Paulina Twarogal
Marcin Dobosz
Handling large volumes of data has always been a serious challenge for different businesses. For decades, they relied on conventional relational database management systems (RDBMS) to structure and analyze their data. However, everything changed when Facebook came along. It then suddenly turned out that a traditional RDBMS was not quite enough for its needs.
This led to the creation of Apache Cassandra, a robust open-source platform. It has quickly become the choice for organizations dealing with large unstructured data, looking for solutions that are both highly available and scalable. This is particularly important in the field of banking data management.
So, what’s the story behind Apache Cassandra? How does it simplify the complexity of data processing? Read on to learn more about Apache Cassandra—its role, workings, strengths, and limitations.
What is Apache Cassandra?
In the pursuit of powering Facebook’s inbox search feature, Apache Cassandra was born—a distributed, wide-column, NoSQL database management system. Developed by Avinash Lakshman and Prashant Malik, it was introduced to the public as an open-source project in July 2008. In 2009, it gained Apache project status. That was a significant move that showed a major shift in database technology. Since then, Cassandra has been regularly updated to ensure consistently high performance.
By design, NoSQL databases are open-source, non-relational, and mostly distributed. And Cassandra happens to be one of the most popular NoSQL database systems in the world. Its strength lies in effectively handling large volumes of fast-moving data across multiple commodity servers, all without a single point of failure.
That’s why companies such as Facebook, Instagram, Netflix, Twitter, and Reddit are deploying Apache Cassandra for mission-critical features. This preference extends beyond social media to include large enterprises across sectors like banking, automation, IoT, and e-commerce—all sharing the common need for high-speed information relay.
How does it work?
Apache Cassandra is based on three core principles: architecture, partitioning, and replicability.
Architecture: At its most basic level, Cassandra is structured as a peer-to-peer system, composed of a cluster of nodes. The core of Cassandra’s architecture is the idea that each node has equal importance. These nodes, organized into clusters, collectively store data within data centers. This design ensures constant data availability, even if one data center unexpectedly goes offline.
A key feature of Cassandra’s architecture is its flexibility. By adding more nodes, it can be effortlessly expanded to house more data without any downtime. Conversely, when things get too crowded, it’s possible to scale back easily, a feature particularly useful in banking data management scenarios.
Partitioning: At the heart of Cassandra’s data management is a partitioning system that governs how information is stored and retrieved. A partition key, typically a hashed representation of the data, guarantees constant (O(1)) access time. Each node is responsible for its unique set of tokens based on the partitioning key that helps the system find the data.
When new data comes in, a hash function is applied to the partition key. Then, the coordinator node (the node a client connects to with a request) ensures it’s delivered to the right node with the matching token.
Replicability: Cassandra improves reliability through data replication across nodes, called replica nodes. The replication factor (RF) dictates the number of identical copies, aligning with the CAP theorem—a fundamental concept in distributed database design. According to this theorem, a distributed system can achieve, at most, two out of three guarantees: consistency, availability, and partition tolerance.
Cassandra leans towards availability and partition tolerance while aiming for eventual consistency. In the face of network partitions, it prioritizes remaining available, allowing nodes to operate independently. It may potentially lead to temporary inconsistencies. Yet, the system eventually reaches a consistent state.
Embracing eventual consistency ensures that even during periods of independent node operation due to network partitions, Cassandra reconciles inconsistencies. This preserves data integrity. Moreover, it supports High Availability (HA) and enables fast recovery. In cases where temporarily disrupted nodes resume operation, data consistency and overall system functionality are maintained.
Benefits: What makes Cassandra unique?
It’s not easy to deal with the complexities of modern software. That’s why it requires solutions that balance scalability, high availability, and uninterrupted performance. Traditional RDBMS such as PostgreSQL, MSSQL, or Oracle often fall short in meeting these demands.
However, Apache Cassandra emerges as a unique solution for businesses that manage large datasets across many servers. It ensures resilience against data loss and server outages which makes it perfect for those who prioritize data integrity, continuous operation, and robust data security. This proves to be especially valuable for data management in banking, where high volumes of customer transactions and real-time data analysis are crucial.
The rise in Cassandra’s popularity in recent years is not without reason. This distributed database delivers tremendous value. To understand what sets Cassandra apart, let’s delve into the aspects discussed below.
Open source availability
Is there anything more exciting than getting a useful product for free? Probably not, and it would be hard to resist. Much of Cassandra’s widespread popularity is attributed to its open-source availability. Hosted by Apache, it’s accessible to everyone at no cost. Moreover, the availability of enterprise services, like DataStax Enterprise, builds on open-source Cassandra with performance improvements and extended support.
Scalability
Nowadays, when thinking of technology, one thought comes to mind: Everything progresses dynamically. So, for businesses to thrive, easy scalability is imperative. Otherwise, falling behind the competition is inevitable.
In 2018, Amazon reportedly lost nearly USD 100 million in just one minute of outage, overwhelmed by a surge of users flooding the site. A scalable system, like Cassandra, acts as a safeguard against missed opportunities during peak traffic. As a result, it saves businesses from the repercussions of costly and disruptive outages, a crucial aspect for data management in banking.
With Cassandra, businesses can easily scale up or down. What’s intriguing is that any number of nodes can be added or removed without disruption. Moreover, scaling Cassandra doesn’t require cluster restarts or query changes. This keeps throughput high, even with many nodes. As you scale, both read and write throughput increase together, which ensures zero downtime or interruptions for applications. Not surprisingly, 57% of Cassandra users identify high scalability as the primary reason for choosing it.
High Availability via data replication
Nowadays, a database needs to handle data from multiple geographic sources. In contrast to traditional primary-secondary architectures, where a failure could halt operations, Cassandra’s architecture stands out. Here, every node can read and write, making quick data replication across regions possible. This design guarantees an optimal user experience around the world. Even if a node goes down, data stays accessible. And that’s all thanks to automatic traffic rerouting to the nearest healthy node.
Cassandra’s impact is clear: IBM research reveals that bad data costs US organizations a staggering USD 3.1 trillion annually. With Cassandra’s automatic data replication, worries about duplicative work, lost intellectual property, or inaccessible customer data fade away. The result? Savings that eliminate the need for a separate disaster recovery data center.
High fault tolerance
In an ideal scenario, systems would always function flawlessly, even in the face of component failures. The truth is that Cassandra makes it possible through its peer-to-peer architecture and robust data replication.
Transparent fault detection and recovery keep applications running smoothly, even if nodes go offline. For example, DataStax Enterprise, a leading Cassandra distribution, adds an extra layer of assurance with built-in repair services that instantly address issues. Unlike primary-secondary architectures that demand manual intervention, Cassandra ensures automatic fault tolerance. So, when a node fails any hands-on fixes aren’t necessary.
High performance – speed
Speed matters. Today, users expect quick outcomes, and when things don’t go smoothly and quickly, they’re very likely to go elsewhere. Whether it’s a website or an app, 53% of customers expect it to load within two seconds. And around 70% of them won’t support a business that has poor website performance.
High-performance systems, like Cassandra, go beyond flashy capabilities. They empower employees to accomplish tasks and ensure positive user experiences. This aligns perfectly with the demand for instant data delivery in today’s fast-moving world.
Where does Cassandra fall short?
Cassandra brings numerous advantages to the table. That’s a fact, but… it’s also important to realize that it has its challenges as well. Although these obstacles can be managed with the right strategy and resources, it’s crucial to explore them in detail and understand why many organizations opt for extra assistance to fully maximize Cassandra’s potential, especially for complex data management tasks.
Complexity
Cassandra is a complex database system. So, setting it up and keeping it running can be difficult. To achieve optimal performance, developers need careful planning and configuration because of its distributed architecture.
Query language
Another aspect that doesn’t make using Cassandra easier is its query language called CQL (Cassandra Query Language). Cassandra isn’t a relational database management system, but its query language has an SQL-like syntax. This similarity can be misleading. It may give developers the impression of working with a familiar data model. In reality, however, there are significant differences between the two.
Limited joins
In traditional relational databases, joins are used to combine data from multiple tables based on related columns. However, being a NoSQL database designed for horizontal scalability, Cassandra doesn’t provide native support for joins. As a result, querying data that’s spread across multiple tables can be challenging.
To address this limitation, Cassandra relies on a denormalization approach. It involves duplicating and storing data in multiple tables to optimize read performance. While this can speed up query performance, it also makes the data modeling process more complex. Developers need to carefully manage duplicated data across tables.
High storage overhead
In Cassandra, data is replicated across multiple nodes in a cluster to ensure fault tolerance and high availability. This replication, however, comes at a cost in terms of storage. Since each node in the cluster is responsible for storing a copy of the data, the total storage consumption is high.
For instance, in a three-node cluster where data is replicated three times for fault tolerance, the storage capacity needed is tripled. This setup improves resilience, but it also means higher storage requirements, particularly in larger clusters with higher replication factors.
Eventual consistency
Eventual consistency in Cassandra involves a gradual propagation of data updates across the cluster, introducing a time lag for all nodes to reflect the latest changes. A delay like this can lead to temporary inconsistencies in the data until the updates are fully distributed.
The approach focuses on keeping the system available and resilient to faults. As a trade-off, there might be a temporary phase during which different nodes show slightly different data versions before eventually reaching a consistent state.
Fragmented support in Cassandra’s adoption
Cassandra’s free accessibility makes its adoption easy, but this simplicity leads to unique challenges. To be more precise, teams often independently implement the database which results in an ad hoc approach. As the deployment expands across an organization, the demand for consistent risk management and support services grows. However, finding support for Cassandra’s adoption is much more difficult than with traditional RDBMS.
The free and open-source nature of Cassandra often leads to a fragmented support system—incorporating in-house resources, the open-source community, and third-party agencies—with varying levels of Cassandra expertise and response times. Such a patchwork approach is less efficient, cost-effective, and reliable overall.
Limited expertise
Cassandra benefits from a robust community, yet the wealth of knowledge isn’t intuitively organized. Implementing and configuring Cassandra poses a steep learning curve for developers as all the data must be modeled properly before using it.
Hiring in-house expertise becomes challenging. It’s due to a limited talent pool. Employees need to self-educate through open-source documentation, community assistance, and trial and error. This self-driven learning approach slows down adoption and creates additional work for IT.
Conclusion
Apache Cassandra turns out to be an ideal solution for organizations dealing with large datasets, particularly in banking, by simplifying the storage and analysis of extensive financial data. This, in turn, helps financial institutions improve how they manage information.
Despite the advantages Cassandra offers for financial operations, it’s crucial to be aware of the challenges that come with it. Fortunately, with strategic guidance and support during implementation, organizations can fully leverage the potential of this database.
Unsure if Cassandra is the right choice for your business? Neontri’s experts are here to guide you.
