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In our last article, we learned how to implement a component control loop in TiDB Operator. This time, I’ll move on to a new but important topic: backup and restore.
Backup and restore are two of the most important and frequently used operations when you maintain a database. To ensure data safety, database maintainers usually need a set of scripts that automatically back up the data and recover the dataset when data is corrupted. A well-designed backup and restore platform should allow you to:
Summary: "Apple Inc. provides cloud storage in every Apple device. This cloud computing service is known as iCloud. Unfortunately, Everyone gets only 5GB of cloud storage for free. Therefore, we need to manage this free cloud space effectively. This article will help you understand how to backup iCloud to an external hard drive and why we need to backup iCloud on an external hard drive?"
iCloud securely stores photos, files, notes, contacts, and more in the cloud space. It keeps your data secure in iCloud and allows you to access the data with any of your Apple devices. However, this will cause your iCloud space out of storage as you have only 5GB of storage space available in it. Hence, you have to backup iCloud to an external hard drive to utilize your iCloud space efficiently.
One of the key challenges for Kubernetes Day 2 operations is observability, i.e. having a holistic view of your system’s health. This is where BotKube helps to improve your monitoring experience of your Kubernetes clusters by sending notifications to supported messaging platforms. BotKube helps you solve several interesting use cases, for example, monitoring Velero backup failure or certificate issue/expiry status by cert-manager, etc. In this blog, we will configure BotKube to monitor your Velero backups and restores.
BotKube is a messaging tool for monitoring and debugging Kubernetes clusters. BotKube can be integrated with multiple messaging platforms like - Slack, Mattermost, or Microsoft Teams to help you monitor your Kubernetes cluster(s), debug critical deployments, and gives recommendations for standard practices by running checks on the Kubernetes resources. — BotKube website
Regardless of the size of your company, data backups and system recovery need to be a critical part of your IT operations and planning. Without a proper backup plan or recovery techniques put in place for when an IT disaster strikes, you are signing off on your business’s failure. Today, many technologies encompass the backup realm, and AWS has its own in-house feature to help you automatically back up and recover multiple other services too; AWS Backup.
AWS Backup is a fully-managed and automatic backup service first released by Amazon Web Services in January 2019 and was updated earlier this year. With this policy-based service, it is possible to automatically backup data from multiple AWS services in your cloud environments as well as your on-premises servers with the additional help of AWS Storage Gateway.
Backing up or restoring large-scale distributed databases is time-consuming. When it takes a lot of time to backup or restore a database, Garbage Collection might break the snapshot used in the backup or restore process. Thus, some changes might be missing. This threatens data safety.
As an open-source, distributed SQL database, TiDB fulfills the requirement for backing up and restoring large-scale clusters. TiDB 4.0 release candidate (RC) introduced Backup & Restore (BR), a distributed backup and restore tool, that offers high backup and restore speeds—1 GB/s or more for 10 TB of data.
Backups are one key to a successful disaster recovery plan. Every database engine has its own backup commands and procedures, and Microsoft SQL Server is no exception. SQL Server has capabilities for full and differential backups as well as a backup process for transaction logs. These procedures can be used in combination to ensure limited downtime should your database suffer from an outage or critical, unrecoverable crash.
Before creating a backup, it's important to know the different types. There are three types: full, differential, and incremental. SQL Server supports full and differential backups, but some administrators incorrectly call differential backups "incremental." There is a distinct difference between the two, however, and it affects the way databases see backup data.
Have you ever felt a moment of panic when you realize that your data might be lost? Human errors, malicious attacks, systems crash, or disasters happen when least expected. So it is highly important to always be prepared for such situations by keeping up-to-date backups that can help with data recovery. Automated backups ensure in-time refreshment of the data copies and eliminates the need for manual tracking and setup. In this article, we’ll examine how to create a backup schedule and offer hints for better data scheduling and restoring.
You might also like: PostgreSQL Backup and Recovery Automation
Jelastic PaaS provides a convenient and straightforward script for backing up the stored data on the MySQL and MariaDB database nodes. Thus, the process is as simple as providing the required parameters and can be accomplished in a matter of minutes. No specialized knowledge is needed — just follow the setup steps to configure the appropriate cron expression.
A critical PostgreSQL client contains valuable data, and PostgreSQL databases should be backed up regularly. Its process is quite simple, and it is important to have a clear understanding of the techniques and assumptions.
The idea behind this dump method is to generate a text file from DataCenter1 with SQL commands that, when fed back to the DataCenter2 server, will recreate the database in the same state as it was at the time of the dump. In this case, if the Client cannot access the primary server, they can have access to the BCP server. PostgreSQL provides the utility program pg_dump for this purpose. The basic usage of this command is: pg_dump dbname >backupoutputfile.db.
As part of an application lifecycle, we are periodically required to refresh our non-production (dev/test) environments with production data. This helps us test applications with the right data and ensures we do not leak any obvious defects. It also enables us to test for performance of our application, as we will have the same quantity of data as in production. Further, testing on real data is bound to inspire confidence on an application release.
But copying live data increases the risks and the exposure of confidential information. A non-production database is likely to be accessed by developers and business analysts who may not have the same access in a live environment. They might only be interested in testing a feature but should not be exposed to the confidential information in the live system itself. To reduce such risks, data needs to be anonymized. i.e., personally identifiable/confidential information is removed or replaced with dummy values.