How do you balance cost and efficiency in data warehouse optimization?

Identify the types of data you'll be storing in your data warehouse. This may include structured data from databases, semi-structured data from logs or IoT devices, or unstructured data such as documents and images.

Efficiency is very subjective topic as such . First work with Business Partners to understand SLAs required . Then work with Technology leaders to understand current costs and thresholds to benchmark any improvement . Observe operational reports to understand various workload and process patterns and look out for any major outliers where there is beyond normal resource consumption . Focus on those areas before putting wider Net . Observe for processes which work well during normal traffic , but degrade during peak time workloads . This is an indication , that scalability is an issue . Cache frequently accessed data which avoid unnecessary resource consumption . Compute engines should be only running when needed . Idle compute may be costly

Materialized views can improve query performance by reducing the need for expensive join and aggregation operations at query time.

Before jumping to query tuning you should have conclusive evidence of that and make sure it is not the issue with shared resources . For query tuning first ensure if query performance has a sudden detoriation or it is consistent . Sudden dip could be due to sudden spike in data volume and that case we may need much more than just query tuning . For query tuning first thing you should do is look at query plan and look for any tell-tale signs of issue like , visibly higher cost for a step over others , full table scans when index should have been used, Cartesian products and so forth. Up to date statistics for all objects is essential for optimizer to create most efficient query execution path and that to work consistently , so ensure that

Vertical scaling involves increasing the computing power or capacity of individual servers or nodes within your data warehouse infrastructure.

Scale whenever needed and scale however needed . Vertical scaling will give you more compute power but add to the cost as well . So make the decision on if added power is worth the cost. Horizontal scaling will help with concurrency , during high usage time , horizontal scale will help avoid bottlenecks .Also while scaling don’t scale randomly , but scale using preset scale factor . So that both vertical and horizontal scaling happens in logical increments . When not need scaling down is important . Beyond certain point advantages of vertical scaling will diminish but costs will increase exponentially , so be careful with that .To minimize frequent scaling needs have sepeate compute severs fur different workloads .

Schedule regular backups of your data warehouse and associated metadata, and store backup copies securely in offsite locations or cloud storage.

Automation helps save time, cost and any manual interventions .Automate report and analytics generation , publication and notification to users . Have automated performance monitoring tasks which monitors Compute consumption for the Warehouse and notify in case of any Threshold breaches . Enable alterts and notification and make someone intervene if needed . Automating these infrastructure changes like scaling ensures , that these operations are more organic than sporadic . Automate housekeeping operations like data archival , gathering statistics on data objects and which may have impact on both storage and compute costs. Make Automation even driven , so that unnecessary latencies can be avoided

While cloud provides special types of archive storage it's not so straightforward on on-premise. We have to remember that many companies still run on-premise or hybrid environments. In such cases compression and backing up old, rarely used data that is not impacting your strategic decisions (most likely further than 3-5 years ago depending on data domain) is something to very much consider. Having retention data policy is crucial not to overspent on architecture (server, disk) cost.

With cloud native implementations storage is competitively more cheaper than compute . But still more storage means higher cost . There are multiple ways to control it . Refer to your existing design principles or historical data retention strategy and make sure you retain as much data as needed. Any data older than what is required either should be purged or archived . Archival should be in cheaper storage class. Within the data compression will help reduce overall size of the data . Savings can be achieved using right data types for your data columns.

Use monitoring tools to track performance trends over time and identify any deviations from expected norms. Regularly analyze performance data to pinpoint areas for improvement and optimization.

Data Purging/Archival To maintain cost and efficiency ., history data from high volume “event”/“transactional” tables need to be purged/moved to separate history archival tables periodically . There by loading and analytical/reporting queries will run fast due to less I/O . This will help overall system performance.