well-defined architecture that is meant for scale and is lightweight. Microservices based architecture enable developers who work on different technologies like Java, Python, .NET, Node.js etc. to be able to collaborate effectively via REST, Graph QL or gRPC based APIs. It also allows use of multiple different operating systems. For example, the front-end developers can use a Mac, while the backend developers may use a Linux based machine and the API may be built on a Windows based machine. With application being containerized, this is not just doable, it's seamless.

At the same time, deployments can be a nightmare, depending on the size of the system. We had clients using monolithic systems that had to have multiple teams planned for days for a deployment, which took hours. This meant reducing the ability to deploy only three to five times a year. With the microservices architecture, the same system could be deployed faster, and partial updates to the system could happen as frequently as multiple times a day.

Consuming Independent Microservice

In a microservices-based architecture, applications are built as a collection of small, independent services that can be developed, deployed, and scaled independently. Microservices represent an architecture in which each microservice performs a specific task and is isolated and independent. This architecture uses lightweight messaging frameworks to share output between microservices. In the Java world, there are several frameworks available that make developing microservices easy. A containerization platform such as Docker allows you to package your microservices and their dependencies into self-contained units that can be easily deployed and scaled to a cloud platform like Google Kubernetes Engine (GKE), Azure Kubernetes Service (AKS), OpenShift, Cloud Foundry, Heroku, and Amazon Elastic Container Service for Kubernetes (EKS).

There are several microservice frameworks available for Java, including Spring Boot, Quarkus, Micronaut, and Eclipse Vert.X. These frameworks provide a range of features and tools for building microservices, such as service discovery, fault tolerance, metrics, and support for REST APIs. The top microservices frameworks have been discussed below:

  • Spring Boot: is a Java-based framework that makes it easier to create stand-alone, production-grade applications that are microservices based, reactive, event drive, cloud native, serverless, web-based and/or use batch processing. It includes a set of default configurations and dependencies that are commonly used in the development of Spring (framework) applications. Simplicity is one of the main benefits of Spring Boot as it simplifies the process of creating and deploying applications. The starter template includes all the necessary dependencies and configurations thereby improving productivity.

  • Quarkus: is a Java framework designed to make it easier to develop lightweight, high-performance cloud-native applications and supports Docker and Kubernetes. It is based on the MicroProfile framework and focuses on efficiently running Java applications in containers.

    One of the key features of Quarkus is its ability to compile Java code into native executables, which can provide significantly better performance compared to traditional Java applications that are run on a Java Virtual Machine (JVM). This is achieved using GraalVM, a high-performance virtual machine developed by Oracle that can execute Java, JavaScript, and other languages. However, despite the emphasis on low memory usage, in some cases it may use more memory than JVM based frameworks.

  • Micronaut: is a Java framework for building lightweight, JVM-based, full-stack framework for building modular, easily testable microservice and serverless and cloud-native applications with support for Docker and Kubernetes. It is designed to be efficient, fast, and scalable, with a focus on minimizing startup time and memory usage.

    One of the key features of Micronaut is its support for the creation of microservices, which are small, independent applications that can be easily deployed and scaled. Micronaut makes it easy to build these microservices by providing a range of tools and features, such as support for dependency injection, configuration, and monitoring.

  • Eclipse Vert.x:is a Java framework for building reactive, distributed, and high-concurrency applications. It is designed to be lightweight and efficient, with a focus on providing an event-driven programming model that allows developers to build applications that can scale to handle large amounts of concurrent requests.

    Vert.x makes it easy to build these microservices by providing a range of tools and features, such as support for dependency injection, configuration, and monitoring. Vert.x also includes several other features that make it suitable for building high-concurrency applications, such as support for reactive programming, non-blocking I/O, and distributed event-bus communication.

Java and Scala

Scala and Java are a great combination for software development because they offer several benefits when used together. Scala combines elements of object-oriented programming and functional programming, making it an ideal choice for projects that require concurrency, functional programming, or scalable and high-performance distributed systems.

  • Interoperability (via JVM): As mentioned above, both are Java Virtual Machine (JVM)-based. That means Scala is designed to be fully interoperable with Java, meaning that Scala code can be called Java code and vice versa.
  • High performance: Scala is known for its high performance, which can be beneficial when building complex software systems. It is also designed to support concurrent or multi-threaded programming (form of programming that involves writing code that can be executed concurrently, or simultaneously, by multiple processors or cores), which is essential for building scalable and efficient software systems.
  • Extensive ecosystem: Both Scala and Java have large and active developer communities, which means that there are many libraries, frameworks, and tools available for both languages. This makes it easier for developers to build and deploy software systems using a combination of Scala and Java. High performance frameworks like Apache Spark, Akka and Play are built on Scala.

Overall, Scala and Java are a great combination for software development because they offer a powerful and flexible set of tools and technologies for building scalable, high-performance software systems. Scala is also heavily used for machine learning.

How can Cazton help you with Java Consulting?

At Cazton, we help Fortune 500, large and mid-size companies with Java development, consulting, recruiting services and hands-on training services. Our team includes Java certified experts, Java architects, consultants, developers as well as experts in other notable open-source technologies. We have significant experience in Java technology stack, and our expertise can enhance your project's performance and improve its efficiency. We believe in customizing each solution to tailor to the exact needs of the client. We can provide you the following services: