For managing Big Data, it is advisable to use NoSQL databases as they are crafted to handle voluminous and diverse data sets efficiently. In contrast, when attempting to use a relational database with Big Data, its shortcomings are likely to become evident.
Once you have chosen the type of database for your project, you need to make a decision on which specific database to use. There are multiple NoSQL databases to choose from, such as MongoDB, RavenDB, Redis, CouchBase, IBM Cloudant, and Amazon DynamoDB – all capable of handling the workload.
The Apache Project upholds HBase and Cassandra – two additional NoSQL databases that may appear similar, but upon closer inspection, have distinct dissimilarities. To determine which database is better suited for your business, let us compare and contrast Cassandra and HBase.
An overview of HBase is provided.
Apache HBase, an open-source NoSQL database, is a perfect fit for distributed databases that require managing voluminous data. This NoSQL database offers the capacity to store petabytes of data while providing real-time access to the same, with organized and uniform processing.
HBase uses columnar storage with row keys as index. Leveraging the principle of distributing queries and data across a range of computer clusters, it can deliver quick responses in a matter of milliseconds. This makes HBase a suitable option for managing extensive data repositories with the ability to fetch both row and column data promptly.
HBase provides a non-relational data storage solution available via its Application Programming Interface. Additionally, for ease of use, HBase can be utilized alongside Apache Phoenix to yield a SQL-based interface, allowing database operators to use the familiar syntax of SQL while entering, deleting and querying data.
HBase is swift, reliable, and scalable.
HBase comprises the following fundamental components:
Who or What Exactly is Cassandra?
Apache Cassandra is a renowned, open-source distributed NoSQL database with expansive storage capabilities. It comprises a masterless architecture, where each node within the cluster performs the same function. This quality makes Cassandra an excellent fit for both public and private cloud environments, while providing the security of data safety in the event of data center malfunction.
Cassandra is frequently lauded for its scalability, high availability, and performance, making it a favoured option in the database domain. It can be deployed on both commodity hardware and cloud infrastructure, rendering it an excellent fit for meticulous data. When it comes to speed, Cassandra ranks amongst the fastest NoSQL databases, making it the optimal choice for your enterprise or venture.
Key Components of Cassandra
The subsequent components bring together Cassandra:
- Polymerase Chain Reaction
- Record of Accountability
What Sets HBase and Cassandra Apart from Each Other?
In this discussion, we’ll take a closer look at two significant components of a database, write and read performance, where differences might be more pronounced.
For an Insight into Performance, Read On
In HBase, writes are handled by a single server, whereas Cassandra disseminates updates to several servers running various software versions. HBase stores data utilising Hadoop Distributed File System (HDFS), which utilises bloom filters and black caches, resulting in superior read performance. In contrast, while accessing data, Cassandra must verify the partition table first.
In this scenario, HBase does not facilitate concurrent writing. However, Cassandra allows for both updating its log and cache simultaneously to expedite the process. Additionally, Cassandra’s consistent hashing approach enables rapid data distribution, resulting in quicker writes. Conversely, HBase necessitates client contact with the metadata server via Zookeeper to locate the address store and table where updates will be made, creating an additional layer of overhead that causes HBase writes to be slower than Cassandra writes.
The average latency is expected to decline when the instances of random reads and updates increase in HBase. Conversely, growing I/O operations in Cassandra cause increased latency. However, after approximately 10,000 reads and writes, latency is observed to decrease.
HBase normally maintains a steady throughput of roughly 100,000-200,000 operations, with the potential to exceed 250,000 or even more. In comparison, Cassandra’s throughput rises as the number of reads and writes increase.
Compared to other databases, HBase has a greater average read latency. Yet, this delay does not alter significantly with increased instances of read operations.
Which One Meets Your Requirements More Effectively?
When weighing this decision, it is crucial to take into consideration the fault tolerance of each database. If the master node fails in HBase, the entire database becomes out of reach. Conversely, Cassandra adopts a masterless architecture, granting it the ability to remain functional even if a node fails. However, data inconsistencies could arise.
If ensuring data consistency is essential, then HBase is the optimal selection, but if availability is the primary concern, then Cassandra is the preferred solution.