I believe that's the ideal scenario that everyone wants to achieve. Okay, let's achieve it together. First of all, try to use columnar file formats such as 'Parquet' which are optimized for reads. Also, try to adapt Delta Lake which can add ACID properties to your data without any performance downgrade. Do you know the best part is? You can further optimize your data by applying ZYORDERBY commands on top of your Delta Tables. And, ALL SET!

?? Data Partitioning: Break large datasets into smaller segments based on criteria like timeframes. For instance, partitioning a sales dataset by month can speed up queries by reducing scanned data. ?? Strategic Indexing: Implement indexes on frequently queried fields, such as user IDs. This enhances retrieval speed but requires balance, as too many indexes can slow down write operations. ?? Cloud Storage Solutions: Use scalable cloud platforms (like AWS) to handle varying data loads, ensuring optimal performance during peak times. ?? Query Optimization: Regularly analyze and refine SQL queries to minimize data transfer. ?? Performance Monitoring: Utilize tools like Grafana to track performance metrics and identify bottlenecks proactively.

To optimize storage and query performance with limited resources, I’d start by implementing data partitioning to break large datasets into manageable chunks, speeding up queries without using excessive storage. I’d also use indexing wisely—focusing only on key columns to improve search speed without significantly increasing storage costs. Leveraging cloud solutions like Amazon S3 or Azure Blob Storage allows for cost-effective, scalable storage. Additionally, services like AWS Athena or Azure Synapse can help execute efficient, serverless queries, balancing performance and budget. This strategy maintains efficiency while respecting resource constraints.

When dealing with limited resources for data storage and query performance, the goal is to be smart with storage and fast with queries. In AWS, I’d partition the data based on key fields like date or location, so the system only looks at the relevant part of the data, speeding up queries. I’d also use compressed file formats like Parquet to save space without slowing things down. Along with That buketing can be better option for key column which you are querying frequently. Processing only new or changed data (instead of everything), we save time and resources.. This keeps everything running efficiently while keeping costs low.

To optimize our data storage and query performance, I've implemented a multi-pronged approach. I've carefully analyzed our data usage patterns and prioritized data that requires frequent access. I've leveraged data compression techniques to reduce storage costs and improve query performance. Additionally, I've optimized database indexes and query execution plans to ensure efficient data retrieval. By combining these strategies, I've successfully managed to balance data storage and query performance, even with limited resources.