Crunch for data replication

The challenge of traditional data replication

As enterprises experience exponential growth in data volumes, traditional data replication methods are increasingly falling short:

• Scalability issues — The sheer volume of data often overwhelms traditional replication techniques
• High costs — Replicating large datasets across regions involves prohibitive networking costs
• Inefficiency in disaster recovery — Meeting RPO and RTO targets becomes nearly impossible with large-scale data
• Operational overheads — Managing replication at scale adds significant complexity and cost

Granica Crunch in data replication

• File compression — Granica Crunch applies lossless data compression to source data, leading to substantial cost savings
• Data transfer savings — Compressed data for replication results in lower bandwidth usage and reduced costs
• Faster data movement — Transferring compressed data is quicker, enabling a more agile and responsive data strategy
• Reliable replication — Advanced hash-checking guarantees accurate data replication from source to destination

Key benefits

• Up to 80% reduction in data movement — Granica Crunch intelligently identifies and replicates data, drastically reducing data transfer costs
• Operational efficiency — Simplifies replication management at scale, lowering operational overheads
• Improved disaster recovery — Accelerated replication ensures quicker recovery times during system downtimes

Replication strategy

Streaming replication for active/passive workloads involves keeping the source data in its original state while the replicated data is compressed and stored in the destination.

How it works

• Data at the source remains in its original, uncompressed state
• Replicated data is compressed using Granica Crunch for efficient transfer to the standby destination
• Standby destination stores the compressed data, reducing storage requirements

Advantages

• Ensures operational flexibility at the source with data in its original form
• Reduces storage costs at the destination due to data compression
• Simplifies data recovery and access at the destination
• Efficient for scenarios where the source data integrity is paramount

Trade-offs

• Storage savings are realized only at the destination, not the source
• Potential delay in data availability at the destination due to decompression