Big Data projects are no longer special projects that run once. Businesses now run analytics programs every day, searching for insights and competitive advantages. As analytics are now integrated into normal business functions, keeping that data secure, backed up, and available for future use becomes critical.
Big Data Backup Challenges
Big Data presents a number of challenges to traditional backup processes. The distributed file systems that store this data don't fit well with legacy tools, and the size of the data to be backed up means that time and space to store backups can be major issues.
Yet backup can't be ignored. While distributed file systems have built-in redundancy and hold multiple copies of data, this is not the same as having a backup. While you won't lose a file due to a device failure, all replicas hold only the current version of the file, including any corrupted data or accidental deletion of data.
Storing the data in the cloud also doesn't provide the needed backup functionality. This simply provides another replica of the file, in its current form. Unless you have a plan, there are no historical versions that can be restored in case of corruption or deletion.
You can't rely on snapshots, either. First, snapshots reside on the data nodes, so a disk failure will lose both the original data and the snapshot. Second, while snapshots keep a copy of data, restoring data from them is a complex, manual process. Big data snapshots are based on files and may not contain necessary schema definitions.
Recognizing that, you can attempt to write your own backup scripts for big data, but this isn't an easy task. The scripts need to be tested and maintained as new big data platforms come into use or existing systems increase in scale.
Many users rely on open-source big data tools, but if you use a commercially supported product, it may come with a backup tool. These tools are typically limited and aren't effective support for meeting your recovery objectives.
To effectively back up and recover big data, you need a vendor-supported backup tool with features that overcome the big data backup challenges.
Effective Big Data Backups With NetBackup
You can workaround some of the big data backup issues with vendor products by backing up one node in a cluster only, but that node then becomes a bottleneck. NetBackup provides a mechanism that enables efficient backup and recovery of Hadoop big data clusters. With the NetBackup Parallel Streaming Framework, data is backed up rapidly and scalably, without the bottleneck, and Automated Discovery avoids backing up redundant data to minimize the time needed to backup.
Contact dcVAST to learn about our managed NetBackup services that ensure NetBackup safely backups up all your data and makes it available for recovery. Whether your clusters support big data, hyperconverged infrastructure, or other workloads, we ensure the reliable NetBackup technology keeps your data protected.