Features
Auto-grow
Auto-grow
Never worry about running out of bottomless analytics storage
Data compression
Data compression
Data stored in analytics tables benefit from efficient data compression of 5-15X and is ideal for large data volumes. For example: 150GB becomes 15GB with a 10X compression.
Automatic caching
Automatic caching
fully managed within Hydra to enable sub-second analytics.
Zero-copy snapshots & forks
Zero-copy snapshots & forks
Zero-copy snapshots enable data sharing with additional teams in your organization. Hydra’s serverless processing guarantees that these different users can access the analytics schema concurrently without sharing compute resources.
Easy to perform joins between the row and columnstore
Easy to perform joins between the row and columnstore
Build richer apps and analytics when combining application data, user sessions, logs, timeseries and events - all available inside Postgres when using Hydra.
Bottomless storage
Events, time-series, user sessions, click, logs, IOT sensor readings, etc. generate a lot of data over time. While on-disk storage works well for Postgres’ rowstore, known as “heap” tables, it’s a poor choice for fast growing data that requires analysis. To avoid the scale limit of on-disk storage, Hydra separates compute and storage.The benefit of separating storage and compute is the ability to scale compute and storage resources independently. As compute needs peak, only CPUs are deployed. As your storage needs increase, only the storage footprint increases.