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Data Performance Optimization
There are a lot of tuning options available when it comes to Data Performance. It is important to choose the right ones based on one's business requirements. Our approach is to first understand the data use cases of our customers and the intricacies of the data itself. Then use the right optimization options that suit best for the business requirements. Prioritize them, apply them one at a time, and measure the performance impact to assess their effectiveness. We have helped our customers save thousands of dollars on new hardware and software by simply optimizing their data performance.
Schema Design
Normalized vs Denormalized schema design plays an important role in data performance. Normalization reduces data redundancy but can lead to complex joins. In some cases, denormalization, where redundant data is introduced, can improve query performance by reducing the need for complex joins. However, denormalization should be used carefully to avoid data integrity issues. A lot of thought needs to be given at the schema design stage as this is the most important element of the data architecture and will be primarily responsible for data performance. Appropriate schema design needs to be selected based on the business use cases. These include Star Schema, Galaxy Schema, Snowflake Schema etc.
Database Indexes
The right amount and type of indexes is the most important factor for query performance. It needs a deep understanding of the domain and data access patterns to decide the right indexes. Properly index the columns frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses. This can significantly reduce the time it takes to retrieve data. Common index types include B-tree, hash, and full-text indexes. One must not overdo indexes, as there is a cost to maintaining indexes. Regularly optimize and maintain database tables and indexes by running vacuum or optimizing commands to reclaim space and improve performance.
Query Optimization
There is a lot that can be done in the realm of query optimization. Choose the appropriate type of JOIN (e.g., INNER JOIN, LEFT JOIN, RIGHT JOIN) based on the data relationships and the desired result set. Ensure that JOIN conditions are well-optimized and use appropriate indexes. Beyond this we can use a query profiler to identify slow-performing queries. Analyze query execution plans to understand how the database processes the query, and make adjustments as needed. This may involve rewriting queries, restructuring JOINs, or using appropriate query hints.
Partitioning
Partition large tables into smaller, more manageable subsets. This can improve query performance by reducing the amount of data that needs to be scanned. Partitioning can be done based on ranges, lists, or hashes.
Caching
Implement caching mechanisms, such as query result caching or application-level caching, to store frequently requested data. This can reduce the load on the database and improve response times for read-heavy workloads. Use the LIMIT or OFFSET clauses to restrict the number of rows returned by a query. This can be especially useful for paginated results.
Case Studies
Enhancing Chat Bot Interactions Accuracy for Healthcare Platform
JashDS enhanced a healthcare platform's chatbot accuracy by 10% by implementing an advanced data ingestion and analysis pipeline, leveraging Azure and Medallion architecture to process 5 GB of daily conversation data and deliver optimized Power BI reports.