Rubrik customers have an advantage when it comes to mitigating the log4j crisis. Utilizing our API-first architecture, Rubrik customers can make a single API call to find instances of log4j across their protected systems.
You run many of your business-critical applications on Nutanix AHV. Protecting your data from cyberattacks and being able to quickly restore your applications, so your business can continue as usual. This is why, at Rubrik, we continue to innovate and expand protection for virtual environments, and we are humbled and proud to be a trusted partner when it comes to protecting businesses.
Microsoft SQL Server and Oracle Database play a critical role in almost every IT environment. That’s why from the beginning, Rubrik has focused on delivering a single platform that converges data protection and data security to ensure you can rapidly and reliably recover when disaster strikes. Our initial release automated the backup of databases with a single SLA policy engine that enabled customers to automatically protect their Microsoft SQL Server and Oracle databases as they were created.
SAP HANA is often a database of choice for large enterprises looking to drive more significant insights and quick decision-making through real-time or advanced analytics. As a result, SAP HANA has become key to driving day-to-day operations for many organizations and must be protected to guarantee maximum availability. However, managing this protection is no easy feat.
Since January 2020, Microsoft Azure SQL DB has grown nearly 3x in popularity. This rapid growth comes as no surprise as Platform-as-a-service (PaaS) offerings have many benefits in the migration strategy from traditional SQL Server database workloads. The Microsoft PaaS offering, Azure SQL Database, takes care of mundane tasks once performed by Infrastructure Engineers such as patching operating systems and applying SQL Server updates or critical security patches.
Following up on our Simulating Filesystem Tree with Billions of Files series, let's imagine that we want to create a filesystem with 1 billion inodes in memory. At this scale, every single byte used to represent the inode will use 1GB of memory. Just with the inode numbers, each with 8 bytes, will use at least 8GB of memory. If we consider, conservatively, that each inode will use approximately 50 bytes of space, it will not be feasible to store this information in memory.
