With the growing role of information and commercial data in doing business and making grounded decisions, more and more companies are interested in finding reliable ways to manage these data efficiently. But what is important to note is that volumes of data that can be useful for businesses are continuously growing, and the demand for big data services. Today the market offers a wide range of ready-made solutions. Still, in many cases, to achieve the highest efficiency of data usage, it is much more sensible to think about custom big data application development, which will allow building a solution according to your individual needs and requirements.
This article will explain the peculiarities of big data analytics software development and provide information about the pitfalls that you can face on this journey toward your individually built solution. So, if it sounds interesting, just keep reading to be well-prepared for all the possible challenges.
What is Big Data development?
When we are talking about big data development, we refer to the process of building software tools and products intended for gathering, analyzing, and managing large volumes of commercially important information. Further, the processed information can be used to reach various business goals, more grounded decision-making, and better strategic planning.
Such products are often powered by ML (Machine Learning) and AI (Artificial Intelligence) tools that ensure faster and more accurate data processing.
Want to learn what tendencies in big data development are ruling the market today?
Benefits of Big Data app development
As we’ve already noted above, the number of companies using Big Data solutions today is continuously growing. And the main reason behind this trend is the wide scope of benefits that a business can get thanks to the implementation of powerful tools for managing their commercial data. Here’s a list of the most important of them:
- high accuracy of business decisions;
- timely detection of possible issues and efficient problem solving;
- detection of suspicious behaviors and fraud prevention;
- higher customer satisfaction;
- better understanding of clients’ interests and preferences;
- better pricing management;
- higher operational efficiency;
- reduction of operational costs;
- increase in sales and profits.
We also recommend reading our blog post about the role of big data solutions for businesses. Just follow the link.
Big Data application development: challenges and ways to overcome them
Though Big Data solutions have so many benefits for companies, and today they are an integral part of different processes at many enterprises, some pitfalls are associated with them. If you are considering Big Data analytics application development, you should be aware of its challenges and ways to get over them.
The possible challenges and solutions are:
- Too large data sets -> unified data architecture, as well as tiering, compression, and deduplication methods, can be an answer;
- Unstructured data -> data consolidation will help in such a case;
- Incorrect integration of a solution -> integrate subsystems via APIs and automate the general control of the entire system;
- Too late data updating -> introduce tools for data filtering, Big Data slicing, and faster data processing;
- Lack of Big Data experts -> if you do not have in-house specialists, you can use the services of freelancers or find a reliable outsourcing agency.
We’ve analyzed big data challenges in more detail in one of our previously published articles. We believe that the provided information can be of great use to you.
Big Data technologies and tools
Though the final choice of technologies used for building a solution is usually the responsibility of a Big Data app development team, it still can be a good idea to check the most popular tools before proceeding to the software creation.
- Hadoop and Apache Spark for data storing and processing;
- Snowflake, EMR, BigQuery, Synapse Analytics, and DataFactory used as cloud platforms;
- Hive, RedShift, and Athena for data querying;
- SQL/NoSQL databases;
- Scala, Python, and Java;
- Kubernetes for building Big Data CI/CD pipelines;
- Kafka, AWS Kinesis for Big Data streaming in real-time.
Big Data best practices
To ensure that you are on the right way and that you will be using your Big Data tools with the highest efficiency level, read these best practices.
- Determine your business requirements and understand what tasks Big Data tools will perform.
- Understand what information should be collected and what data formats should be processed.
- Analyze what resources you already have and what should be created.
- Continuously analyze the performance of your Big Data tools and not ignore possible issues. If you are not fully satisfied with the results, improve the tool!
- Start slow. Integrating Big Data solutions is not an appropriate task where you should be in a hurry.
So, how to ensure the highest efficiency of Big Data analytics application development?
- Ensure that the right data sources were chosen to solve the hard business tasks.
- Create a solid ecosystem of reliable platforms to collect data from numerous sources.
- Monitor the quality of data and clean and re-organize databases if necessary.
- Apply ML and data science models when it is required.
- Visualize the results to get a clear picture of the ongoing situation.
Big Data use cases
As we’ve already mentioned, Big Data solutions are used in many business domains, including but not limited to:
- Banking and finance;
- Healthcare, etc.
Today there are a lot of examples of Big Data applications in many industries, but we’d like to focus on the sphere of healthcare and offer you to look at some of the most popular use cases. In this industry, Big Data tools help to:
- Improve the efficiency of the provided services, ensure a higher quality of patient care, reduce risks of human error, and enhance the diagnosis accuracy;
- Ensure better operational efficiencies, and provide visibility into resource management, inventory, and procurement;
- Facilitate and streamline accounting and finance.
Key big data use cases in healthcare:
- Analysis of electronic health records (EHRs). EHRs help monitor and store people’s health data, including pre-conditions, etc. Sharing data between medical experts can improve patient care. Big Data tools help efficiently work with this information, improve the quality of the provided services and reduce operational costs.
- Optimization of supply chain processes. One-third of hospitals’ overall operating expenses are allocated for the supply chain management. Big Data can help at all the supply chain stages, from placing an order to conducting payments and invoicing. Big Data tools ensure real-time visibility into all the related operations and allow healthcare organizations to avoid supply round-tripping and other issues related to deliveries and orders.
- Better patient care management at hospitals. With Big Data tools, it is possible to reduce patient hospital readmission rates. In addition, medical institutions that use efficiently processed data can detect at-risk patients. Medical history, diagnostic data, and medical equipment are used in this case.
Our seasoned engineers, developers, and analysts are always at your disposal. At Geomotiv, we are always open to cooperation.
Big Data projects by Geomotiv
Our team can become your long-term software partner and can assist in different tasks. We have rich expertise in building different software solutions, including big data and mobile app development. And we’d like to tell you about a couple of successfully realized projects that we have in our portfolio.
RTB platform logging system optimization
One of such projects was the RTB platform logging system optimization. Our client was a tech firm that built one of the leading online video ad ecosystems. As they expected a company's growth, they needed to prepare the system for the load increase. And our team was responsible for that. We analyzed the existing software and understood that the company had the same MySQL databases for storing, reading, and transferring event data, and all these processes were performed simultaneously. As a result, it took minutes to execute MySQL queries. However, our experts managed to find and implement the solution that allowed reducing this time to split seconds. Thus, the system was fully prepared for the load increase.
You can learn more about this project and the tools used here.
Data processing layer optimization in an RTB platform
Another big data project realized for the same client included optimizing a data processing layer in an RTB platform. When the client contacted us, the company was using Microsoft SQL Server. And the ownership cost was constantly growing because of the increase in volume. The client had a goal to optimize the expenses and conduct the migration to Hadoop, which presupposed the data processing layer reengineering. When the set tasks were performed, the client could enjoy reduced costs, greater system scalability, and decreased batch processing time. For getting more detail, please, follow the link.
We believe that good results can tell you more than just words, and these projects can be viewed as real proof of our developers’ expertise. If you have any issues similar to our client's or any other business problems, do not hesitate to contact us! We will deeply analyze your existing software systems, detect the nature of issues and offer possible solutions. Together with you, we will choose the most appropriate variant and transform all the ideas into real tools and products. And if you want to be sure that you are hiring the best Big Data developers, there are some recommendations for you in our blog.
For us, every client is a very special one! So let’s together help your business scale and develop!
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