What is Apache Cassandra?

Apache Cassandra (Cassandra) is an open source NoSQL database built for managing large amounts of data across multiple data centers.

Designed as a distributed database management system (DBMS), Cassandra relies on a peer-to-peer architecture. Every node, or individual server that stores part of the data, in a Cassandra cluster is equal, with no reliance on a master node.

Data is partitioned across peers rather than stored in a centralized location, eliminating a single point of failure (where one malfunction quickly cascades into multiple). This design enables seamless replication, efficient data distribution and continuous service even during planned downtime or sudden changes. 

Cassandra offers automation, data backups and integrated metrics for use cases like managing connected Internet of Things (IoT) devices. More specifically, it delivers linear scalability, high availability and fault tolerance, making it a popular choice for big data applications and real-time workloads. As of September 2024, Cassandra was used by more than 30,000 organizations worldwide.

Cassandra’s story started in 2007 at Facebook, where engineers sought a system that could store data for the company’s growing messaging platform. By combining established NoSQL database models (Amazon’s Dynamo and Google’s Bigtable), they created a system with efficient data structures and eventual consistency—where updates propagate until all replicas match over time.

In 2008, Cassandra was released as an open source project, quickly gaining traction among developers seeking an alternative to traditional relational databases. The Apache Software Foundation took over stewardship in 2009, formalizing its governance and accelerating community adoption.

Cassandra’s momentum grew as early adopters like eBay, Spotify and Instagram deployed it to handle big data. The rise of IoT and real-time personalization further cemented Cassandra’s role as a go-to database for scale and availability.

Commercial support from DataStax added enterprise-grade tooling, tutorials and services, while the open community developed tools and expanded documentation. Today, Cassandra remains central to many distributed systems, thriving within both the open source ecosystem and enterprise deployments.

From streaming services and social media to online shopping, customers expect always-on digital experiences. For companies, uptime is no longer an IT goal but a business metric. The cost of falling short is steep: the world’s leading companies lose an estimated USD 400 billion annually to unplanned downtime.

At the same time, a surge of unstructured data from event logs, telemetry and data streams is making operations more complex across regions and cloud environments, increasing the likelihood of system failures. Organizations need a reliable database that can handle diverse data types and scale with demand across global infrastructures. Cassandra is engineered to meet these demands.

Industries rely on Cassandra’s high performance to process billions of write operations (insert, update and delete) while serving users with real-time accuracy. Its resilience comes from replicating data across commodity servers, or standard off-the-shelf machines, minimizing the risk of outages and ensuring durability even when hardware fails.

Cassandra’s ability to manage workloads across multiple data centers provides consistency and availability for companies worldwide. Organizations like Netflix and Amazon use Cassandra to deliver personalized experiences while protecting against downtime and data loss. In fact, Netflix’s Asset Management Platform team uses Cassandra to manage roughly 1.9 billion annotations (about 2.6 TB), having doubled its cluster from 12 to 24 nodes.

Unlike relational databases, which rely on rigid schema definitions and centralized control, Cassandra is built for distributed scale. In relational systems, a primary key is tied to strict data modeling and limited scalability. Cassandra, by contrast, uses a partition key and replication factor to determine how datasets are stored across nodes and data centers.

While Structured Query Language (SQL) systems excel at complex joins and aggregates, they often introduce bottlenecks and a risk of a single point of failure. Cassandra avoids this by embracing distributed architecture and eventual consistency. Compared with MongoDB, the Cassandra database favors write-heavy, linearly scalable workloads across multiple data centers.

For organizations managing large amounts of data, Cassandra offers clear advantages: high throughput, low latency and tolerance for outages. However, Cassandra does not provide the same level of ad hoc querying that some relational databases offer. Developers using Cassandra should design data modeling strategies carefully to optimize write operations, replicas and data integrity.

Cassandra’s design combines innovations in distributed systems with tools for enterprise-grade data management. Key features include:

• Open source
• High performance
• Tunable availability
• Linear scalability
• Seamless replication
• Familiar interface

Cassandra is open source under the Apache Software Foundation, helping organizations avoid vendor lock-in and customize the database to fit their needs. When enterprise-grade help is required, teams can use community resources or choose commercial support and managed services.

Cassandra’s storage engine uses a step-by-step flow (or write path) consisting of a commit log, an in-memory table (memtable), and sorted string table (SSTable) files. This flow accepts write operations quickly and safeguards them. Frequently accessed data is kept in the cache for low-latency queries while compaction, an automatic housekeeping function, helps ensure efficient long-term data storage.

Under the CAP theorem, when a network partition occurs, a distributed system can deliver only two of three desired characteristics: consistency, availability and partition tolerance (CAP). Cassandra addresses this trade-off through tunable consistency levels, allowing users to prioritize availability or consistency depending on use case.

Cassandra increases capacity by adding new nodes without service interruption, delivering linear scalability on commodity servers instead of expensive vertical upgrades. As nodes are added, Cassandra automatically redistributes data and traffic across the cluster, so workloads scale out and throughput rises proportionally.

Cassandra replicates data across nodes and data centers so local users experience low latency while avoiding a single point of failure. It also integrates with Kubernetes, application programming interface (API) frameworks and Amazon Web Services (AWS) environments. It is written in Java and runs on the Java Virtual Machine (JVM).

Teams use Cassandra Query Language (CQL)—which mirrors SQL—to quickly define key constructs such as keyspace, tables and primary keys. Interactive tools like CQL shell (cqlsh) and official tutorials can also help reduce onboarding time for new developers.

Cassandra interacts with applications through CQL, a domain-specific language inspired by SQL. CQL syntax is familiar to database developers, allowing them to define the keyspace, schema, data types and both primary and partition keys.

For example, during a global game launch a developer may create a keyspace—Cassandra’s top-level database equivalent that defines replication settings. After that, they can design tables where the partition key (such as player ID or region) keeps related data on the same nodes for efficient data distribution. Using cqlsh, the team could run tutorials, validate queries and manage the Cassandra cluster as they add new nodes to handle the increase in player volume.

Because Cassandra emphasizes write operations and throughput, its syntax avoids features that would slow performance, such as complex joins. Instead, developers rely on secondary indexes, aggregates and optimized data modeling to achieve flexibility.

Although CQL resembles SQL, the two languages reflect different approaches to data management.

Developers moving from SQL can adapt quickly to CQL’s syntax but must rethink data modeling strategies to leverage Cassandra’s distributed systems approach.

Cassandra powers mission-critical workloads across industries that demand high performance, low latency and resilience. Examples include:

• E-commerce: Retailers use Cassandra to store data on shopping carts, personalize recommendations and process payments with fault tolerance.

• IoT: Cassandra manages sensor streams and datasets from millions of devices, ensuring real-time insights with durability.

• Cloud deployments: Cassandra integrates with AWS and other cloud services. It can also be orchestrated on Kubernetes for containerized environments.

• Streaming and entertainment: Streaming services leverage Cassandra to handle global user activity, delivering personalized experiences without risking downtime.

Beyond these verticals, Cassandra supports organizations building distributed systems for big data and scalable data storage. With a combination of API support, enterprise tooling and open community tutorials, Cassandra remains a cornerstone for modern database management systems.