To keep up with business demands and to innovate with customer-centric applications, IT teams are always looking for modern tools that provide increased developer agility, and increased productivity of DBA and infrastructure teams — all while maintaining highest order of data recoverability. From the first day of Datos IO and with the release of RecoverX 1.0, we have focused on building data management capabilities for modern and cloud-native applications including  delivering a full featured and full fidelity backup and recovery product. While doing so we have invested in product capabilities of orchestration, granular protection, and automation. RecoverX delivers fully automated, application consistent, any point-in-time backup as well as granular, repair-free recovery. The result is highest order of backup storage efficiency for modern applications and distributed databases (NoSQL, Cloud Databases, etc.) running on-premises or on multiple public cloud platforms.

RecoverX 2.5, the newest version of our flagship, award winning product, incorporates feedback from our customers to now offer advanced recovery features that deliver unparalleled levels of recovery performance for broad set of data management use cases: operational recovery, test/dev, DR, and more. With RecoverX 2.5, we have enhanced our recovery features in several ways with a common objective of reducing recovery times (RTO’s), giving application and data owners tighter control of what data gets recovered, and reducing the infrastructure requirements for recovery. The new advanced recovery features in RecoverX 2.5 include:


Restore only what I want – Query-able Data Recovery!

When needed, DBA teams and IT administrators often need to view their backup data, inspect it, and then recover their data back as rapidly as possible – logical errors and failures are a norm and backup is a must have for every organization to run today’s high velocity, customer centric, data driven companies.   With query-able restore capability, customers can now selectively restore specific data from their backup data, e.g. choosing specific  records from a table. With query-able data recovery you can create select criteria to query specific columns and rows from backups. The result is flexible sub-table level recovery. The query criteria can be selected through a streamlined GUI.  Click on the columns to restore and define conditions to select specific rows to include in the restore.  For those who want to automate query-able restore, the capability can also be accessed via CLI and API — thus enabling customers with API based automated recovery policy frameworks.

Query-able restores results in key benefits including:

  • Reduced recovery time and storage required in target clusters by restoring just the data that is needed.
  • Support for data privacy (e.g. HIPAA, GDPR) regulations or initiatives with the ability to remove columns of sensitive or non-essential data from restores.
  • Non-Recovery Workloads – RecoverX already enables restore of data to any other data source cluster, with the same or different topology. With RecoverX 2.5, data owners can now restore query selected data for Test/Dev/QA, without having to restore entire tables providing them additional flexibility when restoring to Test/Dev/QA clusters that are often configured with less capacity than production clusters. Now granular subsets of data, that are a subset of the size of full tables, can be restored to these alternate clusters. Likewise subsets of data can easily be restored for analysis, transformation or other downstream uses.   


I want my data within a specific time range – Incremental Data Recovery!

With incremental data recovery, DBAs and IT  administrators can select specific time ranges of data to restore.  By choosing a time range, a time based subset of any table can be restored into any database cluster, the original or a different cluster.    Use cases include restoring time specific data for test/dev and  recovery from data corruption.

The customer benefits are similar to query-able restore but based upon time, specifically:

  • A subset of a table can be recovered faster and consumes less cluster storage on the production sources or test/dev sources.
  • An incremental time range restore can be used to recover from specific data corruption without having to restore an entire table, thus resulting in faster RTOs! For example, you can now selectively restore only the last day of changed data from a production table to test/dev table without restoring the entire table every day.

Big Data Recovery Needs Streaming recovery!

Data sets, particularly big data, frequently involve very large restores (in TBs) which can be slow and require considerable storage infrastructure. With our latest streaming recovery capability, restores are streamed to the target data sources / clusters, requiring minimal temporary storage requirements. In addition, streaming recovery adds granular data checkpointing during recovery. If a restore is interrupted as a result of network or other infrastructure issue it will continue from the most recent checkpoint without reverting to the beginning of the restore. The benefit is elimination of temporary or restore staging capacity on data sources and increased failure handling during restore operations.

For additional insight on all of these new features check out our new RecoverX 2.5 product page and register to watch product webinar.  You can also check out all of our customer case studies here.

If you’re ready to try Datos IO RecoverX for yourself, simply register here for a free trial. Take The Tesla Of The Backup on a ride on Amazon Web Services (AWS) platform, all on us!