Java BigData Development

2 min read
Big Data Java

With Java Big Data development business performance can be improved significantly

Big data stands for huge amounts of data, both structured and unstructured. These data sets may come from various sources – meteorological devices, medical equipment, audio and video recording devices, etc.

Data may also arrive in different formats. Therefore, it becomes a great challenge to store it, to process it, and – the most important – to gain useful insights from mining this data. An attitude of any Java developer to Big Data, as a rule, reflects the following: this phenomenon provides great opportunities in a variety of spheres. To name a few examples:

  • Big Data in manufacturing can be used in predictive solutions aimed to reduce production costs;
  • With the increased use of electronic devices collecting data about patients’ health, Big Data is actively applied in medicine;
  • Big Data in marketing provides companies great opportunities to understand the audience better and tailor their commercial offers to each customer individually;
  • Scientists process large data sets gained from real-time sensors to conduct their research.

When working with huge data sets, many programmers use Java technology and develop Big Data Java solutions. Why? Java is cross-platform, reliable, and it provides rich monitoring capabilities. Compiled languages, such as C++, lack these characteristics. In addition, there is support for many libraries and frameworks that are useful in building Java Big Data services. In particular, software developers often apply Big Data tools in Java, such as Hadoop – an open-source framework. Apache Spark written in Java, Scala, Python and R is another open-source tool which is often used while building Big Data solutions. So, Java with Big Data complement each other.

In Java Big Data projects, it is important to choose an appropriate database. In most cases these are NoSQL databases. Why is that? Because they are scalable, highly available, performance, and not expensive. Cassandra DB meets these characteristics to the fullest. You can read more about Cassandra usage in Big Data systems here.

What challenges is Big Data usually associated with? Taking our experience into account, we can name the three major issues that one should be aware of:

  • How to store data?
  • How to process data?
  • How to show data to users?

As to the first issue, a data warehouse should be designed. It serves to accumulating data. A data warehouse can have its specifics - for instance, it can exist in a file system, it can consist of NoSQL databases, etc. Working with all these specifics requires deep knowledge and extensive experience, both of which ISS Art team possesses.

Once data is accumulated in a warehouse, the next step is to process it in a proper way. How can this be achieved?

There are two data processing types:

  • Batch
  • Online (real-time).

Batch data processing assumes that large data sets are processed on some regular basis - say, once a day, once a week, once a month, etc. No user interaction is normally required when batch processing is carried out. Data is collected, grouped into sets (or batches), and processed after that. A monthly bill for purchases is an example of batch processing application. Such systems consume less resources than real-time processing ones do. By the way, Hadoop – one of Java Big data technologies – works with batch processing.

As for online (or real-time) data processing, all the required operations are performed here and now. Being an interactive process, it involves user communication with the system. This kind of data processing is required in systems where prompt (or even immediate) response is needed and no delay is acceptable. Radars and move detection systems are just a couple of examples.

What else is it important to know about processing data effectively? One of the possible obstacles is that the input information can be located on different servers. Thanks to the latest technologies we have, there are certain ways to work with such data. Stream data processing is one of them. When implementing this method, we continuously process data and save it.

What benefits does streaming processing provide to developers?

  • Huge data volumes can be handled promptly – almost "on a fly", without a delay;
  • Data streams from internal sources can be complemented by data streams from external sources, and, therefore, useful insights can be obtained from all these pieces of information after they are processed;
  • Considerable increase in data amount and its complexity level can be handled easily.

To get the most of these opportunities, various streaming engines for handling Big Data in Java solutions can be utilized. To name a few of them: Apache Storm and Apache Spark frameworks, IBM InfoSphere Streams, TIBCO StreamBase.

These stream processing tools can be integrated with a data source directly. Beside this, they are capable of filling a data warehouse with the most up-to-date information – on their own, or with the help of special connectors.

What can be the focus of these streaming procedures? In other words, what are the possible desired outputs from carrying out these activities? The examples include the following:

  • Various reports for business need to be generated;
  • Data mining is required to get certain insights;
  • Data classification is needed.

After data is processed, the next stage is to show it to users. The biggest challenge here is that data is often needed by a person in real time. How to ensure that a user receives the desired information here and now, without any delay? There are several ways to make this happen.

One of the options is to perform the required calculations and save the results beforehand. These results will serve as fragments for a requested report further. Then, upon user request a system can generate a complex report based on the fragments prepared earlier.

Another way to provide information in real time assumes storing data in a NoSQL database. A user enters a special key to find the necessary data. This key is associated with the specific piece of information within the database. This approach is also known as a key-value store.

One more variant of giving users real-time access to information is to provide them with ready UI libraries. By using them, users can retrieve the specific pieces of data themselves.

It's not a secret that complex information is perceived much better when it is put in visual context. That is, graphs, diagrams, heat maps and different kinds of info graphics make the presented data way more descriptive. This is where data visualization systems can help, and many developers apply them when performing Big Data visualization Java project tasks.

Today Tableau and Qlik are the leading providers of such solutions, but there are many others as well. These visualization tools are capable of connecting to data sources, taking the data required and generating the suitable type of graphics. Which solution to choose depends on specific project tasks. In some cases, the basic functionality is enough; in other cases, rich visualization functionality is needed.

Data visualization systems are of much help when Big Data analysis is performed. Big Data analytics using Java (as well as other programming languages) aims at getting valuable conclusions based on research of large data volumes. These can be the following kinds of insights obtained:

  • Important trends to be taken into account;
  • Discovered correlation between different factors;
  • Opportunities for increasing revenue and/or cutting expenses.

Of course, it is great when the analysis findings are accompanied by visual content. The information obtained from Big Data analysis in Java project can be of much help for business owner in making a competent management decision.

Here at ISS Art we know how to deliver Big Data Java solutions. For one of our Clients we developed an application to store 3D models. The project objective was to reduce possible construction expenses. This application provides users with an opportunity to model data about an object together with positional data for 3D visualization of a construction area or an object. Data sets received from various sources are transformed into an appropriate format, so that a user can make a competent decision based on the insights provided. This project implemented with Java Big Data technologies helped reduce labor costs and improve business performance.

Another project where we rendered our Java Big Data services is a prognostic solution for manufacturing field. The project is aimed at industrial equipment malfunctions prevention. As a result of large data sets being processed, the application can forecast equipment malfunctions. Here Big Data technologies in Java development helped to deliver a product that gives an opportunity to optimize Client's production processes significantly. In addition, profit is generated from selling this product as a SaaS solution.

As we can see, Big Data becomes increasingly applicable in many spheres. It provides great opportunities to get powerful insights about the processes. IT gurus all over the world willingly apply Java to Big Data projects and get the most of the opportunities this language provides.

We are happy to offer our Java Big Data services that will certainly help you achieve your ambitions goals and significantly optimize your business performance.