Big Data Development and Its Value to the World
The big data development sphere is projected to see huge growth by 2027. We discuss how big data development works and the real-world benefits it yields.
Join the DZone community and get the full member experience.Join For Free
Big data has become an integral part of many life spheres of the world and continues to emerge from its borders. Regardless of the initial doubts and mistrust towards the term, big data has established itself as a stable development direction. According to recent research, the big data market will be worth $109 billion by 2027.
With such growth, the big data development sphere will see more innovations, talents, and variety. Thus, to stay on the top of the game, developers need to be aware of the chief directions of big data development and the exact application areas. Here, we will review:
- The main fast-growing big data development areas
- How the existing technologies work
- The application area of big data development in business and healthcare
Major Domains of Big Data Development
Safety is a crucial factor of big data development, as long as the sensitive user’s data is a focus point of hackers (NIST, CWE, WASC). For that reason, big data applications should comply with several privacy laws and security standards.
To protect information on its way to the database and store it safely, developers use the following practices.
Data encryption is able to protect the data from the sides of the database, application, and during file transition.
Several encryption methods can assist you in ensuring database security. First of all, it's file encryption on a system level. As the name hints, this type encrypts data on file and folder levels. It is also called “data at rest” encryption for its effectiveness with data storage. Among other DB-side encryption types are column-level and transparent data encryption (TDE), which encrypts the whole database.
Data Transition Security
For better data transportation security, developers use SSL (Secure Socket Layer) and TLS (Transportation Level Security) protocols. Additionally, a system can attain the SSL/TLS certificates from the authorities. These certificates can serve as proof of service legitimacy and ownership docs.
APIs include a variety of app data protection mechanisms like app misuse prevention and hacker defense. However, APIs perform their security function better when the app data is properly encrypted.
Static code analyzers check the bugs in the system before it is run. The core of the analysis lies in the code matching with the predefined set of rules. If the code meets all the requirements, the system starts. If not, you get an error report. The report allows you to spot the weaknesses and address them.
Apart from the security errors, static code analyzers can find data leaks, prevent concurrency issues, and prioritize and keep track of technical debt.
Currently, there are numerous static code analysis tools. Among them are Apache Yetus, Code DX, CodeScene, Axivion Bauhaus Suite, and Coverity.
Real-Time Data Analysis
Real-time data or RTD is a new big data development paradigm. It is the process of making valuable insights out of the data as soon as it gets into a database. To provide the insights, real-time analytics systems measure and manage the incoming data. At the output, it provides users with up-to-the-moment information that helps them respond immediately.
How Does It Work?
First, a real-time analytics system pulls the data that falls into a so-called streaming place. The system can manage the process without a streaming place, but it makes the analysis slightly intermittent. After the data is received, the real-time analytics in software evaluates and distributes it across the data threads that lead the insights to a visualization engine. As a rule, the whole analysis cycle happens within a minute.
For the real-time analysis, a system should include the following elements:
- Aggregator: Performs pulling of the raw data into the system from multiple sources
- Analytics engine: The engine defines, evaluates, and sends the processed data further down to the broker
- Data lakes: Aggregate the qualified data for the later analysis
- Data broker: Provides availability for other software components
- Stream processor: Receives and sends data, analyzes it, and maintains the processing logic
Data Management Automation
Data management automation is essential for large databases that process plenty of data daily. Many software engineers put lots of effort into finding new ways to automate data handling or upgrade the existing ones. At the moment, most of them automate data management using artificial intelligence (AI).
It is difficult to overestimate the impact of AI on the data management sphere. The RPA (Robotic Process Automation) and machine learning subsets of AI streamline data processing in several ways:
- Programmed bots can enter the data automatically in the system
- The machine learning aspect helps to avoid data repetitions
- Scans the database, finds the missing data, and fills the blank spaces
- Consolidates the related data into one block that makes storage more efficient
- RPA and machine learning also participates in data analytics
The last but not least domain in our list is data visualization. Data visualization is a process of making real-time data, insights, and statistics visible on the UI in the form of charts, diagrams, graphics, etc. It helps the end user to understand the information better. Besides, you can interact with the system through your interface to point out unnecessary info. Thus, AI will learn what information might not be interesting for you and won’t collect it next time.
How Do You Visualize Big Data?
There are a great number of tools to create various types of visualizations. The choice mostly depends on the type of business or project you have and the software you use.
Free and open-source programming language. Python includes a few visualization libraries:
- Matplotlib: It’s a multiplatform library designed to work with the broad SkiPy stack
- Seaborn: Static code visualization library based on Matplotlib
- Ggplot: An open-source visualization package that works best for plotting
- Bokeh: Allows creating zoom and interactive elements
- R: R is a software environment with the programming language that is used to display statistics and graphics. It includes the ggplot package to break up graphs into layers and scales
At the end of the process, one of these visualization tools renders the information in the user interface. The rendering happens on digital dashboards that are designed for a particular business sphere. It can be a customer service dashboard, business manager console, or fleet maintenance dashboard like in the picture below.
Real-World Benefits of Big Data Application Development
As for the application area, those big data technologies can make a difference in multiple fields of human life. So let’s boil down to the areas big data development affects the most.
Medical data demands the highest level of security. You don’t want your health issues to be an open secret. Thus, big data development security methods are essential for providing medical confidentiality.
Moreover, big data development in healthcare can improve real-time visualization for a patient's state tracking during complex operations. Big data technologies are sure to positively impact the world's medicine as they develop and grow. Who knows, maybe one day a surgeon making life-saving decisions will visualize real-time data like Tony Stark.
Data security is vital for business apps. Any application or system that stores users’ sensitive data needs to use all means to protect it. Especially if it concerns banking apps, for any data leak that can lead not only to the money loss of a customer but a lawsuit for a bank.
As for real-time analytics, it is a game-changer for many types of businesses like supply chains, telecom, and others. The major influence of big data for the development of business-oriented applications lies in the following domains.
Real-time and historical data analytics makes the process of marketing strategy creation much easier and effective. Due to the real-time analytics and insights visualization systems, marketers have an unprecedented ability to react swiftly to any market change.
Big data global distribution has a huge impact on how a business continues in the domain of customer relations management. For instance, a trained AI can find the customers’ sentiment throughout social media and deliver valuable insights to a customer care department console. Often, it serves as an issue preventive mechanism.
Additionally, it helps to personalize customer interactions with a business’ website. First of all, it can be a chatbot that greets the new coming customer and asks what he or she wants. Apart from that, big data technologies improve targeting, making a service visible to the people who may be really interested in it.
By examining the vital parameters of your business, you can track which parts of it work well and which need your attention. In the same manner, big data technologies allow you to control employees’ performance and take immediate actions to motivate or instruct them.
Besides, providing the employees with big data tools can also improve the overall business performance. This will make them more efficient as your employees will have lots of valuable data to assist them.
Opinions expressed by DZone contributors are their own.