Fraud detection has come a long way. What once relied on manual reviews and endless spreadsheets is now powered by real-time streaming data, automation, and advanced engineering techniques. Let’s explore this journey, highlighting why businesses must evolve their fraud detection strategies to stay ahead.
In the early days, fraud detection heavily depended on manual processes. Analysts painstakingly reviewed transactions, cross-checked entries, and flagged irregularities—often using Excel or similar tools. While spreadsheets offered some flexibility, they had significant drawbacks:
Time-Intensive: Reviewing fraud manually took days or weeks.
Static Data: Spreadsheets lacked real-time capabilities, making it easy for fraudulent activities to slip through.
Error-Prone: Human oversight led to missed red flags.
As fraudsters became more sophisticated, the limitations of spreadsheets became glaringly obvious.
The Automation Revolution – Moving Beyond Static Tools
Enter automation. With the rise of data engineering tools, businesses began automating fraud detection workflows. This shift offered two key benefits:
Scalability: Companies could handle larger datasets without requiring proportional increases in manual effort.
Technologies like SQL scripts, Python automation, and ETL pipelines laid the foundation for modern fraud detection.
Streaming Data – The Real-Time Game-Changer
Today, fraud detection thrives on real-time data streams. Unlike traditional batch processing, streaming allows businesses to process data as it’s generated, enabling immediate detection and response.
How Streaming Works
Streaming involves tools like:
Apache Kafka: For real-time data ingestion and processing.
AWS Kinesis: To handle high-throughput streaming.
Apache Flink: For analyzing data streams in real time.
These tools empower businesses to spot fraudulent patterns instantly. For example, a sudden surge in login attempts or unusual purchasing behaviors can trigger immediate alerts.
Webhooks – Instant Alerts for Fraud Prevention
A critical enabler of real-time fraud detection is the webhook. Think of a webhook as a digital messenger—it delivers data from one system to another the moment an event occurs.
Why Webhooks Matter
Immediate Notifications: Fraud teams get alerts as soon as suspicious activities happen.
Seamless Integration: Webhooks work across systems, from e-commerce platforms to payment gateways.
For example, a webhook can notify fraud teams the moment a high-risk transaction is flagged, enabling them to act before damage is done.
The journey from spreadsheets to streaming is more than a technological evolution—it’s a necessity in today’s fast-paced digital world. Fraudsters aren’t waiting, and neither should your business. By adopting streaming data, webhooks, and automation, you can stay ahead of threats, protect your bottom line, and build a stronger, fraud-resistant organization.
Real-time presence indicators is a cool feature request coming to your backlog. If you need to improve your companies software, DEV3LOP is here to discuss real-time presence indicators!
I spent many nights creating a new software Vugam, but now I need to make my software better. What do I do? Real-time presence indicators could be the next step in the equation.
However if you’re like me, you’re calling this multiplayer, or tracking cursors. Perhaps it’s the ability to see someone is typing in slack, or that there’s a green icon in zoom if you’re online. Some people are never online, and it’s really obvious via zoom icon indicator.
Does your software need multiplayer?
Do you want multiple users working together in a collaborative environment?
Are users working together on similar problems?
Are users using single player software that is built to collaborate with other people?
My first time seeing real-time indicators was while using GoogleSheets and GoogleDocs in college, however no cursor indicators and limited capabilities had me wondering, what’s next… But not being in the software engineering path, being focused on information system, I felt a little disconnected from the technology.
This blog discusses improving user experience with collaboration, the differences between what to stream real-time and what to store in storage, and the balancing act of how to manage real-time data flows.
I need to create a user experience that allows end users to come together.
But once that software is done, how do I improve? Perhaps, real-time presence indicators… Using websockets.
Hi, I’m Tyler, I’m interested in adding real-time presence indicators into new projects and in our future software releases. One in particular is creating a multiplayer analytics software, however how the heck do I make a software multiplayer? Friends have told me this is a stretch and a lot of work…
I’m naive, I didn’t believe them. Created websockets/multiplayer in a day, and created a bit of a problem. I wasn’t thinking about what should be persistent between sessions VS streaming. This caused a lot of bugs. But lets take these lemons and create a drink.
Javascript VS HTML (legacy) in this screenshot.
Why a Server and a Database Are Essential (and Why the Cursor COULD stay Ephemeral)
When building real-time web applications, one of the biggest decisions is how and where to store data. Not knowing how to do this yourself is a cool maze of learning that I’d like to explain to you, for business users, and technical people who are interested in transitioning into a more technical space!
Some might assume that a websocket alone can handle everything, but this misses a crucial point: you need a server and database layer to keep important data secure, consistent, and reliable. Some may even start learning “acid compliance” to further explore the rules of a database.
Following down a path of creating a websocket software that didn’t consider what should be persistent in a file or database, VS streaming in websockets is where I have fallen a victim, but in the mistake I found this is likely not common sense to business users who desire this request…
Real-Time Presence Indicators: The server acts as the backbone, ensuring everything runs smoothly and logically
Again, the server acts as the backbone, you’ll need a server to use websockets and the backbone is used to ensure everything runs smoothly and logically!
The database (or perhaps document storage) preserves what actually matters—data that should last beyond a single session or connection. But not everything belongs in the database. Take the cursor: it’s a dynamic, real-time element. It doesn’t need to be saved, tracked, or tied to a user’s identity. Let it stay ephemeral, moving freely through the websocket.
This approach doesn’t just streamline the system; it respects user privacy. By not storing or analyzing every cursor movement, users can trust they aren’t being monitored at this granular level. It’s a small but meaningful way to give control back to the user.
Why real-time cursor trackingHas Stuck with Me
My goal is to make real-time cursor tracking and communication a cornerstone of the web applications I build in the future. It’s a tool I’ve come to value deeply, largely because of the success I’ve seen with platforms like Figma.
Real-time collaboration is more than just a feature; it’s a way of thinking. Working with it teaches lessons about system design that stick with you—lessons that make you better at building solutions, even if you’re not the one writing the code.
The nice thing about creating a real time cursor tracking software yourself is that you run into the troubles of not knowing better, and this is the best teacher. Whether to use express or websockets, an exciting time.
There’s also a balancing act here that matters: Real-Time System Management
Real-time systems shouldn’t come at the expense of user privacy.
Knowing when to store data and when to let it flow naturally is key—not just for performance, but for creating systems that people can trust. Perhaps that system is one that doesn’t LEARN on your users and create a product within the gray area.
For me, this isn’t just a technical challenge—it’s an opportunity to build better, smarter, and more thoughtful applications. Want to learn more, simple, contact us now.
Everyone needs software, and they need it now! If project success defines your situation, I’d like to introduce to you a concept that may change your perspective on solving problems. This is where a tedious project maybe completed in minutes VS months, thanks to artificial intelligence.
Micro opp apps or micro ops apps, in our mind, are similar to micro opportunities and are usually operational in nature. Little wins or low hanging fruit that is accessible to win in a short period of time.
Micro is the size of the code, the length of the engagement, the requirements given are thin, and that’s what you need to complete this micro software.
We specialize in micro and macro application development (we are dev3lop) and have over a decade of experience implementing these applications into hardened rocket ships at enterprise, government, and commercial companies.
Micro Opp apps
Have you ever wanted to craft software but never had the time to invest into the education or fundamentals? Great! AI is in a place where you can ask it to write an entire prototype and within a few minutes you have proper software that solves a business problem!
The open-source world and closed-source LLM revolution are meeting eye to eye from a code perspective, and it’s a great time to dive into this realm of AI-infused development.
Companies are constantly seeking ways to streamline operations without the burden of overly complex software. Micro Operational Applications are emerging as the perfect solution—tailored tools that address specific business needs without the unnecessary bulk of traditional SaaS products.
Why Traditional SaaS Products Fall Short
While SaaS products offer robust features, they often come with limitations that make them less than ideal for certain business requirements. Their one-size-fits-all approach can lead to tedious workflows and inefficiencies. Customizing these platforms to fit specific needs can be time-consuming and costly, involving multiple software engineers, database administrators, designers, and executive approvals.
The Rise of Micro Operational Applications
Micro Operational Applications are changing the game by providing targeted solutions that can be developed in a single working session. Thanks to advancements in AI and development tools like ChatGPT and Claude, non technically savvy individuals can now transform text prompts into working prototypes swiftly.
Prompt: “Create a single html file using cdn <insert javascript framework>: <type what you want the software to do, how you want it to look, and any features you can think of>”
This prompt is how you can begin creating html files that can be a solution to a problem, it’s easy to share with others via chat software, and may start get peoples wheels turning!
Benefits of Micro Operational Applications:
Speed of Development: Quickly create applications without the lengthy timelines of traditional software projects.
Cost-Effective: Reduce the need for large development teams and extensive resources.
Customization: Tailor applications precisely to meet specific business needs.
Agility: Adapt and iterate applications rapidly in response to changing requirements.
AI Assistance Accelerates Development
AI-infused development tools are democratizing the creation of software. They enable individuals who are “technical enough” to develop functional prototypes without deep expertise in coding. This shift not only speeds up the development process but also reduces the dependency on large teams and extensive planning.
A Glimpse Into the Future
Given the rapid advancements in AI-assisted development, it’s foreseeable that Micro Operational Applications will become mainstream in the next few months or years. They represent a significant shift towards more efficient, agile, and customized business solutions.
Embrace the future of business operations with Micro Operational Applications—where efficiency meets innovation.
Authors perspective on micro apps in production environments.
Some projects are easy to complete but require a lot of social skills to understand full requirements. Micro apps win here because it gets the brain moving without much input. Also, micro apps are great when you have all the requirements, this allows for instant prototyping, and instant value proposition.
Micro Operational Applications are used to solve problems that don’t require a SaaS product because the SaaS product is too robust and has limitations that simply make business requirements tedious.
They are software you can create in a single working session, and they are prototypes for what could become a more hardened software in your wheel house. Think of “excel” today, it’s easy to stand up, get moving, and most people know the software. Micro apps are moving this way quickly. You don’t have to be a hero of tech to move it forward.
Micro Operation Applications are becoming easier to develop due to AI assistance.
Tools like Claude and Chatgpt are opening the door for ‘technical’ enough gurus to move the torch from text prompt to working prototype.
These micro apps are helpful because they offer a door into not needing three software engineers, your DBA, your designer, and executives involved in the creation. They can happen faster than any software project has happened.
To make it truly important there’s more engineering required, however given AI infused development is picking up in speed, I can foresee Micro Operational Software becoming main stream soon enough.
The next phase is going to be AI connecting it to backends. Without a lot of work. Until then you’re going to need data engineering to help you make the leap.
So as far as we know, AI is lacking the ability to thread into your current data systems without more lifting, and that’s where you’ll need focused Data Engineering Consulting Services!
Right now, businesses need a way to store, manage, and analyze vast or even small amounts of information, thus the birth of spreadsheets. Companies in the past have relied on businesses like dev3lop.com’s data engineering services to help alleviate the struggle of updating data in a timely fashion, regardless of whether it is ETL or ELT, or both at the same time. We have experience helping companies drag, drop, and write code from the ground floor.
Many companies use expensive drag-and-drop software to manage data because it is easy to use and has a nice visual interface. However, businesses increasingly recognize the benefits of using open-source platforms like Node.js to move, transform, and load data. Node.js is better than expensive ETL products because it can do the same job for free at scale. This shift can save money and make your system more flexible and scalable.
Most cloud offerings will allow you to run nodejs in a free tier for a lifetime due to the amount offered and needed in an environment.
The High Cost of Proprietary Data Warehousing.
Proprietary drag-and-drop data warehousing tools come with significant costs, and even though you may not be using the robust features it’s added each year, you’re paying for it and still paying more each year.
These include:
High Licensing Fees: Commercial software solutions require substantial upfront investments and ongoing licensing fees.
Maintenance Costs: Regular updates, patches, and customer support often incur additional costs.
Limited Customization: While drag-and-drop interfaces are user-friendly, they can limit the ability to tailor solutions to specific business needs.
Why Node.js is a Game-Changer
Node.js, an open-source JavaScript runtime built on Chrome’s V8 JavaScript engine, offers a powerful alternative to traditional data warehousing solutions. Here’s why transitioning to Node.js can be a strategic move:
1. Cost Efficiency
Node.js is open-source, meaning it’s free to use. This immediately eliminates the hefty licensing fees associated with proprietary software. Additionally, a large and active community of developers continually enhances and supports Node.js, ensuring ongoing improvements without extra costs.
2. Scalability
Node.js is designed for scalability. Its non-blocking, event-driven architecture allows it to handle numerous simultaneous connections efficiently, making it ideal for real-time applications and data-intensive processes. This is particularly beneficial for businesses experiencing rapid growth and increasing data volumes.
3. Flexibility and Customization
Unlike rigid drag-and-drop solutions, Node.js offers unparalleled flexibility. Developers can build custom data processing pipelines and integrate seamlessly with other systems and APIs. This customization ensures that the data warehousing solution can evolve with the business’s needs.
4. Performance
Node.js is known for its high performance. Its asynchronous nature allows for faster code execution and data processing and retrieval. This results in more responsive applications and quicker insights from data analysis.
5. Community and Ecosystem
The Node.js ecosystem is rich with modules and libraries that can be easily integrated to extend functionality. From data visualization to machine learning, there’s likely a Node.js module that fits your needs. The active community also means finding support, tutorials, and best practices is straightforward.
Making the Transition: Best Practices
Transitioning from a proprietary data warehousing solution to Node.js requires careful planning and execution. Here are some best practices to ensure a smooth migration:
I’ve come to conclusion this route isn’t for everyone and some companies want to spend millions or as low as $15k annual on a desktop license that will enable someone or many people ad-hoc analytics capabilities but if you multiple that in ten years, you may start regretting, this is where companies like Dev3lop can save you money, so why wait!
Tyler Garrett, Founder
1. Assess Current Infrastructure
Evaluate Existing Setup: Begin by thoroughly examining your current data warehousing environment. Document all components, including hardware, software, data pipelines, storage solutions, and integrations.
Identify Components for Replacement or Upgrade: Determine which parts of your system are outdated, inefficient, or incompatible with Node.js. Identify dependencies and assess potential risks associated with changes.
Create a Detailed Transition Plan: Develop a comprehensive plan outlining the steps needed to transition to Node.js. This should include timelines, resource allocation, risk mitigation strategies, and fallback plans. Prioritize critical components to ensure business continuity.
2. Skill Development
Invest in Training: Allocate resources for training your team in Node.js. This could include formal courses, online tutorials, or workshops. Ensuring that your developers are proficient in JavaScript and familiar with Node.js’s event-driven architecture is crucial.
Encourage Certification and Continuous Learning: Encourage team members to pursue certifications and participate in continuous learning opportunities. Staying updated with the latest trends and best practices in Node.js will maximize your team’s effectiveness.
Build a Knowledge Base: Create a repository of internal documentation, best practices, and code snippets to facilitate knowledge sharing within the team. This will help new members get up to speed quickly and ensure consistency in development practices.
3. Pilot Projects
Start Small: Select a few non-critical projects to implement using Node.js. These pilot projects should be manageable in scope but comprehensive enough to provide meaningful insights into Node.js’s capabilities.
Evaluate Performance and Feasibility: Monitor Node.js’s performance in these pilot projects. Evaluate aspects such as speed, scalability, ease of integration, and any challenges encountered.
Adjust Based on Feedback: Use feedback from the pilot projects to refine your approach. Address any issues and make necessary adjustments before expanding the use of Node.js to more critical components of your data warehousing system.
4. Incremental Migration
Plan for Phased Implementation: Break down the migration process into manageable phases. Begin with less critical components to minimize risk and gain confidence.
Ensure Integration Continuity: Gradually transition services to Node.js while seamlessly integrating existing systems. This approach helps identify and mitigate integration issues early.
Monitor and Optimize: Continuously monitor the performance and stability of the migrated components. Optimize configurations and processes as needed to ensure that the new system meets or exceeds the performance of the old one.
5. Leverage Community Resources
Engage with the Node.js Community: Participate in online forums, discussion groups, and community events. Community engagement can provide valuable insights, support, and best practices.
Utilize Open-Source Libraries: Take advantage of the vast array of open-source libraries and modules available for Node.js. These can significantly speed up development and reduce costs.
Stay Updated with Best Practices: Regularly follow Node.js blogs, newsletters, and official documentation to stay informed about the latest updates and best practices. This ensures that your team remains at the forefront of Node.js development techniques.
By following these expanded steps, businesses can ensure a smooth and effective transition from traditional data warehousing solutions to a modern, open-source Node.js environment.
Conclusion on Drag-n-drop or Node
Transitioning from expensive drag-and-drop data warehousing solutions to Node.js is a cost-saving measure and a strategic decision offering enhanced flexibility, scalability, and performance. We have over a decade of experience helping companies transition to code solutions and drag-n-drop ETL solutions, and now that we are on the nodejs journey, we are eager to keep taking our clients along the path if that’s in their vision!
As businesses continue to seek ways to optimize their operations and reduce costs, embracing open-source solutions like Node.js will become increasingly appealing.
By carefully planning the transition and leveraging Node.js’s robust capabilities, organizations can unlock new levels of efficiency and innovation in their data management processes. Now is the time to explore the possibilities and shift towards a more agile and cost-effective data warehousing solution.
Data engineering in business is becoming increasingly important as we rely more on software with API systems that do not speak to each other outside of the wheelhouse of a full-stack engineer. Even with companies spawning that are paid to tunnel information from the API, most have specific limitations; it’s more of the same problems as creating it internally; one problem most see is this becoming a significant expense over ten years because the price continues to increase!
The choice of programming languages, like Python or nodejs, can significantly impact a company’s efficiency, scalability, and competitive edge.
Python has long been a favorite in data engineering because it has a lot of ‘make it easier’ methods for data science, ML, and AI… like the panda is an incredible solution within Python that is difficult to avoid… but wait, have you heard about nodejs?
Currently, you can’t Google JavaScript without finding the nodejs.org website. The rise of Node.js prompts a shift that savvy businesses can’t afford to ignore.
This article delves into why transitioning from Python to Node.js, especially in API-driven data pipelines, is a strategic move for forward-thinking companies eager to take advantage of open-source tools.
The Power of Node.js: A New Era in Data Engineering
Node.js represents a significant shift in data engineering, offering unparalleled performance, scalability, and efficiency for modern applications. Think of Node.js like a colony of rabbits: quick, agile, and capable of multiplying rapidly to handle an ever-increasing workload. Just as rabbits swiftly navigate their environment and adapt to changing conditions, Node.js, with its non-blocking, event-driven architecture, efficiently manages large volumes of concurrent connections, making it ideal for real-time data processing and API-driven environments.
Node.js simplifies workflows, enhances collaboration, and accelerates development cycles by enabling the use of JavaScript across the entire development stack, an important characteristic that JavaScript-familiar engineers will understand.
This unified approach reduces future complexity, lowers costs, and improves the agility of data engineering projects, positioning businesses to meet the digital age’s competitive requirements.
1. Superior Performance and Scalability
Node.js is built on the V8 JavaScript engine, which is known for its high performance and efficiency. Its non-blocking, event-driven architecture allows Node.js to handle many simultaneous connections with minimal overhead, making it an excellent choice for data-intensive applications where performance and scalability are paramount.
Node.js’s event-driven architecture is akin to a well-coordinated warren of rabbits, each ready to leap into action at a moment’s notice. This design allows Node.js to handle multiple connections simultaneously without getting bogged down. Instead of waiting for one task to complete before starting another, Node.js listens for events and responds to them as they occur. This means that while one part of the system waits for a database query to return, it can still handle other requests, ensuring that resources are utilized efficiently. This architecture significantly enhances performance, particularly in applications that require real-time data processing, such as live chats, online gaming, or collaborative tools.
Scalability: Easily Scales Horizontally, Making It Ideal for Growing Businesses
Just as a rabbit colony expands effortlessly to accommodate more members, Node.js applications can quickly scale horizontally. When a business grows and demands increase, Node.js can add more instances to handle the load without significant changes to the codebase. This horizontal scaling is achieved by distributing the workload across multiple servers, ensuring the application remains responsive and performs well under high-traffic conditions. This makes Node.js an excellent choice for businesses expecting rapid growth, as it can adapt and scale cost-effectively, ensuring consistent performance regardless of user demand.
2. Unified Language Across the Stack
One of the most compelling reasons for adopting Node.js is the ability to use JavaScript across both the front and back end. This unification simplifies development processes, reduces developers’ learning curve, and fosters better collaboration between teams.
Key Benefits:
Consistency: JavaScript across the stack leads to more consistent code and easier maintenance.
Productivity: Streamlines development processes, accelerating project timelines.
3. Robust Ecosystem and Community Support
Node.js boasts a vibrant and rapidly growing ecosystem with many libraries and tools available via npm (Node Package Manager). This extensive ecosystem enables developers to quickly find solutions to common problems, accelerating development cycles and reducing costs.
Key Benefits:
Extensive Libraries: Access to a vast array of tools and libraries that can be easily integrated.
Active Community: Continuous improvements and innovations driven by a large, active community.
4. Real-Time Capabilities
For businesses that rely on real-time data processing and analytics, Node.js offers significant advantages. Its non-blocking I/O operations and event-driven nature make it ideal for applications that require real-time data streaming and updates.
Key Benefits:
Real-Time Data Processing: Efficiently handles real-time data streams, crucial for modern applications.
User Experience: Enhances user experience by providing immediate feedback and updates.
5. Cost Efficiency
Node.js can lead to substantial cost savings in both development and operational expenses. Its ability to handle high traffic with fewer resources translates into lower hardware and hosting costs. Additionally, the development speed in Node.js can reduce time-to-market, providing a competitive advantage.
Key Benefits:
Reduced Infrastructure Costs: Efficiently uses resources, leading to lower operational costs.
Faster Development: Accelerates time-to-market, saving on development costs.
Transitioning to Node.js: A Strategic Business Decision
Enhanced Collaboration and Productivity
By adopting Node.js, companies can streamline their development processes and improve collaboration between front-end and back-end teams. Using a single language across the entire application stack simplifies communication and reduces the complexity of codebases, leading to higher productivity and more maintainable code.
Future-Proofing Your Technology Stack
Node.js is rapidly becoming the go-to technology for modern applications, particularly those that rely on microservices and API-driven architectures. By transitioning to Node.js, businesses can future-proof their technology stack, ensuring they are well-equipped to handle evolving technological demands and innovations.
Real-World Success Stories
Several industry leaders have successfully transitioned to Node.js and reaped significant benefits. Companies like Netflix, LinkedIn, and Walmart have reported improved performance, scalability, and overall user experience after adopting Node.js.
Conclusion
The transition from Python to Node.js in data engineering is more than a technical shift—it’s a strategic business decision that can drive performance, scalability, and cost efficiency. For companies focused on building robust, real-time data pipelines and API-driven applications, embracing Node.js is a step towards future-proofing their operations and staying ahead of the competition.
Make the smart move today—transition to Node.js and unlock the full potential of your data engineering projects.
