A strong data infrastructure is critical for organizations to harness the power of data and drive business growth. It encompasses hardware, software, and frameworks that collect, store, process, and analyze data. Key components include databases, data warehouses, data lakes, ETL tools, and analytics platforms. A modern data infrastructure includes scalable, high-performance analytics capabilities such as data storage, data processing, analytics tools, and data governance. To build a robust data infrastructure, organizations should decouple storage and compute, leverage cloud platforms, implement a unified data catalog, automate data pipelines, and establish DataOps practices. Real-time data infrastructure analytics is essential for detecting and responding to events in real-time, and key use cases include predictive maintenance, dynamic pricing, fraud detection, and personalized recommendations. A robust data analytics infrastructure enables organizations to make faster, better decisions, optimize operations, and improve customer experiences while reducing costs and risks. Examples of successful implementations include Netflix's processing of over 500 billion events daily, Uber's ingestion of over 100 petabytes of data daily, and General Electric's use of a Hadoop-based data lake for predictive maintenance.