Hey! My name is Saeed Barghi. Born in Tehran, Iran, I work for one of the most prestigious consultancies in Melbourne, Australia. I started my career as a .NET developer and soon moved into Business Intelligence and Data Warehousing.

I have a deep interest in working with different aspects of data and information management, and I have shifted my focus toward Big and Streaming Data and everything that surrounds this exciting field.

On this blog, I write about my experiences on the job — the problems I face and the ways I work around them. Hopefully these notes help others navigating similar challenges in the data engineering and BI space.

In the modern data landscape, the ability to process and analyze information in real time has become a critical differentiator for organizations of all sizes. Streaming architectures built on event-driven foundations allow businesses to ingest, transform, and act upon data the moment it is generated. This shift from batch-oriented processing to continuous, low-latency pipelines enables use cases ranging from IoT sensor monitoring to fraud detection and personalized customer experiences. The ecosystem of tools and platforms supporting these architectures continues to mature, offering more accessible entry points for teams looking to adopt streaming workflows. Understanding how to design, deploy, and maintain these systems is essential for any data professional working with modern analytical infrastructure.

Data quality and governance remain foundational concerns even as processing technologies evolve. Whether working with structured tables in a data lake or semi-structured streams from distributed sources, the integrity of the information flowing through a pipeline determines the reliability of downstream analytics and decision-making. Implementing robust validation, schema management, and monitoring practices helps ensure that data remains trustworthy across its entire lifecycle. As organizations increasingly treat data as a strategic asset, the roles of data engineering and architecture become more tightly integrated with business outcomes. Professionals who combine technical depth with a clear understanding of data management principles are well positioned to drive meaningful results in this fast-moving field.