Venkata Chaitanya Jonnalagadda

Hi, my name is. I work in SQL, Lambda SQL, Python SQL, and other.

So I utilized T SQL in complex SQL queries by trying to understand the ask first and then create a solution in my mind and try to implement that with T SQL. It could be incorporating functions or different procedures while solving complex problems.

So to optimize this equal query that is running too long, I would see to, I would first try to see the cost where exactly the query is taking long, actually. So I would run the stats to see where the cost is getting more. And then I try to gather the stats or try to fine tune the query by removing the redundant checks and filtering the records that are needed and stuff retrieving by giving select star. So I will try to pull the required fields for the query, and I would use subqueries instead of in because in will take a lot of time in also in joins also. So wherever the cost is more for the or the time is taking more for the.

For retrieving data from multiple tables in the database, I would do a join on the tables that have the data, and I will only pull the required fields that are necessary to be populated in the business report. This is how I handle data extraction by collating data from different table sources and pulling it out into the report, so I can do it with a SQL query by doing a join. And also I can do it with ETL by joining two forces to a flat file, which is a file source for the report.

Designing a SQL database for an application that needs to handle large-scale data, which requires high read-write throughput, is first, I would allow concurrency control to be limited. So, basically, multiple read options will be there. If there are going to be multiple operations, then I would allow concurrency for multiple sessions by the same or different users to perform read and write operations at the same time, simultaneously without any stoppage or blockers in the way. So, I would also design a highly scalable database by taking offerings that would help perform queries faster, and I would incorporate tuning queries while creating the tables itself and try to optimize the runtime by writing efficient scripts for creating the tables and objects within a database.

Some of the issues that we might encounter while designing and implementing a SQL database is to understand the scalability of the database, how much memory the database could use, and how many users or sessions can the database handle. So these all things come into mind when these all issues might arise when we start designing and implementing a SQL database. And I would resolve the issues by creating the database objects that are basically efficient by trying to utilize the memory with less memory as possible and being cost-effective while trying to run the SQL queries. So this can be achieved by creating the tables and database objects and other database objects such as stored procedures, functions, and all with efficient scripts, actually.

So we can optimize the query by doing a select count. Instead of star, we can give the select count of a particular single field, which will equate to the same count that we are really looking for. So we can do a select count of field name from customers where country for US entity equals New York. By doing this, by eliminating the star, we try to pull less records, which will give the same count as that which we get while giving a count of star, or we can use a count of 1. That will solve the problem.

Can be made. So, doing a select start from orders where order ID equals 1 to 3. So we can try to pull the particular fields instead of giving a start, which would help improving the snippet and thereby

So the process which we would take to migrate a database from one to another is to do a cyclic check by transferring the database from one to another. So basically, we employ a pluggable database, which will ensure to transfer data from one to another. Since it's a large SQL database, we can transfer data from one to another using a pluggable database, which will ensure that the downtime is minimal since the original database from which we are transferring will still be up, and there'll be no downtime or restart of the services. So using a pluggable database can solve the problem.

In the event of a SQL database failure, the steps we would take to restore the system by minimizing data and downtime loss is to take backups while ensuring we do the data transfer beforehand. So, basically, when we deploy a pluggable database, we simultaneously take a full backup on the backup drive, which would help us to restore the data in case of any unexpected failures, and it will minimize data loss and downtime.