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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Apache Ignite, Hazelcast, Cassandra, and Tarantool solve different data problems. Ignite is a memory-first distributed SQL database; Hazelcast is an in-memory data grid and real-time data platform; Cassandra is a durable wide-column database built for partition-key workloads and availability; Tarantool combines an in-memory database with a Lua application server. The right choice depends less on which is “fastest” and more on your data model, transaction boundaries, consistency requirements, and persistence needs.
How the four systems differ
| System | Core model | Consistency and transaction scope | Good fit | Main trade-off |
|---|---|---|---|---|
| Apache Ignite, especially Ignite 3 | Memory-first distributed SQL database with optional persistence and schema-driven data colocation | Ignite 3 documentation describes Raft-backed strong consistency, MVCC, and ACID transactions across partitions | Low-latency SQL and key-value access, shared state, event enrichment, microservice state, and feature stores | Database and cluster operations require care; Ignite 2 has a more cache-centric model than Ignite 3 |
| Hazelcast | In-memory data platform with distributed maps, caches, replicated structures, SQL, and processing | Consistency depends on the structure and configuration: Hazelcast classifies structures as AP or CP | Distributed caching, shared application state, and real-time data processing | Its data-grid primitives need workload-specific modeling; it is not a wide-column durable database |
| Apache Cassandra | Partitioned wide-column NoSQL database with multi-primary replication | Ordinary writes converge eventually; consistency is tunable per operation. Paxos lightweight transactions support single-partition compare-and-set | High-volume, geographically distributed workloads naturally queried by partition key, especially when availability matters | No cross-partition transactions, distributed joins, or foreign keys |
| Tarantool | In-memory DBMS and Lua application server in one platform | ACID storage; WAL and snapshots; durable distributed storage and Raft synchronous replication are available | Low-latency OLTP, queues, caches, and data-centric services | Smaller ecosystem and more application logic embedded in Lua; Enterprise capabilities are separately packaged |
These labels describe different design priorities, not interchangeable product categories. Apache Cassandra’s overview calls it “an open-source, distributed NoSQL database”; Tarantool describes a combined in-memory DBMS and Lua server with ACID-compliant storage. Neither description makes the products equivalent to a cache grid or a distributed SQL database.
Which one should you choose?
Choose Apache Ignite for distributed SQL and cross-partition transactions
Ignite is a strong candidate when the application needs SQL and key-value access, low-latency reads, schema-aware placement, and ACID transactions that can span partitions. Its current Ignite 3 materials describe a memory-first database with optional persistence, MVCC, Raft replication, SQL/JDBC, and partition-aware clients. Check the version before applying this model: Ignite 3 is database-first, while Ignite 2 deployments commonly use a cache-centric API model.
Choose Hazelcast for data-grid structures and real-time processing
Hazelcast fits applications built around distributed maps and caches, near-cache behavior, replicated maps, WAN replication, SQL over data-grid structures, or real-time processing. It is more appropriate to select a particular structure and its consistency behavior than to label Hazelcast as a whole “eventually consistent.” Its documentation distinguishes partitioned AP structures, including maps and caches, from separate CP structures. The cited Hazelcast documentation is for version 5.6.
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Choose Cassandra for partition-key access at distributed scale
Cassandra suits large, geographically distributed workloads whose queries can be designed around partition keys and that prioritize availability over transactions spanning arbitrary records. Data modeling is central: Cassandra requires a partition key for performant access patterns, and applications should design tables around the queries they need to serve. Cassandra documentation used for this comparison is labeled Version 5.0.
Choose Tarantool to keep data operations close to application logic
Tarantool may suit services that benefit from an in-memory database and application server in one process, Lua procedures close to data, advanced indexes, queues, or cache behavior. Its documented storage options include write-ahead logging (WAL) and snapshots, alongside durable distributed storage, failover modes, and Raft-based synchronous replication. Enterprise offerings add cluster management and broader database connectivity; evaluate packaging and licensing needs separately from the core architecture.
Transactions and consistency: the deciding differences
“Supports transactions” is too broad to guide a design. The important question is which operations can be atomic and what consistency the application gets when nodes fail or communication is interrupted.
- Ignite: Ignite 3 documents ACID transactions across partitions and presents strong consistency as its default model, using Raft-backed replication and MVCC.
- Hazelcast: consistency is structure-specific. AP and CP structures offer different trade-offs, so identify which structure the application uses and how it is configured before assessing behavior during a partition.
- Cassandra: ordinary writes use eventual consistency, with consistency tunable per operation. Paxos-based lightweight transactions handle compare-and-set operations within a single partition; they are not general cross-partition transactions.
- Tarantool: its storage is ACID-compliant, and the platform documents synchronous replication using Raft. Distributed replication and failover design still need to match the durability and availability requirements of the deployment.
Cassandra explicitly avoids operations that require cross-partition coordination, which it describes as slow and difficult to reconcile with highly available global semantics. If a business operation must update multiple partitions atomically, that is a major architectural distinction from Ignite’s documented cross-partition transaction support.
Data model, queries, and durability
Model around the queries the system is designed to serve
- Ignite: schema-driven colocation places related data for distributed processing; SQL and key-value access support database-style use cases.
- Hazelcast: distribute data using the maps, caches, replicated structures, and processing features appropriate to the workload. It is a data-grid model, not a wide-column schema.
- Cassandra: choose partition keys and table layouts based on required queries. It does not offer distributed joins or foreign keys, so applications should not expect a relational database’s cross-table query model.
- Tarantool: store indexed tuples and use application logic, including Lua procedures, close to the data.
Plan persistence and recovery explicitly
Ignite persistence is optional, so decide whether the workload relies on memory alone or requires data to survive restarts through persistent storage. Cassandra is built around replicated durable storage. Tarantool supports WAL and snapshots as well as durable distributed storage and replication options. Hazelcast durability varies with the chosen structure and deployment; a cache or data grid should not be assumed durable without checking its configuration and recovery design.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical selection checklist
Before choosing, assess these requirements against a representative workload rather than a product label:
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- Access pattern and data model: Do queries map naturally to Cassandra partition keys, SQL and colocated data in Ignite, Hazelcast data-grid structures, or Tarantool indexed tuples?
- Transaction boundary: Must one operation atomically update multiple partitions, or is a single-partition conditional operation sufficient?
- Consistency during a network partition: Which operations may proceed, and what consistency guarantees must callers observe? For Hazelcast, identify the exact AP or CP structure.
- Memory and disk durability: Is persistence optional, essential, or supplied by a separate layer? What data must survive a node or site failure?
- Replication and multiple data centers: Define failover behavior, replication requirements, and acceptable recovery outcomes instead of assuming that “distributed” means the same thing across systems.
- Query language and joins: Verify the required SQL, client, and query patterns. Cassandra does not provide distributed joins; Ignite documents SQL/JDBC.
- Client support and operations: Check the languages your services use, available operational tooling, managed-service options, team expertise, and the effort required to run the chosen deployment.
Do not select on an assumed latency or throughput ranking: the official materials considered here do not establish comparable benchmark results. Test with your own data shape, consistency settings, durability configuration, and failure scenarios before committing to an architecture.
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