Break down that big dataset into smaller, more manageable pieces. You can do this with pagination or batch processing. If you're working with APIs, use query parameters to fetch just a subset of the data instead of grabbing everything at once. Make your database queries snappy by using indices on fields you query a lot. Optimize those join operations and try not to pull in data you don't need. Filter right in the database query to cut down on the data Xano has to process. For heavy-duty data analytics, use aggregations, grouping, or pre-computed views in your database. Pre-computed views can really speed things up by giving you pre-processed data instead of raw data. Take advantage of Xano's background tasks for data processing that doesn't need to be instant. Background tasks let you handle big workloads asynchronously, which helps keep real-time operations quick. Set up caching to store frequently accessed data temporarily. Xano's built-in caching can help reduce the load on your database and speed up data retrieval. Whenever you can, handle data transformations in separate processes or ahead of time. This might include ETL (Extract, Transform, Load) processes that get your data ready for quick access and cut down on real-time computations. Use API rate limiting and throttling to control data flow and prevent your server from getting overwhelmed. This keeps your system responsive during busy times by spreading out the load. Keep an eye on resource usage with Xano's monitoring tools. Find bottlenecks and tweak memory and CPU usage as needed. Adjust your database settings and application configurations to boost performance. Pick the right data structures for your dataset and the operations you're doing. For example, use arrays for sequential data access and hash tables for quick lookups. Use parallel processing to handle multiple parts of your dataset at the same time. This can really cut down on processing time and make things more efficient. Xano's multi-threading capabilities can help with this. Keep your payload sizes small by choosing efficient data serialization formats like JSON or Protobuf. Only serialize the fields you need to minimize data load and parsing time. Regularly archive old data and maintain your database to keep it running smoothly. Set up automated archiving to move older data to cheaper storage, keeping your active database clean and efficient.