Built for maximum ingest, fast query, and high concurrency to help you process, analyze, and act on data instantly. Available in the cloud, on Kubernetes, or on-premises.
Document-oriented database that can store your records as key-value pairs.
Non-relational or NoSQL
Relational model that can leverage the document model through JSON support without losing the powerful relational joins for your BI and analytics
Document-oriented NoSQL model lacks the relational set of capabilities required for analytics
Combination of technologies like MVCC, code generation, lock-free data structures, skip lists, and in-memory execution delivers ultra-fast data ingestion and data access in parallel for the most common use cases of document DBs
Architecture limitations can result in sluggish performance for the use cases where data ingestion and data access needs to be performed in parallel
Scale-out architecture efficiently and quickly responds to growing workloads leveraging commodity hardware without add-ons or specialized tuning expertise
Offers secondary indexes, however the indexes don't scale with sharding and the system is really suboptimal at loading large amounts of random data, as the underlying file format is not truly efficient
Transparent pricing model with having a simple single-product subscription for all of your workloads which is, easy to manage and patch, and delivers maximum performance on a familiar relational data structure at a reasonable cost
Upfront costs including developer and administration resources, software licenses, support, storage and server related hardware turns out to be very expensive
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