StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Cassandra vs Hadoop vs Hazelcast

Cassandra vs Hadoop vs Hazelcast

OverviewDecisionsComparisonAlternatives

Overview

Cassandra
Cassandra
Stacks3.6K
Followers3.5K
Votes507
GitHub Stars9.5K
Forks3.8K
Hadoop
Hadoop
Stacks2.7K
Followers2.3K
Votes56
GitHub Stars15.3K
Forks9.1K
Hazelcast
Hazelcast
Stacks427
Followers474
Votes59
GitHub Stars6.4K
Forks1.9K

Cassandra vs Hadoop vs Hazelcast: What are the differences?

Introduction

In this article, we will explore the key differences between Cassandra, Hadoop, and Hazelcast. These are three popular distributed computing platforms used for managing and processing big data. Each platform has its own strengths and weaknesses, making them suitable for different use cases.

  1. Data Model:

    • Cassandra: Cassandra is a NoSQL database that follows a wide-column data model. It stores data in tables with rows and columns and allows flexible schema design. It provides high scalability and availability by distributing data across multiple nodes.
    • Hadoop: Hadoop is a distributed data processing framework that works on a file system called Hadoop Distributed File System (HDFS). It follows a batch processing model and uses a scalable storage system to store and process large datasets.
    • Hazelcast: Hazelcast is an in-memory data grid that stores data in a distributed manner across a cluster of nodes. It provides a key-value data model and supports distributed data structures like maps, lists, and sets.

  2. Processing Model:

    • Cassandra: Cassandra provides a real-time and distributed query processing model. It supports fast read and write operations by using a distributed hash-based system called consistent hashing. It also allows the execution of lightweight analytical queries using Cassandra Query Language (CQL).
    • Hadoop: Hadoop follows a batch processing model, where data processing is done in parallel on large datasets. It uses a MapReduce programming paradigm, where computation is split into map and reduce tasks. Hadoop is optimized for handling large-scale data processing with fault tolerance.
    • Hazelcast: Hazelcast provides an in-memory computing model. It excels at distributed data parallelism and supports executing parallel computations on distributed data structures in real-time. With its high-performance in-memory processing, it is suitable for low-latency use cases.

  3. Data Replication and Fault Tolerance:

    • Cassandra: Cassandra replicates data across multiple nodes using a peer-to-peer distributed system. It provides fault tolerance by using data replication and consistent hashing. Data is replicated across multiple data centers, ensuring high availability and durability even in case of node failures.
    • Hadoop: Hadoop provides fault tolerance by replicating data across multiple nodes in the HDFS file system. It uses a master-slave architecture, where the NameNode manages the file system metadata and the DataNodes store the actual data. If a DataNode fails, the NameNode replicates the data to another node to ensure fault tolerance.
    • Hazelcast: Hazelcast replicates data across a cluster of nodes to ensure fault tolerance. It provides configurable data backup options, allowing users to choose the desired level of redundancy. In case of node failures, Hazelcast automatically redistributes the data to maintain high availability.

  4. Scalability and Performance:

    • Cassandra: Cassandra provides high scalability by using a distributed architecture. It allows adding new nodes to the cluster easily, enabling linear scalability. It is designed to handle large amounts of data and to provide low-latency performance for both read and write operations.
    • Hadoop: Hadoop is built to handle large-scale data processing and provides high scalability. It allows scaling by adding more nodes to the Hadoop cluster, and it can process data in parallel across multiple nodes. Hadoop's performance can be optimized by tuning various parameters and configurations.
    • Hazelcast: Hazelcast is designed for high-performance and low-latency data processing. It scales by adding more nodes to the cluster, and it automatically distributes the data across the nodes to leverage the computing power of the cluster. Hazelcast's in-memory computing capabilities ensure fast data access and processing.

  5. Querying Capabilities:

    • Cassandra: Cassandra provides rich querying capabilities with the Cassandra Query Language (CQL). It supports a SQL-like syntax for querying data and provides features like filtering, sorting, and aggregation. CQL also allows defining secondary indexes for efficient querying.
    • Hadoop: Hadoop primarily focuses on batch processing and is not designed for interactive querying. However, with the help of additional components like Apache Hive or Apache Drill, Hadoop can provide SQL-like querying capabilities on large datasets.
    • Hazelcast: Hazelcast supports querying on distributed data structures using a query language called Predicate. It allows filtering and querying data based on the attributes of distributed objects. Hazelcast also supports distributed SQL queries through its integration with Apache Calcite.

  6. Use Cases:

    • Cassandra: Cassandra is well-suited for use cases that require fast read and write operations, high scalability, and continuous availability. It is commonly used in applications with high write throughput, time-series data, and real-time analytics.
    • Hadoop: Hadoop is commonly used for offline batch processing, large-scale data analysis, and data transformation. It is suitable for scenarios where data can be processed in bulk and does not require real-time or low-latency processing.
    • Hazelcast: Hazelcast is often used for caching, distributed computing, and real-time data processing. It is particularly useful in scenarios that require fast data access, low-latency processing, and distributed computations.

In Summary, Cassandra is a NoSQL database with a wide-column data model, Hadoop is a distributed batch processing framework, and Hazelcast is an in-memory data grid. They differ in their data models, processing models, data replication, fault tolerance mechanisms, scalability, performance, querying capabilities, and use cases.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Cassandra, Hadoop, Hazelcast

Vinay
Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

174k views174k
Comments

Detailed Comparison

Cassandra
Cassandra
Hadoop
Hadoop
Hazelcast
Hazelcast

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

--
Distributed implementations of java.util.{Queue, Set, List, Map};Distributed implementation of java.util.concurrent.locks.Lock;Distributed implementation of java.util.concurrent.ExecutorService;Distributed MultiMap for one-to-many relationships;Distributed Topic for publish/subscribe messaging;Synchronous (write-through) and asynchronous (write-behind) persistence;Transaction support;Socket level encryption support for secure clusters;Second level cache provider for Hibernate;Monitoring and management of the cluster via JMX;Dynamic HTTP session clustering;Support for cluster info and membership events;Dynamic discovery, scaling, partitioning with backups and fail-over
Statistics
GitHub Stars
9.5K
GitHub Stars
15.3K
GitHub Stars
6.4K
GitHub Forks
3.8K
GitHub Forks
9.1K
GitHub Forks
1.9K
Stacks
3.6K
Stacks
2.7K
Stacks
427
Followers
3.5K
Followers
2.3K
Followers
474
Votes
507
Votes
56
Votes
59
Pros & Cons
Pros
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
Cons
  • 3
    Reliability of replication
  • 1
    Updates
  • 1
    Size
Pros
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
Pros
  • 11
    High Availibility
  • 6
    Distributed compute
  • 6
    Distributed Locking
  • 5
    Sharding
  • 4
    Load balancing
Cons
  • 4
    License needed for SSL
Integrations
No integrations availableNo integrations available
Java
Java
Spring
Spring

What are some alternatives to Cassandra, Hadoop, Hazelcast?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

Redis

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase