Due to this, non-tabular databases like Hbase and Cassandra emerged to demonstrate their features to customers. In this article, let’s talk about how Hbase and Cassandra compare side by side.
Hbase vs Cassandra – Overview
Two well-known database model types that can be used to store, manage, and extract data and make the greatest use of data are Apache Cassandra and Apache HBase. However, if we compare Hbase and Cassandra, they do share a characteristic. Not just one item, but several. They are visually identical and share comparable personalities and abilities.
Let’s discuss the following topics that we are going to discuss in this tutorial:
Now, it’s time to discover more interesting facts related to NoSQL databases such as Hbase and Cassandra.
Hbase vs Cassandra
This blog post compares Hbase and Cassandra databases in-depth in terms of design, support, documentation, SQL Query language, and other factors. It aims to highlight the differences between the two databases.
Hbase
A distributed, open-source NoSQL big data storage is called Apache HBase. It makes petabytes of data accessible in real time at random and with tight consistency. Large, sparse datasets can be handled with ease using HBase.
HBase works on top of the Hadoop Distributed File System (HDFS) or Amazon S3 using the Amazon Elastic MapReduce (EMR) file system, or EMRFS, and integrates easily with Apache Hadoop and the Hadoop ecosystem.
HBase interacts with Apache Phoenix to provide SQL-like queries over HBase tables and provides direct input and output to the Apache MapReduce framework for the Hadoop data processing system.
Column-oriented, non-relational databases like HBase are common. This indicates that data is organized into separate columns and indexed using a special row key.
With this architecture, it is possible to efficiently scan through individual columns in a table and quickly get certain rows and columns.
A HBase cluster’s distributed servers handle requests and data equally, enabling millisecond queries on petabytes of data. Non-relational data is best stored in HBase and accessible through the HBase API.
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Cassandra
A Cassandra cluster, which can be made up of one or more real or virtual servers, is where an Apache Cassandra database is housed.
Additionally, it refers to information that is kept in a database and is accessed online using the query languages and methodology laid out by the Apache Cassandra project.
Users can discuss usage and the most recent innovations in the active Apache Cassandra community.
A Cassandra cluster, which can be made up of one or more real or virtual servers, is where an Apache Cassandra database is housed.
Additionally, it refers to information that is kept in a database and is accessed online using the query languages and methodology laid out by the Apache Cassandra project.
Users can discuss usage and the most recent innovations in the active Apache Cassandra community.
The way Cassandra saved data was another essential element. The approach relies on writing files to disc in an immutable (unalterable) state rather than continuously updating massive monolithic, mutable (alterable) data files.
If information for a specific database entry changed, the change would be made to a new immutable file instead.
You can learn HBase from experts in this HBase Tutorial!
Difference between Hbase vs Cassandra
Let’s tryout to find the difference between Hbase and Cassandra:
| Hbase | Cassandra |
| HBase is built on top of Google BigTable. | The foundation of Cassandra is Amazon DynamoDB. |
| The Master-Slave Architecture Model is used. | The Active-Active Node Architecture Model is used. |
| HBase can make use of a coprocessor’s capabilities. | Cassandra doesn’t support coprocessor capability. |
| Infrastructure for Hadoop is used by Hbase. | For various applications, Cassandra fully utilizes a variety of DBMS and infrastructure. |
| Setting up an HBase cluster ecosystem is challenging. | Compared to HBase, Cassandra cluster setup is easier. |
Compare Cassandra and DynamoDB for your next project’s scalability and performance needs.
Hbase Advantages and Disadvantages
Here is a list of every benefit of HBase:
Advantages of Hbase
Large volumes of data
On top of HDFS file storage, HBase can manage and store huge datasets. Additionally, it compiles and analyses the HBase tables’ billions of rows.
Databases breakdown
Relational databases occasionally malfunction, which is where HBase comes into its own.
Fast processing
In comparison to regular dataBase, HBase requires less time to read and process data.
Failover support and load sharing
Because HDFS is internally distributed and automatically recovered and HBase operates on top of HDFS, HBase is automatically recovered. We also have this failover capability that makes use of replication from RegionServer.
Schema-less
Since HBase lacks a schema, it has no idea of fixed columns schema. Therefore, it only defines column families.
Interested in mastering Cassandra? Check out this Cassandra Training Course!
Disadvantages of Hbase
Here is a list of every disadvantages of HBase:
One potential point of failure
There’s a chance of failure when there’s only one HMaster in use.
Not Supporting Transactions
The transaction is not supported in HBase.
Database not handling JOINS operations
JOINs are handled in the MapReduce layer rather than the database itself.
Only sorted by key
HBase is indexed and sorted solely on key, whereas RDBMS can be indexed on any field.
Integrated authentication
Permissions and built-in authentication are absent.
Not a perfect substitute
We cannot fully anticipate using HBase as a replacement for traditional models because it does not support several of their characteristics.
Cassandra Advantages and Disadvantages
Given below are the Cassandra Advantages and Disadvantages
Advantages of Cassandra
Cassandra offers all the high performance advantages that other NoSQL databases may, similar to how most NoSQL databases do. According to the End Point Benchmark for top NoSQL databases, Cassandra performs well with huge data sets and outperforms the other NoSQL databases in terms of throughput and latency.
Scalability
Cassandra’s distributed architecture allows for both linear and elastic scaling. According to linear scalability, the cluster’s read/write throughput capacity can be expanded by merely adding or removing nodes.
You can quickly scale up or down with elastic scalability by simply adding or removing nodes.
Architecture
Cassandra is designed as a peer-to-peer distributed database with no master or slave and no single point of failure, where each node is equally essential.
Additionally, having nodes that are equally critical to the architecture strengthens it so that any node can take read/write requests from clients.
As a result, Cassandra can support characteristics like scalability and availability more effectively.
Fault Tolerance & Availability
Cassandra has no single point of failure and several nodes can fail without affecting the database’s overall availability since it has a distributed architecture in which all nodes are equal.
Any other node may still be able to accept requests from the client and return the results if a node fails. With Cassandra’s multi datacenter capability, nodes can span many data centers in various regions, which further increases the database’s availability and fault tolerance.
Disadvantages of Cassandra
No database management tool is flawless, of course. Here are some drawbacks of Cassandra:
- Relational data characteristics including ACID are not supported.
- Transactions take longer because of how much data and requests it manages, which causes latency problems.
- Because data is modelled more after searches than after structure, the same information is frequently retained.
- Cassandra holds a lot of data, so there may be problems with JVM memory management.
- There is no support for joins or subqueries.
- Aggregates are not supported by Cassandra.
- Reading has a tendency to be slower because Cassandra was designed from the outset for quick writing.
- Last but not least, there is no official documentation from Apache, thus you must search among independent businesses.
When to use Which Database? Hbase vs Cassandra
Hbase
Depending on the application type they are employed in and the desired results, Cassandra and HBase use cases can be distinguished from one another.
If you need consistency in your large-scale reads and you do a lot of batch processing, use HBase. MapReduce is better because it has a direct connection to HDFS.
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Cassandra
Online log analytics, write-intensive applications, and apps that require a big volume, like Facebook postings, Tweets, etc., are among the use cases for HBase. In addition, there are numerous use cases for integrating Cassandra and Hadoop.
If you require high availability for large-scale reads, use Cassandra. Additionally, the procedure is much simpler to begin because it involves very little setup and less administrative cost. Additionally, it allows for more adaptability in CAP theorem tradeoffs.
The creation of messaging systems, e-commerce websites, and real-time sensor data are some examples of what Cassandra is used for.
Check out this YouTube video on Hbase Training for more information:
Conclusion
It is clear from the architecture distinctions between Cassandra and HBase that HBase is more akin to a meta-data storage due to its need on external systems and potential for increased complexity if used independently.
If your big data project calls for interactive data and real-time transaction processing, go with Cassandra; if you want to aggregate massive data, choose HBase.
Choose wisely based on your project’s goals and your organization’s requirements because no solution is perfect and each has advantages and disadvantages.
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