![]() ![]() You can use Azure backup, via recovery services vault to configure backups on multiple servers. Having Azure automatically handle the backups, gets you one step closer to Platform as a Service whilst still retaining the flexibility or running a full SQL Server instance.Īutomated backups for SQL Server 2016+ 3. This makes it unsuitable for our scenario here but if all you need is 30 days or fewer, then this is a great option. Whilst there is some customisation available, you can’t retain your backups beyond 30 days. If you have SQL Server running in Azure VMs, you can opt to have them automatically backed up by the SQL Server IaaS Agent Extension. Your data may take several hours to be available when you make a request so it’s not suitable for any disaster recovery scenario but providing you don’t mind waiting, you will cut your Azure bill substantially. If you’re holding on to backups for regulatory purposes or to adhere to company policies but won’t be using them regularly, the archive tier provides great value for money. There are huge savings to be made by moving storage to the archive tier. The rules are fairly customisable so this a basic option which works well without too much configuration. You can either delete files or move them to a cheaper storage class. Lifecycle management rules are a simple way to manage files within blob storage. There are four main ways to actively manage data retention in order that you optimise your cloud costs, without breaching your data retention policy. So if we optimise the storage to only keep the full and t-log backups that we need, and we use cool and archive storage correctly, we can save over £6,000 per month. Costs with no planned retention policy TimescaleĬosts with a planned retention policy Timescale On top of that, daily full backups for the previous 3 months and a weekly full backup for between 3 months and 7 years must be kept.įor simplicity, lets assume an average full backup size of 25GB and a log backup size of 100MB. Your company policy only requires point in time restore functionality for 2 weeks of data. Multiply this for 7 years or more and the costs can get very expensive. This multiplies to 1,820 full and 174,720 t-log backups over 52 weeks. This means in 1 week, you will have 35 full backups and 3,360 transaction log backups. You also take log backups every 15 minutes as each database is in full recovery mode. You take daily full backups of each database on your instance. You have 5 databases on a SQL Server Instance. Whilst blob storage can be reasonably cheap, if you keep every backup with no retention policy, the costs will soon escalate. This advice is to specific on-premises or IaaS backups which are going directly into an Azure blob storage account. In this post, I look at how to cut costs by implementing an Azure IaaS SQL Backups retention policy and ensuring you are not paying for more than you need. It’s so easy for costs to escalate in the cloud, and being proactive is the best way to optimise your costs, and make sure you aren’t spending more than you need to. Remember to test your backups regularly to ensure that they are working correctly and that you can restore data when needed.This post is part of a planned series on 7 ways for data professionals to save money in Azure. ConclusionĪutomating SQL database backups using Python is a great way to save time, reduce the risk of errors, and ensure that data is always protected.īy following the steps outlined in this article, you can easily automate SQL database backups and schedule them to run at regular intervals. We first connect to the database, create a backup, save backup details to a CSV file, and then disconnect from the database. In this script, we have combined the previous code snippets into one script, making it easy to automate the backup process. # Create a DataFrame object from the backup detailsīackup_df = pd.DataFrame(data=backup_details)īackup_details_file = os.path.join(backup_dir, 'backup_details.csv')īackup_df.to_csv(backup_details_file, index=False) Here is an example code snippet that connects to a SQL Server database: import pyodbcĬonn = nnect('DRIVER= We will use the pyodbc package to connect to the database and execute SQL commands. The first step in automating SQL database backups is to connect to the database using Python. Step 1: How to Connect to the SQL Database The pandas package (for working with data).The pyodbc package (for connecting to SQL databases).Prerequisitesīefore we get started, you will need to have the following installed: In this article, we will explore how to automate SQL database backups using Python, making the process faster, easier, and less error-prone. It's a critical task that helps ensure that your data is always protected.īut manually backing up a database can be time-consuming and error-prone, especially if you have multiple databases to back up. You should back up your SQL database on a regular basis. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |