These days, logistics companies face growing pressure to move faster, deliver smarter, and respond to disruptions in real time. In 2025, the industry is undergoing a major shift, and big data analytics is at the heart of it. More and more companies are using advanced data tools to forecast demand, optimize inventory, and manage risks proactively. As a result, they get faster deliveries, fewer delays, and better customer satisfaction. Behind it all is the need for a strong, flexible data infrastructure—the kind that can turn raw information into insight, and insight into action.
To understand how companies are turning raw logistics data into business-critical decisions, we spoke with Dmytro Verner, Senior Software Engineer and cloud infrastructure specialist, a senior member of IEEE, and a member of the community of innovators AIFN. In his career, Dmytro has played a key role in developing a cloud-based data lake and helped automate insight delivery across their operations. His architecture integrated AWS services, Apache Spark, and Terraform to support one of the world's most demanding real-time supply chain environments.
We spoke with Dmytro Verner to understand how the system was built, why it stood out, and how infrastructure can fuel strategic foresight.
Dmytro, with big data and real-time analytics becoming core to how supply chains operate, how does a robust data lake architecture help logistics companies move from raw data to actual decision-making?
In logistics, you're not just dealing with a lot of data. You're dealing with it constantly and from dozens of sources: sensors, APIs, shipment updates, port statuses, etc. A data lake gives you one central place to bring all of that together in raw form, whether it's structured, semi-structured, or unstructured. What makes it powerful is that it's flexible: you don't have to force everything into a predefined schema. That means analysts, engineers, and even machine learning systems can tap into the same data pool to generate insights in real-time. You're not just storing data; you're creating a system that thinks with you, always ready to guide the next move.
Many logistics companies today are trying to build data architectures that "think with them"—systems that can grow, adapt, and deliver insights on the fly. Based on your experience, what key principles or tools would you recommend to anyone designing a scalable, real-time data infrastructure today?
The main things to get right are flexibility and scalability. First, it's important to store raw, unstructured data in a centralized repository—this makes it easier to integrate various data sources, including IoT devices, transaction logs, and sensor data. A scalable data lake built on platforms like AWS S3, Google Cloud Storage, or Azure Data Lake ensures the system can grow without friction as data volumes increase.
Using a schema-on-read approach is another core principle. Instead of defining a strict structure upfront, the data remains unstructured until it's needed for analysis. This gives the system more agility, especially when working with diverse or evolving data types.
For real-time capabilities, I recommend tools like Apache Kafka or AWS Kinesis to stream data directly into the lake, enabling immediate analysis. And none of this works well without strong data governance and security practices. These are essential to maintain compliance, ensure data integrity, and support sustainable scaling over time.
The changes you made led to a more than 45% reduction in AWS costs and doubled the system's data processing speed. What made such a dramatic performance and efficiency improvement possible?
It really came down to using our resources more wisely. Some of our older processes were running oversized EC2 instances, big, expensive servers that stayed active longer than needed. We migrated many of those tasks to optimized EMR clusters because they are more efficient systems that automatically shut down when they are done. We also reorganized how our data was stored and accessed—for example, grouping logs by time and combining lots of tiny files into fewer, larger ones made everything run faster. One surprising fix was removing temporary data that wasn't actually being used—some processes were generating huge files that just sat there, wasting space and money. Each change on its own was small, but together they made the system much faster and significantly cheaper to run.
As you were building this, what challenges did you face—technically or organizationally—and how did your team solve them?
One big challenge was keeping data consistent when it was coming in from so many different sources with different standards. We had to build a strong validation layer right at ingestion to catch anomalies early. Another issue was scaling—not just the infrastructure, but the team's ability to work with it. That meant building internal tooling, dashboards, and alerts so teams could monitor pipelines without needing to dig into code. And of course, compliance and security were critical. We worked closely with AWS to achieve SOC 2 compliance (a security standard that ensures systems are managed securely and protect customer data) and earn official AWS Partner certification. That meant putting strict access controls in place, encrypting data, and keeping detailed audit logs. It wasn't always easy, but it gave us a solid and secure foundation to build on.
Once the system was live, how did it change the way TransVoyant's clients—many of them Fortune 500 companies—made decisions in their logistics operations?
The most noticeable change was in how quickly and confidently decisions could be made. When teams have access to current data rather than delayed reports, their planning becomes more dynamic and informed. Instead of reacting to problems after the fact, they're able to anticipate disruptions, assess potential risks, and adjust accordingly. For example, if a weather disruption threatened a key shipping lane, the system could flag at-risk shipments, suggest alternative routes, and even estimate delivery delays. Whether it's choosing alternative routes or rethinking delivery schedules, timely insights empower more proactive and efficient operations.
Looking ahead, how do you see data lake technology evolving, especially in the context of real-time decision-making and AI integration?
I think the next step is even tighter feedback loops. Right now, we've made it possible to go from raw data to insight in near real time. The future is about acting on those insights automatically—event-driven architectures, autonomous optimization loops, things like that. I also see more cross-layer integration with AI: models trained directly on lake data, retraining pipelines triggered by new inputs, and so on. Personally, I want to keep building systems that don't just store and process data but actually help people—and companies—make better decisions faster.
