Utilize a distributed architecture to allow processing workload to be distributed among numerous nodes or servers Utilize efficient data compression techniques to: Reduce storage requirements. Increase query performance. Leverage: Modular storage systems Distributed file systems Cloud-based infrastructure Analyse, optimize queries on a regular basis while implementing data replication Combination of data warehouses and data lakes, known as,"lakehouse" architecture, brings the best of both worlds Removal of old KPIs is crucial to keep pertinent data and performance up to the mark Review the size of the database and configure settings frequently to maintain the data warehouse Refresh indexes once in a while, clear historical data

To boost data warehouse performance while maintaining scalability, focus on optimizing data models (use star/snowflake schemas), partition large tables, and use columnar storage. Scale resources vertically (upgrade hardware) or horizontally (add nodes), and implement caching for frequently accessed data. Optimize queries, archive old data, and use parallel processing for faster workloads. Leverage cloud services for elastic scaling and automate monitoring to continuously improve performance.

Key Techniques : 1. Data Partitioning: Partition large datasets into smaller, manageable segments to improve query performance and data access. 2. Indexing: Use indexing to speed up query performance by allowing faster data retrieval. 3. Query Optimization: Optimise SQL queries by using efficient joins, avoiding subqueries, and minimizing data retrieval. 4. Data Compression: Use efficient data compression techniques to reduce storage requirements and increase query performance. 5. Monitoring and Maintenance: Regularly monitor system health to proactively address bottlenecks or issues. 6. ETL Optimisation: Streamline ETL processes to ensure timely data loading and transformation.

1. Scalability from the beginning, design your DWH with scalability in mind from the beginning. 2. Implement robust ETL process that can handle large volume of data efficiently. Consider using parallel processing and incremental loading to improve performance. 3. Use proven data modeling techniques to optimise data retrieval and ensure efficient query processing. 4. Choose the right storage and compute platform, that supports scalability. 5. Maintain high data quality and consistency by implementing robust data validation and quality checks. 6. Continuously monitor the performance of your DWH and optimise queries and processes as needed. By following these strategies, you can build a DWH that is both scalable and high performing.

Dimensional Modeling: Implement star or snowflake schemas to simplify queries and improve performance. Partition Large Tables: Break large tables into smaller, more manageable pieces based on criteria like date or region. Indexing: Create indexes on frequently queried columns to speed up data reading.