Data engineering and visualization are crucial components of modern data-driven decision-making. However, managing and integrating disparate data sources, processing large volumes of data, and automating the entire data pipeline can be challenging. This session will explore how Apache Beam, a powerful open-source data processing framework, can be used in conjunction with Power BI to create a seamless, automated data pipeline for real-time visualization and analysis.
An overview of Apache Beam and its capabilities for data processing and integration
How to connect various data sources and process data using Apache Beam pipelines
Techniques to clean, transform, and enrich data using Apache Beam’s programming model
Integrating Apache Beam with Power BI for real-time data visualization and analysis
Best practices for automation, scalability, and performance in data engineering and visualization tasks
We will discuss a case study of an organization that leveraged Apache Beam and Power BI to overcome data engineering and visualization challenges. The organization had a set of data sources. Traditional ETL processes were time-consuming, expensive, and error-prone, hindering their ability to make data-driven decisions promptly.
By implementing Apache Beam, the organization was able to unify data from various sources, process it efficiently in real-time, and handle both batch and streaming data. This new data pipeline enabled the organization to clean, transform, and enrich their data, ensuring high-quality insights. The processed data was then integrated with Power BI for real-time visualization and analysis, allowing stakeholders to make informed decisions based on the latest information.