ScaleArc is the only technology that can provide full, granular database access logging for all connections, reads, writes, and transactions without any additional performance overhead on the database stack. ScaleArc also processes these logs into very easy to understand usage patterns, which lets you analyze and compare data access patterns across a very wide time range very quickly and discover anomalies in a fraction of the time it takes to do so via conventional log-based analysis methods – if such a log is present at all, since most databases don’t store connection and read logs but only write logs.
This ability to quickly compare access patterns is particularly critical when a data leak or breach occurs, since the time it takes to discover the usage pattern of how the breach occurred is directly correlated with the time it takes to close the hole that led to the breach. The faster you close the breach, the less risk you have.
Though ScaleArc provides very detailed logs, the ability to store many months of data can be impacted if you’re limited on storage capacity. ScaleArc’s de-duplicated analytics conserve storage space by storing the analyzed, reduced-pattern data in easy-to-access, hourly patterns while still preserving many details about the database traffic, such as what type of query patterns were executed, how frequently, and by which users and what IP addresses. If storage is limited, the logs may be rotated, but this data is preserved. Users are able to see such details for usage patterns for time periods spanning a few months even if they’re constrained on storage space.
ScaleArc’s unique patterns comparison tool lets you quickly see what new query methods or patterns have been used in a time band when compared with another. You can easily find new query patterns that may correspond to SQL injection attacks or new, unique data-access patterns that have never been seen before. This tool lets you quickly find errant query behavior and block it using ScaleArc’s database query firewall.