How can you design a relational database for real-time reporting?

Use apace Pinot that should do everything. Apache Pinot is a real-time distributed OLAP (Online Analytical Processing) datastore designed for high-performance analytics. It's particularly well-suited for scenarios where low-latency queries on large datasets are essential, making it a good choice for real-time reporting.

Relational database real time reporting - Understand the types of reports needed by business - Understand the latency requirements - Build a dimensional model - star schema preferably unless the needs require snowflaking - Load data from Operational systems to the model - Ensure to pick CDC data only from OLTP to OLAP systems to have a near real time ETL process - Usage of proper indexes to help in data selection - Build pre defined aggregates based on users needs and store in a seperate table - Use tools like power BI to enhance self service BI for users

Designing a relational database for real-time reporting involves key considerations: Data model, normalize data, indexing, composite indexes, data partitioning (e.g., by date or region), consider data warehousing for large-scale reporting. Implement real-time data ingestion (e.g., streaming), choose reporting tools (Power BI, Tableau), optimize SQL queries, ensure data security (access controls, encryption), plan for scalability, set up monitoring and maintenance (backups, index maintenance), test thoroughly, and maintain documentation for the schema, queries, and reporting processes.

Optimizing your data model involves normalizing data to reduce redundancy and denormalizing for frequently used data. Choose appropriate data types and minimize complex relationships. For query optimization, use indexes wisely, write efficient SQL queries (avoiding SELECT * and functions in WHERE clauses), monitor performance, and utilize database-specific optimization features. Employ data partitioning, maintain indexes, and perform regular maintenance like updating statistics and purging old data. Use query analyzers, application caching, and monitor the hardware. Continuously refine to ensure optimal performance as needs change.

Monitoring and testing database performance is crucial for maintaining optimal operations. Monitor through tools like SQL Profiler and database dashboards, tracking metrics such as query execution time, resource usage, and system health. Set up alerting to respond quickly to issues. For testing, perform load testing, stress testing, and benchmarking to assess how the database handles varying workloads. Use tools like JMeter or Apache Benchmark. Continuously fine-tune based on test results and real-world monitoring data to ensure efficient database performance.

Designing a relational database for real-time reporting involves careful consideration of the data structure, performance, and scalability. Here's a high-level outline & major points that we need to consider while creating real-time streaming pipeline which involves Databases 1. Identify the Data Requirements 2. Define the Data Schema: 3. Choose the right Database 4. Data Ingestion 5. Indexing and Optimization 6. Real-Time Updates 7. Reporting Layer 8. Security and Access Control: 9. Scaling and Redundancy 10. Monitoring and Alerting 11. Data Retention and Archiving 12. Backup and Disaster Recovery 13. Testing 14. Documentation 15. Ongoing Maintenance