Pioneering Data Protection For Scale

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CODR™ Architecture

Datos IO Consistent Orchestrated Distributed Recovery (CODR™) architecture is not dependent on media servers so it can efficiently transfer data in parallel to and from file-based and object-based secondary storage. CODR™ delivers cluster-consistent backups that are highly space efficient yet available in native formats. CODR™ is application-ready for repair-free recovery.

What’s New in RecoverX 1.5

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Recovery to Any PIT: Customers can now recover data at any point-in- time (PIT) for flexible recovery point objective (RPOs), giving application owners improved and faster control of data.

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Granular Node Level Recovery: RecoverX now allows companies to recover data for a particular node of a database cluster, shortening time-to-recovery from dedicated node failures.

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Application Aware Data Masking: Users are now provided native support for data masking to hide sensitive personally-identifying information (PII) data, allowing for continuous test and development needs while meeting compliance requirements.

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Advanced Storage Efficiency: RecoverX optimizes compaction by extracting only net-new data (vs. virtual) from databases, resulting in minimized secondary storage costs and reduced network storage and database cluster loads.

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Application Centric Cluster Consistency: Allows administrators to configure cluster consistency for backups to map the needs of application consistency of write operations.

Scale-Out Software

The Problem

Scalability is a key requirement for cloud applications. As usage of an application scales, the entire infrastructure stack (databases, storage, data management tools) on which these applications run should also scale. This also means that a data protection solution should be able to scale out – both for resiliency against hardware failures and for performance to meet RPO and RTO windows.

The Solution

Datos IO RecoverX is architected as scale-out software that may be deployed in a single node or a cluster configuration (3-node). This scale-out nature ensures that RecoverX is able to grow with increasing application needs. Scale-out architecture of RecoverX brings a high degree of parallelism to achieve higher backup and recovery throughput leading to lower RPO. When deployed in a cluster configuration, RecoverX also ensures high-availability of data protection infrastructure.

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Scalable Versioning

The Problem

Cloud applications and non-relational databases cannot be quiesced to make a consistent backup copy across the database cluster due to their distributed nature. More importantly, given the large amount of data that is processed by these applications, it is critical that there is no bottleneck to data movement in and out of the cluster for an application consistent backup.

The Solution

Datos IO RecoverX provides an application-consistent point-in-time backup of non-relational databases. Backup operations are highly parallel and streaming in nature, where RecoverX acts as a control plane that orchestrates data movement from the data source cluster to secondary storage. It does further post-processing (out-of-band, global state) to derive an application-consistent backup that is highly space-efficient and always available in native format. This ensures that there is very little to no impact on the source database cluster. A key benefit of this is that there are no repairs when a backup is restored, resulting in reduced application downtime. RecoverX allows database administrators to generate backups of their databases at a user-specified time interval and at a desired granularity. Administrators may set backup intervals from as small as a few hours to several days based on the RPO requirements. Application owners and DBAs may also define backup policies that include a single table, multiple tables within a database or multiple tables across databases.

Reliable Recovery

The Problem

Cloud applications are always-on in nature. Application downtime directly translates into tarnishing of brand, unhappy customers, and ultimately loss of revenue. If data is lost due to any reason such as human error, fat finger mistake, or data corruption, restoring that data as quickly as possible is critical. Hence, it is important for any backup and recovery solution to offer low recovery time objective (RTO). Any manual steps during recovery translate into delays. In addition, for cloud scale databases, if the restored data is not consistent, the database will need to be repaired, which may take hours to days.

The Solution

Datos IO RecoverX brings forward two recovery modes – Basic recovery and Orchestrated recovery. Basic recovery restores a version (backup copy) to a destination directory on the same or different database cluster. Orchestrated recovery is an advanced feature that allows users to restore a single table or the entire database into a running database cluster that may have similar or different topology as the source cluster. The restore process moves data in parallel to all nodes of the destination cluster. This removes all manual steps from the recovery process, reduces recovery time and minimizes any data loss risk. In addition, the data that is restored is already consistent, hence, no repairs are required after the restore. This enables customers to meet very low recovery time objective (RTO). The recovery is at least ~4-5x faster than any other backup and recovery solution available today. Finally, RecoverX improves DevOps efficiency by providing a 1-click recovery of a production database to test/dev environments. For example, you can restore a version of your database from a 12-node production cluster to a 3-node test cluster (test/dev instance) just in a matter of minutes!

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Semantic Deduplication

The Problem

Data stored on distributed and cloud databases is most often stored in a 3-replica scheme where every write is replicated three times across the nodes of the database cluster. This provides customers with high availability and protection against node or infrastructure failures. In addition, cloud scale databases offer native replication spread across geo locations. However, replication results in data redundancy and increased storage requirements. For example, when a backup copy of a non-relational database is created, the same redundant data is stored in the secondary storage as well. Given the big data nature of cloud databases, this may result in an explosion of storage for secondary storage and prevent enterprises from meeting their required retention time SLA.

The Solution

Semantic deduplication is an industry-first capability that Datos IO has developed specifically to reduce the cost of storing backup data over its retention period. Today, most cloud databases keep multiple copies of the primary data – also called replicas. As part of the versioning process, RecoverX removes the redundant data sets to make sure that the backup has no replicas of a primary data set, thus providing de-duplication of source data across all replicas. This ground-breaking semantic de-duplication feature results in up to ~70% reduction in secondary storage.

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