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We usually come across situations where we need to move files and folders to another directory say for archival or some other purpose.

This article is aimed to introduce SQL developers to the management of sql transaction with the context of json parameters and nested stored procedures.

When the company’s organization become large and production database servers have hundreds of jobs running with different schedule, the DBA might lose track of what job must be run successfully and in a timely matter.

On very large database tables, most of my clients have decided to archive the data by extracting them first and then purging old data based on a Date column using something like...

Many organizations are now facing with a lot of compliance regulations, from SOCII to PCI, GDPR to KYC compliance and many audits are done to understand how they protect their data from external hacking but also internal employees.

I have been working lately with several companies, within Ecommerce, telecommunications and payments industries and they all had a team that needed to work on looking at last week or last month invoicing and ordering from clients.

Nowadays, business tends to get very big database very quickly and they usually need to find ways to manage functionally and physically data in a more efficient matter, both for security and performance reasons.

All have worked very well. I noticed a performance improvement with an additional non-clustered filtered index on the table and SQL server does build the index very well.

This time, I implemented the same code but with encryption and hashing, encryption with a symetric key for the email addresses and hashing for the dictionary.

You might have wondered if there was a solution that could keep a version of your database in case you/a process made a huge mistake (large update without the where for example) on a production database.

As I was saying in an earlier article, Always Encrypted is a key element of the GDPR compliance implementation: how to encrypt and be able to search the personal data.

Of course, indexed views are schema binding. Schema binding tables via indexed views offer additional advantage in terms of securing the structure of your database.

I have struggled with one client on queries that were dealing with date ranges or type ranges or many filters, on very large tables with low density in many cases, and you knew one index would really improves it but it was based on some filter that was undeterministic.

One of them is a very suffessful Ecommerce that persists the shopping carts (not in cookie) and has millions of users storing in the cart products, shopping between 8AM to 10AM massively.

I wrote this article a few years ago noting a change in paradigm where data was more important than database.