How Big Data Benefits From the Right Java Approach
Java often becomes a primary option for creating big data projects, as it is a proven tool for developing data infrastructures, and works well with a wide stack of technologies. Another compelling reason is that most of today’s commonly used data means are based on Java, which makes it an excellent fit for the purpose.
Java is a broadly used and mature engineering language, known for its scalability, superior safety, object-oriented modeling, and extensive variety of frameworks. Such combination explains why Java is often a top choice for different analytical purposes and for the building of data infrastructures. This language has C-syntax, which is the closest one to mathematical algorithms, and well-known for many engineers. Java covers a meaningful part of big data projects and is used for engineering with technologies such as the Internet of Things (IoT), AI-driven logic, and machine learning.
Java development for big data
Its versatility and universality make Java development suitable for nearly any data analytics system. It is broadly utilized for programming in applications like Apache Hadoop, Camel, and Kafka, which are developed specifically for gathering, processing, and analyzing large information volumes.
Big data startups can be certain: Java is a reliable choice due to its longstanding presence and close adherence to the construction of data solutions. Java has a community of over 9 million users and is also highly supported by communities like Stack Overflow or GitHub.
Also, Java is universal in terms of interoperability with various devices and programs, so there is no wonder that the Java engineering language is one of the prime choices for building big data analysis and solutions.
Why choose Java development for big data, AI & ML projects?
If you are planning to create a big data project or a project utilizing artificial intelligence or machine learning (which certainly also includes data collecting and analytics), Java should be your top choice. Most of today’s big data technologies like Apache Hadoop and Storm are composed in Java. These open-source environments are becoming the central infrastructures for data science projects for many companies.
Advantages of Java for big data engineering
Type-safety and reliability
In environments with huge amounts of different types of data, there is no room for type errors. Java is a type-safe language that prevents data misconfigurations and ensures operations are performed with the right type of data. So, it is the initial choice for most developers operating with data-enabled projects. For data scientists, the security level plays a significant role, as it defines the quality of collecting and analyzing the data, primarily when they work with extensive libraries. By using Java, you save time by excluding the necessity of extra testing.
Reusable code
Java code is reusable, which allows for the creation of multi-platform projects and the ability to conduct transfers smoothly. Thanks to the Java code database it is easy to integrate diverse data analysis methods.
Cloud environment
Cloud is another big trend in 2021, as many companies are migrating their data to cloud infrastructures, creating a surge in demand for associated solutions. As big data projects are inevitably connected to the cloud, it is critical to acquire all the possible tools for working with cloud-native environments. Applications written in Java seamlessly integrate with cloud services such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform, which makes it a great choice for big data products.
Best Java-based tools for big data projects
Java is well-known for its capability to be used as a robust and scalable tool in developing big data applications. It has many built-in tools for big data development, including Weka, Deeplearning4j, Java-ML, and MLib. Some of the most commonly used and notable big data processing tools like Apache Spark, Apache Hadoop, Apache Camel, Apache Beam, and Kafka are super compatible with Java. Tools like MapReduce, Storm, Apache Beam, and Scala are just a few pieces of the Java Virtual Machine ecosystem.
Here is a basic tech stack of tools for big data project development with Java:
Apache Hadoop ecosystem
Apache Spark
Storm
Mahout
Deeplearning4j
SQL/NoSQL databases
MapReduce Java code
Kubernetes constructs that are used to build Big Data CI/CD pipelines
RedShift
Hive
Athena for requesting data
Java big data development with Erbis
Erbis has extensive experience in producing big data projects with a Java development stack. Our experts have built advanced and sophisticated solutions in the supply chain, healthcare, and retail industries. Erbis’ Java development team can build your project from scratch or enhance and integrate its current technology fast and smoothly.
