Data is everywhere. With an unprecedentedly high growth rate, every modern business enterprise is dealing with large volumes of data. According to Forbes, by 2025, the global data is set to double every 12 hours. Consequently, businesses need a scalable data infrastructure that can easily handle increasing data volumes, user traffic, and computational requirements without sacrificing performance or reliability.
Distributed databases play a significant role in scaling data systems. Essentially, a distributed database is a database that is spread across multiple servers or nodes, allowing large volumes of data to be stored and processed in parallel. Instead of depending on a single server, distributed databases dispense data and workload across a cluster of interconnected nodes. Thus, enterprises can seamlessly accommodate growing data without any disruptions.
Moving on, Apache Cassandra is deemed one of the best-distributed database solutions. The highly scalable and fault-tolerant NoSQL database developed by Facebook is well-equipped to handle massive amounts of data across multiple commodity servers.
In today’s blog, we will understand everything about Apache Cassandra and find out why it is the best choice for scaling data infrastructure.
Understanding Distributed Databases
Before we move on to discuss Apache Cassandra, you must have a clear understanding of distributed databases and how they work.
A distributed database system operates and stores data across a range of computers, as opposed to doing and storing everything on a single system.
Typically, distributed databases run on two or more computer networks or interconnected servers. Each location where a version of a database operates is called a node or instance. For example, a system of distributed databases may have nodes running in London, Tokyo, and Sweden. Or, it may have nodes running on three separate computers in London. A traditional database, on the contrary, only operates on a single machine in a single location.
It is seen that distributed databases have many advantages over traditional databases. The most notable ones include:
- Distributed databases increase flexibility and reduce risk. For example, if a single-node database experiences machine failure, all the application services that rely on that database will go offline. But with distributed databases, replicas of the same data are configured across multiple nodes. Thus, the other nodes can pick up the slack, enabling the application to continue running.
- Distributed databases are much easier to scale. This is beneficial for a growing business where database requirements may increase over time.
- Distributed databases can help to boost performance. Depending on its configuration, a distributed database can divide the workload among multiple nodes or instances, thereby enhancing performance and efficiency.
Distributed databases, especially those that support multi-region deployments can reduce latency. Consequently, they can improve the application performance by decreasing latency.
An Introduction to Apache Cassandra
Apache Cassandra is a distributed database management system that is capable of managing vast amounts of data across several commodity servers. The NoSQL database is trusted by millions of companies for high availability and easy scalability without compromising performance. Initially developed by Facebook and later open-sourced, the platform is now maintained by the Apache Software Foundation.
Two of the most critical aspects of Cassandra are fault tolerance and horizontal scalability. These make the platform ideal for handling mission-critical data.
At its core, Cassandra employs a distributed architecture known as a peer-to-peer (P2P) model. This means that there is no master-slave relationship among nodes in the cluster. Instead, all nodes are equal and communicate with each other in a decentralized manner.
The distributed nature of Cassandra allows it to provide high scalability and fault tolerance. Data is automatically partitioned and distributed across multiple nodes in a cluster, eliminating single points of failure. Each node in the cluster is responsible for a portion of the data, and the system can dynamically handle the addition or removal of nodes without downtime.
How Does Apache Cassandra Support Data Infrastructure Scaling?
Scalability refers to the addition of more computational resources to a database to gain more throughput. Essentially, scalability can be categorized into two types: vertical and horizontal.
Vertical scalability involves shifting from one system to another system that has a greater capacity in terms of RAM, CPU, and storage. Although this may seem like the most undeviating approach, vertically scaling a database is quite expensive, both in terms of financial expenditure and resource dedication.
On the other hand, a system is horizontally scalable if the hardware is added incrementally. In other words, the hardware is added to provide a linear increase in capacity without needing any reconfiguration. This also doesn’t cause downtime of existing nodes.
Apache Cassandra fulfills the needs of an ideal horizontally scalable system by enabling the seamless addition of nodes. With Cassandra, companies can easily add more nodes to the cluster as and when there is a need for more capacity.
As new nodes are added to the cluster, Cassandra’s performance scales linearly. This means that adding more machines increases the overall throughput and capacity of the system. Cassandra achieves this scalability by evenly distributing the data and workload across the nodes. Each additional node contributes to the overall processing power and storage capacity of the cluster.
Also, Cassandra supports dynamic addition and removal of nodes in the cluster. New nodes can be easily added to the cluster without requiring any downtime or affecting the availability of the system. Cassandra automatically redistributes the data to accommodate the new nodes and balance the load. Similarly, nodes can be removed from the cluster, and Cassandra automatically adjusts the data distribution accordingly.
Scaling Data Infrastructure with Apache Cassandra
Algoscale helped one of its clients, a UK-based internet marketing service to leverage Cassandra in their data-driven ad targeting endeavors. The client aimed to maximize the potential of their data warehouse, segment campaigns, attribute responses, and optimize ad campaign budgets.
The experts at Algoscale utilized Apache Cassandra as a key component of their solution. They integrated data from various sources, including audience information, content preferences, and advertising consumption behaviors, into Cassandra’s distributed database. This allowed for efficient storage, retrieval, and analysis of massive amounts of data in real-time.
By harnessing the scalability and fault-tolerant nature of Cassandra, we achieved fast processing speeds and seamless data management. Additionally, Cassandra’s ability to dynamically add or remove nodes enabled the system to scale effortlessly as data volumes increased. With the power of Apache Cassandra, Algoscale delivered a robust and scalable solution, enabling their client to make data-driven decisions and achieve better campaign outcomes. All this resulted in improved revenue generation for the client.
Conclusion
Apache Cassandra is the best choice for creating a scalable data infrastructure. The continuous developments in Cassandra and its surrounding ecosystem have made it a widely popular option for enterprises that prioritize scalability.
Algoscale is a leading software development company that can help you harness the full potential of Apache Cassandra’s distributed database system. From consulting and architecture design to deployment and customer management, we offer a comprehensive range of services. Our experts can enable enterprises to leverage Cassandra effectively, ensuring optimal performance, scalability, and reliability in their distributed database deployments. Get in touch with us to know more.
