This study explores key techniques employed in modern data warehousing for big data analytics, including columnar storage, data partitioning, and sharding. Data warehousing techniques are fundamental to managing and analyzing big data effectively. As data volumes grow exponentially, traditional data warehousing approaches must adapt to handle scale, complexity, and diversity. Columnar storage optimizes performance by organizing data by columns, enhancing query speed and compression efficiency. Data partitioning and sharding distribute data across different storage segments or servers, enabling better performance, scalability, and fault tolerance. Distributed processing frameworks, such as MapReduce and Apache Spark, facilitate parallel data processing, addressing the need for handling large-scale datasets efficiently. Integration with data lakes provides a complementary approach by storing raw, unstructured data alongside structured data in data warehouses, allowing for more flexible and comprehensive analysis. Real-time data processing technologies, like Apache Kafka and Apache Flink, enable immediate insights and actions, crucial for dynamic business environments. Schema-on-read approaches allow for the storage of diverse data types without predefined schemas, offering flexibility and ease of integration. Data aggregation techniques, including materialized views and data cubes, enhance query performance by pre-computing and storing summarized results. Indexing and optimized query processing further improve data retrieval efficiency. Data governance and metadata management practices ensure data quality, compliance, and effective utilization, while cloud data warehousing solutions offer scalability and cost-efficiency. Finally, integrating machine learning models within data warehouses provides advanced analytics capabilities, driving predictive insights and personalized recommendations. These techniques collectively enable organizations to harness big data's full potential, transforming it into actionable intelligence for strategic decision-making.