The demand for data scientists in each industry is growing significantly. Fintech is no exception. Today, companies seek to gather more information about their users and deeply understand the current state of affairs. In this way, they can drive business development with data-driven solutions. What programming languages help out? Python, R, Java, Scala, etc.

In this article, we explain why it is worth using Python for Big Data and how it affects the fintech industry. Besides, you’ll get to know in what cases Python wins and loses as compared to other languages. And if you still intend to hire Python developers, don’t miss a step-by-step guide at the end of this article. Work with the best!

Python with Big Data for Fintech Projects: Pros & Cons

Not so long ago there was a time when many financial institutions were exploding because of the volume, variety, and velocity of data coming from different sources. The Great Recession of 2008 greatly affected world banks and other financial systems. Thus, these organizations are highly dependent on modern technologies used in data science. While some financial institutions strive for fast processing, leading fintech companies tend to use advanced digital solutions to fully manage big data. 

Big data solutions enable business owners to streamline decision-making processes and offer customers superior services. They help to store, track and evaluate huge amounts of data, including customer information and security. Due to the great trust in technologies that can cope with growing volumes of customers and a large number of transactions, the overall level of services offered by financial institutions has also increased significantly. They cover such aspects of financial services like payments, investments, personal finance, lending, insurance, etc.

Our team of developers worked on the Enhanced Financial Analytics Platform. It was developed from scratch for a Dutch fintech company. As a result, it was a fully-customized web platform aimed at providing reliable financial analytics services for stock exchanges around the world. Contact us for more details about our fintech services, which are based on effective technological trends – AI, ML, Big Data Analytics, etc.

What are big data solutions based on? They operate on lines of code written for specific purposes – to collect, analyze or manage big data sets. Usually, most associate Python with Big Data as the best programming language for developing effective analytics tools. Below we show whether it is wise to use Python in Big Data weighing up all the pros and cons of the language. 

ProsCons
  • An open-source community for automation technology
  • Multi-platform support
  • Solid library support with multiple packages
  • Extensive data processing support
  • Greater transparency between code and its execution
  • Accelerated code development
  • Multi-user development environment
  • Simplified code maintenance
  • It isn’t as fast as some other languages (C/C++ or Java)
  • More testing is required to avoid runtime errors
  • Minor incompatibility with mobile environments (Android and iOS)

The technology is rapidly spreading to all financial enterprises in the world to shape the future of users. The main goal is to adapt work with clients and provide highly personalized services. Big data at fintech focuses on digital solutions to meet the demand and needs of each customer. In this way, they help increase customer satisfaction. Let’s not neglect the other benefits of big data in the fintech sector. One of the advantages is fraud detection by analyzing information extracted from suspicious transactions. In addition, you can perform risk analysis and management. Using data analysis and visualization software, you can prevent or at least minimize all potential business risks.

How to benefit from big data in fintech? Choose the most workable programming language for your project. An important point is which programming language to choose for big data solutions. Choosing a programming language for the best use of the benefits of big data is a specific project task. You must weigh the pros and cons of a particular language in the early stages of software development. 

Why is Python a perfect fit for Big Data

Big data using Python offers a promising way to develop efficient software products. They follow the needs of the fintech market – to collect, analyze, manage, visualize, and store huge volumes of data. They are analytics tools, robo-advisors, fraud detection systems, personalized recommendation engines, etc.

Java & Python in Big Data for Fintech Products

JavaPython
  • Very responsive and quick Java Virtual Machine (JVM)
  • Better сompliance сapabilities
  • Java-based ecosystem used in most Big Data tools (Hadoop, Spark, Storm, etc.)
  • Large tool pool for Big Data projects
  • Without the support of a read–eval–print loop (REPL)
  • Versatile language
  • Great libraries for Big Data (Numpy, SciPy, scikit-learn, etc.)
  • Readable & maintainable code
  • Rapid development
  • Slower than Java but supports a REPL environment & ETL (extract transform load) pipeline

Not surprisingly, other programming languages, like Python, tend to solve modern big data challenges. This seems obvious, but you should keep in mind an overview of the various technologies to make the right choice for your own fintech project. So, if you are going to analyze huge amounts of data, and then model and visualize them, you will need to carefully study Python and Java – what exactly each of these languages ​​will ultimately bring to you.

Today Java is widely used mainly because of the fast JVM – Java Virtual Machine, which makes Java also fast as compared to other languages. Many fintech companies prefer this technology. They develop big data solutions in a single code database and launch them on any platform. So, you can benefit from the ‘write once, run anywhere’ principle used by Java. If you change the hardware or software platform, you remain calm with Java. Besides, it provides the type safety necessary for data processing software. However, Java doesn’t have a great number of libraries for data science methods, compared to some other object-oriented languages, such as, for example, Python.

Python is a powerful library package to meet a wide range of big data requirements – gathering, modeling, visualization, audio/image recognition, etc. As a result, Python can do a lot for you if you’re planning a fintech project with big data – it’s relatively fast and scalable. Outside of many scripting functions, this language uses many advanced libraries such as Scrapy, Pandas, NumPy, Matplotlib, scikit-learn, and others. All Python libraries allow Data Scientists and Data Analytics to successfully handle scientific computing. They can be classified into several categories based on their functions, that are:

Libraries in Python for Big Data

Python with over 200,000 libraries helps in this data-oriented world in various ways. Support for large libraries is commonly referred to as one of Python’s strengths. As a result, this language provides tools suitable for many data operations. First of all, it turns raw data into valuable ideas thanks to a powerful combination of Python with Big Data software. Secondly, thanks to the broad support of libraries, this object-oriented language helps implement machine learning algorithms. It should be noted that Python began to have many libraries for data analysis and machine learning not so long ago. This means that Python is considered by most experts to be the most effective programming language for big data, especially where it is extremely important to have well-analyzed data.

You won’t progress if you collect data that you don’t know how to analyze or interpret. If you want to manage databases efficiently or have other issues with data, please contact us. We will help you expand your talent pool or develop a custom solution with a focus on data capabilities.

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Python and Big Data: Real Use Case in FinTech

Python in Big Data and Fintech

Big data in the fintech industry helps manage risk, detect fraud, and meet customer needs. Given the high degree of risk associated with banking firms, ensuring customer satisfaction is one of the most difficult tasks for them. Customer retention – from ensuring the security of their transactions to providing them with the most relevant and advantageous offers – is a lifelong path for banking firms. Let’s see the real case of using technology in a large Investment banking company – Bank of America.

Bank of America – Long-Term Focus on Big Data

This is an international leader in providing services for consumer and corporate banking, insurance, investment banking, mortgage loans, wealth management, credit cards. Today this large banking company serves 70 million customers around the globe. In 2008, they realized that their customer base was declining. Special software analytics allowed them to figure out the reasons for this behavior. Through various sources where the data come from (sites, call centers, transcripts of interviews) they were able to establish the cause of the loss of customers. A group of analysts found that the comprehensive cash management system was too complex for the average customer. In addition, small banks began to satisfy consumer demand for this product, offering access to a simple and flexible cash management system. 

Eventually, this finding allowed Bank of America to quickly respond to changes in user behavior and make the right decision – to improve services with their banking system. As a result, the company declined the all-in-one offer. And in 2009, they launched a more flexible online product: CashPro Online, a working capital management platform and a separate mobile version of this product in 2010.

They don’t stop at these results and go on using technology. For example, in 2020, Bank of America uses a new data model to predict where the labor market is heading. Check out the video below to keep up with the latest news.

Expertise in Big Data Using Python: Where to Find & How to Check?

So, if you decide that Python is the most suitable language for developing high-tech projects that use big data, continue reading this article. Finding a qualified Python development team is half the battle. Given that Python is one of the most popular programming languages, you can easily find Python developers. About 8.2 million developers who use Python are available to you. They make up a large part of the IT community worldwide, and machine learning is a decisive factor in community size – 69% of ML developers and data scientists now use Python.python developers for big data

Despite the huge community, it takes some time and effort to hire Python developers who are highly skilled in the various environments and libraries used for big data. You need to search among the many available resources (UpWork, LinkedIn, Clutch, GoodFirms, etc.). After that, you should check the level of knowledge to make sure of the final results. Many companies are faced with the problem of finding a technical partner they can trust, but still, try.

Where to Find the Best Python Developer? 

If you look at the statistics below, you’ll see that most Python developers work for the company. Therefore, it is reasonable to look for true professionals in software development companies. 53% of specialists work in a team on multiple projects. So, it might be advisable to turn to outsourcing companies that offer a team of experienced developers based on the needs and requirements of the business. Today it is one of the most profitable engagement models because you have access to a global talent pool. Do not miss it!

Developer working on Big Data using Python

Mangosoft is a Python outsourcing company that is equipped with a complete set of tools to help you develop all your ideas at high speed. Get powerful Big Data software from experienced developers to make the best use of business intelligence.

How to Check the Qualifications of Python Developers? 

No matter where you find a specialist – whether it be a leading outsourcing company or a freelance platform, you should evaluate them as much as possible, as this will affect your future success. As a rule, solid experience is measured by the number of successfully implemented Python projects. It isn’t always true. Well, what to do?

The following guide will help you set clear criteria for choosing a reliable partner:

  1. Do in-depth research on software companies you can work with. You can visit special reviews presented at Clutch or GoodFirms, or read our detailed overview of 10 Best Fintech Companies in 2020
  2. Contact a business representative to find out all the details concerning cooperation, terms and conditions, costs, etc. It is highly recommended that you indicate all the business needs that you intend to implement in your project. If you are not sure about something, you can always turn to a business analyst who will advise on this or that prospect.
  3. Conduct a technical interview with a potential candidate to evaluate the relevant experience. There are many questions related to Python. But you must narrow down the skills and knowledge needed for your own project. If you are not tech-savvy, you can use the services of an IT recruiter or an outsourcing company that usually has this expert on board.

You always can consider a dedicated software development team from Mangosoft. What do you get as a result? Quick responses to your demands, fast turnaround times, staff flexibility, reduced staffing costs, direct reporting of project progress. You can get it from us, because we have significant experience in implementing projects, including for fintech, where big data is effectively used to optimize the business and direct its growth to the peak.