Hadoop vs Cassandra – What’s the Difference ? (Pros and Cons). In this article we take a closer look at Hadoop and Cassandra. We start with an introduction, followed by features and their respective pros and cons. After that we perform a comparison of both technologies in terms of their applications and capabilities. Ultimately, we want to decide, which tool is better for specific scenarios and what are their prospects for their future development.
Nowadays, the amount of data generated is growing exponentially. Analysing it has become crucial for many organisations to make sound business decisions. A number of tools and technologies have emerged to effectively process and analyse large data sets, two of which – Hadoop and Cassandra are particularly popular in the Big Data field.
So, let’s start with Hadoop vs Cassandra – What’s the Difference ?
What is Hadoop?
First tool we are describing is Hadoop. An open source software platform for distributed storage and processing of large data sets. Designed to process and store data on off the shelf hardware clusters. All in all, making it a cost effective solution for big data processing.
Essentially, Hadoop includes two main components: Hadoop Distributed File System (HDFS) and MapReduce. Well, HDFS is a distributed file system that stores large data sets across multiple machines in a cluster. Whereas MapReduce is a programming model that allows these large data sets to be processed in parallel across a cluster.
Features of Hadoop
- Generally considered a highly scalable storage platform because it hosts and distributes large data sets across hundreds of cost effective servers running in parallel. Additionally, Hadoop lets enterprises run applications on several nodes involving thousands of terabytes of data.
- Interestingly, it has earned the nickname widely recognised as a feature of data nativeness. This means moving computational logic to data, rather than moving data to computational logic. What it results in, is reduction of bandwidth consumption of the system.
- Handles unstructured data rather than traditional systems. Provides the ability to analyse customer data of any size and format.
- With Hadoop, cluster data replication preserves data on cluster systems, even if a system fails. The framework itself provides mechanisms to ensure data safety with volume scanners, block scanners, directory scanners, and disk checkers. In the event of a computer failure or data corruption, the data is safely stored in the cluster and is retrieved from any other computer that contains a copy of the data.
- Also has a job scheduling and resource management layer as part of Hadoop architecture. So, all the information is stored in Hadoop distributed file system and works with data processing engines and batch processing, etc. The entire Hadoop process is designed using the YARN framework.
- The data processing layer in Hadoop is Mapreduce. A programming model that is classified as two phases Map and reduce. Simply created for data processes in parallel classified nodes.
Pros
- A major advantage of Hadoop is its fault tolerance. When data is moved to a single node, data is replicated across the cluster. Its distribution has surpassed the removal of name nodes. Furthermore, architecture provides protection against one or more node failures.
- Most modern big data technologies like Spark, Flink, etc. work well with Hadoop. They have a processing engine running Hadoop as the backend. This means you are using Hadoop as your data storage platform.
- It keeps interesting and reliable features and functions.
- Apache Hadoop has made managing large amounts of data quite easy.
- Customer support is quick.
- The various modules sometimes are pretty challenging to learn initially but at the same time it has made Hadoop easy to implement and perform after thar.
- The parallel processing tool of this software is also a good aspect of Apache Hadoop.
- Enterprise support from different vendors makes it easier to ‘sell’ inside an enterprise.
Cons
- Less organizational support system. Bugs need to be fixed and outside help take a long time to push updates.
- Failure in NameNode as it has no replication, which takes a lot of time to recover.
- Integration is not always seamless between the disparate pieces nor are all the pieces required.
- Improvement here should connecting Hadoop to Salesforce. Functionality will be impressive, as most CRM data comes from that channel.
Up next with Hadoop vs Cassandra – What’s the Difference ? is to introduce Cassandra.
What is Cassandra?
Cassandra is an open source distributed NoSQL database management system designed to handle large amounts of data on many standard servers, providing high availability with no single point of failure. Originally developed by Facebook and is now maintained by the Apache Software Foundation.
So, Cassandra is a columnar database, meaning that data is stored in columns rather than rows. Designed to handle structured and unstructured data types, making it suitable for a wide range of applications. Furthermore, Cassandra is also known for its high write performance and low read latency, making it well suited to applications such as real time analytics, ecommerce and the Internet of Things (IoT).
Features of Cassandra
- Provides an easy way to distribute data. Hence, data distribution is made easy in Cassandra because of the flexibility to distribute data whenever needed. All in all, data replication is used between multiple data centers for this purpose.
- Fault tolerant solution. If the cluster has four nodes, Cassandra’s each node has a copy of the same data. If any of the 4 nodes go down, the other 3 nodes can do this as needed.
- Designed to run on cheap commodity hardware. Therefore, performs blazingly fast writes and stores hundreds of terabytes of data, without sacrificing the read efficiency.
- Provides tuneable consistency, allowing developers to specify the level of consistency required for each operation. This feature allows for improved performance in high write environments.
- Cassandra has a solid architecture. With no single point of failure, Cassandra ensures continuous availability of mission critical business applications. These types of applications typically cannot tolerate a single point of failure.
Pros
- Nodes in a ring keep up to date by sharding information to each other.
- One of the best noSQL solutions.
- Distributed System Logic. Multiple data centers and other common network configurations like heterogeneous nodes are handled and exploited well.
- Tuneable consistency model enables consistency as your platform application needs.
- Automatic data sharding between nodes.
- Cassandra preforms read writes very quick.
Cons
- Aggregation functions are not very efficient.
- Database event logging should be handled more efficiently.
- No schema or relationships are used, therefore same data can’t be stored multiple times.
- Data reading tends to be slower as Cassandra is optimized for faster writing.
- No Apache documentation. Therefore, users have to look for documents from other companies.
Hadoop vs Cassandra - Comparison Guide
Both, Hadoop and Cassandra are both popular open source distributed systems used for managing large volumes of data. But they have some important differences.
Data Processing
Generally, Hadoop is primarily used for batch processing of large datasets using the MapReduce. While programming model, which involves splitting the input data into smaller chunks. Additionally processing them in parallel on different nodes, and then aggregating the results. While Cassandra, on the other hand, is designed for real time data processing and supports the Cassandra Query Language (CQL). In turn, that allows users to perform queries on the data stored in the database.
Consistency and Availability
Consistency and availability in Hadoop provides strong levels of both, meaning that all nodes in the cluster see the same data at the same time. But Cassandra, on the other hand, provides eventual consistency, where updates to the data are propagated asynchronously to all nodes in the cluster. Also this approach allows Cassandra to provide high availability, even in the face of network partitions or node failures.
Scalability
Scalability with Hadoop achieves it by distributing data processing across a large number of commodity hardware nodes in a cluster. Hence, Hadoop uses a distributed file system called Hadoop Distributed File System (HDFS) to store data across the cluster. And the MapReduce programming model to process the data in parallel on the distributed nodes. This approach allows Hadoop to scale horizontally by adding more nodes. To the cluster as the data volume and processing requirements grow.
In contrast, Cassandra achieves scalability through a decentralised architecture that distributes data across multiple nodes in the cluster using a peer to peer model. Moreover Cassandra has a ring architecture. In there each node is connected to other nodes in the cluster. Then data gets replicated across multiple nodes for fault tolerance and high availability. Thanks to this approach it allows Cassandra to scale horizontally by adding more nodes to the cluster when data volumes and read and write throughput requirements increase.
Data Storage
With data storage Hadoop uses Distributed File System (HDFS) to store data across a large cluster of commodity hardware nodes. What is more HDFS is a fault tolerant, distributed file system storing data in a distributed manner across multiple nodes in the cluster. Consequently, HDFS breaks large files into smaller blocks and stores them on different nodes in the cluster.
Evidently with Cassandra, it stores data in a distributed manner by partitioning data across multiple nodes in the cluster. In there, each node is responsible for storing a subset of the data. Especially, with Cassandra it also supports data replication, where data is replicated across multiple nodes in the cluster for fault tolerance and high availability. Altogether, this approach provides high availability and low latency access to data in a distributed environment.
Performance
Performance is important factor to compare. On one hand Hadoop achieves high performance through the use of a distributed file system (HDFS) as well as MapReduce programming model. What it does, it processes data in parallel on distributed nodes. Evidently, this approach allows Hadoop to distribute data processing across a large number of commodity hardware nodes, which are added or removed from the cluster as needed, to achieve high throughput and scalability.
Whilst, Cassandra achieves high performance through a decentralized architecture that distributes data across multiple nodes in the cluster using a peer to peer model. Following, Cassandra uses a ring based architecture, where each node is connected to other nodes in the cluster and data is replicated across multiple nodes for fault tolerance and high availability. In turn, this allows Cassandra to handle high speed data ingestion and real time data processing with low latency.
Use cases
This instance with Hadoop it is often used for offline processing and analysis of large datasets, such as log analysis, data warehousing and machine learning. But Cassandra is often used for real time applications, such as messaging systems, social networks, and Internet of Things (IoT) applications.
Thank you for reading this article blog Hadoop vs Cassandra – What’s the Difference ? (Pros and Cons). We shall conclude it now.
Hadoop vs Cassandra – What’s the Difference ? Conclusion
Summing up, most important difference between the two are that Hadoop is best for massive data batch processing, but Cassandra is better pick for real time processing. Also, Hadoop is based on on master slave architecture but Cassandra works on peer to peer communication.
Ultimately, after performing comparative analysis each has strengths and weaknesses. But that depends what strengths are the most valuable for your business needs. The choice between Hadoop and Cassandra depends on the specific requirements of the application, such as scalability, performance, data integrity and ease of use. Each of these systems has its own unique set of data processing tools and approaches, and the choice between them has to be individual business and technology needs.
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