You're facing a dilemma between query speed and data reliability. How do you strike the right balance?

- Rewrite queries to be more efficient. - Modify queries based on the insights gained from the execution plan to avoid bottlenecks. - Use caching mechanisms to store frequently accessed data or query results. - Use the appropriate type of join (e.g., inner join, outer join) based on the query requirements and ensure that join conditions use indexed columns. - Only use aggregations when necessary and ensure they are performed on the smallest possible dataset. - Use indexes to speed up retrieval of rows by creating pointers to data in a table

You can make sure your SQL queries are as efficient as possible in a number of ways. Consider utilising temporary tables within your queries instead constantly referencing live tables, doing this can mean that frequently used tables just need to be referenced once in the query to insert the data into a temporary table, using the temporary table from this point avoids traffic on the frequently used table. Inefficient joins (e.g. many to many) can drastically increase the run times of your queries, so should be avoided where possible, explore decreasing the number of rows involved in a join prior to the join itself. Lastly, avoiding using inefficient sub-queries in your where clause, as this needs to be ran for every single row in your query.

To balance query speed and data reliability, I prioritize data integrity while optimizing performance. First, I ensure robust indexing and efficient query plans to enhance speed. For reliability, I implement data validation rules and regular backups. I also use transaction management to maintain data consistency. Continuous monitoring allows me to identify and resolve performance bottlenecks without compromising data reliability. This approach ensures both fast queries and trustworthy data.

1. Label Your Servers: Assign different servers for different tasks. Use one for heavy development work, another for high-speed reporting, and a sandbox for experimentation. 2. Organize by Purpose: Each database should serve a specific function. Keep related data together, and avoid cramming unrelated info into one place. 3. Ditch the Chaos: Ad-hoc databases can get messy. Stick to a well-defined structure so you always know where everything is. Think of it like a well-organized workshop—everything has its place!

Your databases should be organised into a well defined and clear structure, ensure that analysts and developers are aware of what belongs where. Consider designated servers for different purposes, e.g. a development server for resource intensive queries, a reporting server for high performance and a sandbox area for exploration. Specific databases should contain tables and stored procedures for specific purposes, all data in a single database should be related. It's not always possible to, but try to avoid an ad-hoc database, as these can get messy, stick to a defined structure so you know where all your data is.

Split your daily ETL processes between two servers, a reporting server and a development server. Run the resource intensive stored procedures on your development server to start with, then either log ship these to your reporting server or have other designated stored procedures to create reporting tables. Utilise SSIS packages for efficient ETL processes. Approaching daily loads like this will result in at least two copies of your main tables for use, your main reporting tables and your development ones as a backup. Data up to a particular point can also be archived in a separate database as a static dataset as long as it is not expected to change, meaning this can also be utilised in queries looking at historic data.

- A rotation scheme involving daily, weekly, and monthly backups, providing multiple recovery points. - Determine how often backups should be performed based on data volatility and business needs (e.g., hourly, daily, weekly). - Encrypt backup data both in transit and at rest to protect it from unauthorized access. - Use data masking techniques to obscure sensitive data in backups. - Define how long backups will be kept before they are deleted or archived. - Create detailed documentation for backup procedures, including schedules, configurations, and contact information.

Archiving data up to a certain point in static tables is not only good practice, it can make stored procedures run quicker, as historic data can be combined with recently processed data in a union query rather than having to process the entire dataset every time. Data analytics can be unpredictable, servers shut down unexpectedly and data can become unavailable, backing up copies of data can help mitigate some of the issues caused by this. If datasets aren't too large, historic snapshots can be taken and archived in a table, this doesn't necessarily need to be daily, but it can be useful to have retrospective positions available for reporting.

Once your daily ETL processes have finished, checks should be done to make sure all stored procedures ran and tables built successfully, if something failed steps should be taken to address this. You should also keep an eye on what queries are running at the busiest parts of the day, if something is taking too long and slowing down queries in other areas this can then be raised. It is good practice to utilise no locks after your from statements, to ensure frequently updating tables don't get locked and break. Lastly if you have any SSRS reports that cache or Power BI reports that update on a timed schedule, periodically check the report or model has successfully refreshed, if it hasn't then this will need investigating.