This article is intended for you, dear clients, both current and future. Whether you’re looking to build a new application, enhance an existing system, or integrate complex technologies, crafting an RFP that developers will love involves more than just listing technical specifications. It’s about creating a document that not only conveys your project’s vision but also fosters collaboration, innovation, and a mutual understanding of expectations.

In this guide, I will explore key strategies to help you write an RFP that attracts high-quality proposals and sets the stage for a successful partnership with your development team.

This article will help you understand the RFP process in the realm of software development from the developers’ perspective. By gaining insights into how developers interpret and respond to RFPs, you will be better equipped to communicate effectively and align expectations, leading to more successful collaborations and project outcomes. Read More »

When working with APIs, dealing with raw JSON for input and output can be cumbersome, especially when there’s a convenient UI designed specifically for that API. However, the UI doesn’t always keep up with changes in the service, and sometimes it’s useful to emulate changes in the service along with the UI to finalize decisions on what and how to modify.

Such a solution could allow you to replace actual API responses with tailored fake data, eliminating the need for altering real data or making code changes. It would accelerate slow requests by recording and replaying them, significantly reducing wait times for UI tweaks. Developers could simulate edge cases, such as handling 4XX or 5XX status codes or testing long strings, by modifying response bodies, status codes, or headers, or add a debug/verbose mode automatically to all calls to an API. Additionally, it would enable frontend development against unimplemented APIs by mocking responses, facilitating progress even when backend endpoints are incomplete. For integration with third-party platforms that only function on production domains, such a solution could reroute requests, allowing safe testing on production without risk. Furthermore, it would support debugging by adding artificial delays and simulating network errors, ensuring robust handling of edge cases. In essence, mocking servers on production in your browser would streamline development, enhance testing, and ensure a more resilient application.

As the “victim,” I chose the SAP Commerce demo store (to whose code I don’t have access to), specifically its search functionality. The goal was to inject my custom code between its backend and frontend, and as a demonstration of capabilities, automatically apply certain facets for specific queries. As a result, for the query “cheap cameras,” I will actually see cheap cameras and not tripods, for example.

In one of my recent personal projects, I ventured into the vast archives of The Saturday Evening Post, a magazine that has captured American life since 1821. What started as a nostalgic dive into the past tu,/rned into a technical quest to make over a century of magazine issues searchable and accessible online.

Utilizing a blend of OCR with Tesseract and search mechanisms based on Apache Solr, OpenAI embeddings, FAISS, ML Re-ranking, I managed to create a searchable database of the magazine’s rich content from 1900 till our days. From Norman Rockwell’s iconic illustrations to pivotal historical articles, every page was digitized and ready to be explored.

This project engagingly occupied several weekends and further deepened my expertise in search technology. Dive into the article to see how vector search and traditional keyword search can be used to bring the past into the digital age.

Navigating the vast seas of product searches in B2B and B2C marketplaces can be quite a headache, especially with loads of data from various suppliers. It’s tough for marketplaces to tweak supplier info without spending a fortune, yet when searches go awry, customers tend to blame the marketplace, not the suppliers. My new article suggests a slick new way to cut through the clutter—think product families, not just standalone products. This isn’t just about tidying up; it’s about making the search experience smoother and more intuitive for everyone involved.

Let’s dive into why the old way of doing things—splitting catalogs into neat categories and individual products—just doesn’t cut it for big, diverse inventories. Take electronics and clothes: sorting shirts by color and size is straightforward, but try categorizing gadgets that way and you’re in for a real challenge. The game-changer here could be switching to a product families model, which groups related products together under one big family umbrella. This approach keeps things organized and makes shopping easier, which is exactly what we need for a smoother, more customer-friendly search process in our digital storefronts.

The Part I of this topic is about Product Families as an extension of the Variant Products concept.

Imagine a world where an AI system not only listens but observes, constantly collecting data unless explicitly told not to. This “exoskeleton for the brain” is not just a tool but a transformative extension of human capability, enhancing efficiency and decision-making in everyday tasks. For some, this raises alarms about privacy and the potential for misuse in a society already wary of data overreach. Yet, the real question lingers: how will such capabilities shape the way we live, work, and interact not just with technology but with each other?

As we stand on the brink of making these integrations a reality, it’s important to consider not only the current implications but also the generational shift that might come with it. The youth of today, growing up with AI as a norm rather than an addition, might view this integration as indispensable—melding their cognitive functions with artificial intelligence to an extent previously unimagined. This narrative isn’t just about the technology we create but also about the human experiences and societal structures that will evolve with it. Dive into the full discussion to explore the profound shifts looming on the horizon.

In this article, I want to talk about my experience with how to make a website or online store search more responsive to business requirements and user expectations.

It’s common to give too much attention to technical issues, especially now, with the advent of machine learning/AI in this field. But more often than not, the deepest problems are on the surface and their solution is much simpler than a functional refinement.

One of the main challenges in managing e-сommerce websites is ensuring the product search feature works effectively. Imagine this: you notice an issue with how products are being found through search, and you decide to fix it by tweaking some settings. However, altering these settings could unexpectedly impact your business. When we talk about something being ‘broken’, it’s not just about error messages popping up. Rather, it could mean that customers are having a harder time finding what they want, which might lead to fewer sales over time. This is a subtle effect that can spread across your entire platform. But how do you pinpoint the exact cause of such a decline? Testing and monitoring changes in an ecommerce environment are complex because even small modifications can have wide-ranging effects on customer behavior and, consequently, sales performance.

To monitor changes, it’s essential to understand what to collect and how to extract data from the gathered information. Specifically, for e-commerce search, there are no good ready-made monitoring tools available, but there are various DIY kits that address some needs. Such tools suggest collecting and analyzing search results for reference queries and visualizing changes over time. However, this approach falls short in many respects. For instance, it only covers the most frequent queries.

When it comes to modernizing site search, there’s a big question about whether this is done from scratch or if it’s possible to use accumulated data from existing sessions. Developing from scratch is always a risk—because any assumptions you have about how users will search for content may be wrong. Therefore, regardless of how good the first version of the search turned out to be, it will need to be revised and reworked many times.

Today, I want to share the approach that I have used in several projects. I call it Search Analytics. Read More »

This article focuses on how SAP Commerce stores models in memory, which is a vast topic. To begin with, we will explore a relatively simple aspect, such as the unusual structure that appears in the debugger when trying to view the contents of any SAP Commerce model. We will also discuss how to access all the object properties contained within it.

There are dozens, even hundreds of test automation frameworks available, and we developed our own. Why? Why? Was it worth the effort? 

This article describes our needs and our solution, E2E CLI: the architecture of the API testing tool which has been used by our team for about 2 years. Back in 2020, it was developed by me from scratch over the course of a weekend — In other words, it is a very simple piece based on very simple ideas. That first version was supporting 80% of the functionality available today in the last version (and described below). We don’t add features without good reason.

We use this tool to end-to-end test all our services and integration flows. Whenever we make changes to a software system, we need to ensure that they do not break what was already working.

As the architect and lead developer, I designed and developed it from the ground up, so, of course, I am very proud of my brainchild. But the field cannot be well seen from within the field. You know, reinventing wheels is not always bad. When you build something on your own, you are in full control over what is being built, what its purpose will be, and when it is finally conceived and executed as you wish. It has been two years since I have encountered any alternative to our solution. 

To reach a wider audience, I found it beneficial to share key concepts hoping to get some feedback from the community and enhance the product.

Technically, the tool has nothing to do with SAP Commerce; it’s for testing. However, our entire team, which uses this tool daily, is focused mainly on SAP Commerce development. And the tool is tailored to be used with SAP Commerce.

While the client (for whose benefit the tool is being used) has authorized me to share high-level details, I cannot disclose names or code. In spite of this, the conceptual view should be useful on its own and might become a good foundation for your own solution.

A shopping cart is the centerpiece of almost every e-commerce website.

In fact, this component is the first thing that makes an e-commerce store different from just an online catalog. Working with issues and challenges related to the cart is something you’ll never forget. 

The Russian novelist Leo Tolstoy, at the opening of his novel Anna Karenina, writes ‘All Happy families resemble one another, but each unhappy family is unhappy in its own way.’ You know, the diversity of cart-related issues I have been observing in the SAP Commerce projects, made me think that Tolstoy meant the developers as well. The diversity of the issues and challenges in this component is so vast that every case is different. However, when it comes to building or optimizing the shopping cart processes, understanding the limitations,  pitfalls, and peculiarities is a huge plus. 

Let’s have a look at how a shopping cart works in SAP Commerce under the hood and how to avoid making mistakes in shopping cart customization.

When the coronavirus outbreak happened in China, it boosted local e-commerce as many stuck at their homes forced to buy their goods online. Sales of fresh food on JD.com reportedly jumped 215% during the 10-day period and counting. The ability to serve a large number of customers is growing every year. The traffic may be doubled in a week. How to make sure your system is capable to serve high traffic demand? In today’s article I’m sharing my experience with Tsung, a high-performance testing framework.

Any SAP Commerce Cloud setup is based on a multi-server setup organized as web cluster. This product comes with clustering support out of the box since very early versions. However, many years in a row, it is still one of the most challenging topics. 

JGroups is an essential component of SAP Commerce Cloud. It is widely used as an embedded solution for distributed messaging and eventing in the cluster. It is hard to say that it is underdocumented or it has some quirks that nobody knows how to encounter. However, almost every large project stumbles with JGroups. This article is aimed to shed some light on the internals of the SAP Commerce Cloud clustering and JGroups.  

What content management system you use is of a much higher importance today than it was years ago. Today’s CMS needs to contend with a multi-device world with a wide variety of tablets, mobiles, laptops, and be architecturally ready for new not-yet-existing channels in the consumer’s universe, such as AR/VR and the Internet of Things.

A CMS typically consists of a frontend part, or a «head», and backend. The headless CMS, as their name says, don’t stick to a single predefined “head”. Theoretically, you can develop your own if it is good for your case. Another name for headless CMS is API-first because this interaction is based on APIs.  

For example, the old hybris CMS Cockpit had both parts built with ZK Framework, which orchestrates both the HTML/Javascript “head” and was the essential part of the CMS backend. These parts were tightly connected and intertwined into a single unit called CMS Cockpit.  Later, SAP announced Smartedit, which has been a big bet and a quantum leap for Commerce Cloud’s content management. Spartacus, the open-source Javascript storefront from SAP, well complemented Smartedit. I published the comprehensive reviews of both products in the blog earlier.

There are a number of headless content management systems that are mature enough to efficiently replace the built-in Smartedit CMS. Among them, I would highlight Contentful, Prismic, DotCMS, Cloud CMS, Amplience, and Contentstack as well-established and innovative products with a clear roadmap and good integration capabilities.

One of my recent research projects was aimed at using Contentstack as a headless content management system for SAP Commerce Cloud. This article uncovers the key highlights of the research.  As a result of that work, I also created a data migration tool for moving existing CMS content from Commerce Cloud to Contentstack.  

What is Contentstack? Contentstack is a SaaS headless CMS with an API-first approach that offers the approach known as Content-as-a-Service. It focuses on managing structured content into data feeds that other applications, such as Commerce Cloud, can consume. You can design a content data model interactively or via the Contentstack API and populate the structures with the content interactively or via the same API.

In this article, I pay special attention to the data migration from Commerce Cloud’s CMS to Contentstack. This exercise helped me to see the complexity in all its glory. Because when you design a system from scratch, you can evade the platform’s constraints by designing the structures compatible only with the new CMS. When you need to migrate data, you have to find a solution on how to adapt concepts and data models to get them compatible with Commerce Cloud.