For many specialists in the field, there is little doubt that new technological advances have been boosting organizational markets year after year. This is true both for direct investments in the technology sector and for the indirect impact on other corporate sectors, where digital innovations have contributed significantly to making financial transactions more practical and secure over time.
In this scenario of continuous innovation, new concerns have also emerged among professionals working in negotiations and technology alike. A particular relationship has stood out, presenting itself as a strategic pillar in the pursuit of practicality and security.
Achieving such results increases organizational credibility when this pursuit succeeds. One highlighted path lies in the protection provided by big data pipelines for high-value financial transactions, many of which reach into the billions of dollars.
Another fact supported by recent data concerns the growing adoption of digital platforms for organizational financial transactions. Brazil, one of the largest economies in Latin America and considered an emerging power in this regard, serves as an example.
In 2024, approximately 80% of organizational transactions were carried out through new platforms. Consequently, the use of traditional banking tools for such transactions dropped by about 10%. This trend has been repeating itself in recent years, and expectations are that new records will continue to be set with each annual review—a pattern observed across most analyzed countries.
“When we talk about pipelines, we are talking about a sequence of tasks inserted into these databases. In short, data is extracted from the source and aggregated into another destination. But this is not done directly. Between one point and another, databases go through intermediate layers, where user and organizational needs are taken into account to better structure the path. All of this is built on ever-present pillars, such as the security of this constant and consistent flow of valuable information, as well as other essential and non-negotiable principles for every project, like practicality,” explains Hemanth Kumar Padakanti, senior data engineer and technical leader.
The Indian specialist has accumulated nearly a decade of expertise in the field. Developing data pipelines to safeguard and boost high-value financial transactions is one of the capabilities he has honed along his career.
More Numbers at Play
The rise in digital financial transactions is matched by intense growth in global big data investments across organizations. Emerging technologies such as Artificial Intelligence (AI), cloud computing, and the Internet of Things (IoT) have only strengthened these positive projections—many of which are already reality.
By 2027, investments in organizational big data are projected to reach nearly $200 billion USD. According to Hemanth Kumar Padakanti, pipelines have the flexibility to handle all kinds of data—structured, unstructured, or semi-structured—since organizations store massive volumes of diverse data in their systems.
“These intermediate layers create countless possibilities, such as the so-called ODS layers, formerly known as large ‘tables.’ The information is consolidated and then filtered in a very specific way. Of course, this is a simplified description. Today, resources are far more advanced, and resolutions for each need are delivered efficiently and securely through transformation processes, leading to the desired credibility.”
Career Milestones
Padakanti worked as a technical lead and data engineer at Barclays in New Jersey, USA, for more than seven years. There, he developed real-time data pipelines using Kafka, Spark Streaming, and Hadoop to support critical use cases such as fraud detection and regulatory compliance.
“At Barclays, I developed and optimized real-time pipelines with Spark and Kafka that processed more than two billion transactions, resulting in a 25% monthly improvement in detecting fraud linked to Dark Web activities. This work directly contributed to financial security and fraud prevention, issues of high national priority in the United States. My solutions ensured secure data handling and accurate real-time processing in compliance with global financial standards, while providing scalable models that could be replicated across American banks and fintechs,” recalls the senior expert.
His solutions were regarded as innovative in distributed systems, significantly improving detection accuracy and adherence to financial regulations, as confirmed during organizational strategic planning reviews. He worked at Barclays until May 2022.
Currently, Hemanth Kumar Padakanti connects the worlds of machine learning inference, cloud-native architecture, and observability. At Angi, where he now works, he contributes to the centralized MLOps pipelines team, leveraging tools such as Ray, Seldon Core v2, and MLflow for scalable, automated batch inference projects.
He also introduced real-time monitoring through Grafana and custom observability pipelines. With more than a decade of experience designing and delivering scalable, cloud-native data engineering platforms using AWS, Kubernetes, Spark, Snowflake, and open-source orchestration tools, Padakanti now leads data platform initiatives at Angi Inc., transforming legacy ML and data pipelines into secure, resilient, Kubernetes-native systems.
Another area of his expertise involves safeguarding networks against threats and risks emerging from the Dark Web, the parallel digital marketplace. Working with data distribution to mitigate risks in this risky environment—whether for individuals or organizations—has also become one of Padakanti’s key skills along his career.
The article was written by journalist and corporate communication specialist Mainara Screpanti.
