Directory Data & AnalyticsDatabase PlatformsCassandra (Apache)

Cassandra (Apache)

Open Source

About Cassandra (Apache)

Apache Cassandra is an open source NoSQL distributed database. It is designed for scalability and high availability without compromising performance. It offers linear scalability and fault tolerance on commodity hardware or cloud infrastructure. Read and write throughput both increase linearly as machines are added.

Used for

Replacing failed nodes with no downtime
Tracking DML, DDL, and DCL activity through audit logging
Capturing and replaying production workloads for analysis using fqltool
Streaming data between nodes during scaling operations such as adding a node or datacenter
Choosing synchronous or asynchronous replication for each update

Who it is for

Used by thousands of companies with large active data sets.
Used by Netflix for persistence needs.
Used by WitFoo's R&D team for big-data needs in cybersecurity operations.
Used by application engineers at Bloomberg since 2016.

From Cassandra (Apache)'s own website, read 2026-10-23. How we build listings.

How to evaluate Database Platforms

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Cassandra (Apache). It comes from our Enterprise Database Platforms buyer guide.

25%

Data Model & Query Fit

Match of the engine to the workload: relational integrity and joins, document/JSON flexibility, key-value latency; transaction isolation levels and ACID guarantees; SQL standard coverage and dialect quirks; secondary indexes, full-text and vector (pgvector / Atlas Search) support; foreign keys and referential integrity where you need them

20%

Consistency, Scale & HA

Single-primary vs. multi-writer; synchronous vs. async replication and resulting RPO; read-replica and sharding story; horizontal write scale and global/multi-region behavior (strong vs. eventual consistency); failover time, achievable RTO, and how partitions are handled under load

20%

Operability & Managed-Service Experience

Quality of the managed offering (RDS/Aurora, Azure SQL, Cloud SQL/AlloyDB) vs. self-managed burden; in-place and major-version upgrade path; backup/PITR, observability, and connection pooling; autoscaling and serverless options; day-2 toil — vacuum/compaction, index bloat, rebalancing — and the depth of the DBA skills it demands

15%

Licensing & Portability

License class and its real constraints: OSI open source (PostgreSQL, MySQL, Valkey, Redis 8 AGPL) vs. source-available (MongoDB SSPL, CockroachDB enterprise/BSL) vs. proprietary (Oracle, SQL Server); cloud lock-in of proprietary engines (Aurora, Spanner, DynamoDB); wire-protocol and SQL-dialect compatibility that preserves an exit; audit exposure on proprietary metrics

12%

Security & Compliance

Encryption at rest and in transit, TLS enforcement, and key management (BYOK/HSM); row- and column-level security and fine-grained RBAC; field-level / queryable encryption for sensitive data; audit logging; data-residency controls and certifications (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) on the managed service

Ecosystem & Talent

Driver and ORM maturity across your languages; migration and CDC tooling (logical replication, Debezium, DMS); breadth of extensions and integrations; size and hireability of the talent pool; quality of docs and the support path — first-party vendor vs. third-party (EDB, Percona, Crunchy) for open engines