Apache Cassandra is a NoSQL database ideal for high-speed, online transactional data, while Apache Hadoop is a big data analytics system that focuses on data warehousing and data lake use cases. Let’s look at their similarities, differences, best applications, and how they can be used together. But, first, let’s spend a little more time on Hadoop.
Hadoop And Its Key Components
Apache Hadoop, an Apache Software Foundation Project, is a framework to store and process big data in a distributed environment. It excels at performing near-time and batch-oriented analytics of historical data. Hadoop helps run analytics using high volumes of historical/line of business data on commodity hardware.
Unlike Cassandra, Hadoop is an ecosystem consisting of several components. Here are the four essential ones:
- Hadoop Distributed File System (HDFS) is Hadoop’s primary storage system. It’s a distributed file system that looks like any other file system except that when you move a file on HDFS, it is split into many small files, and each of those files is replicated and stored on three servers (as a default, the number can be customized) for fault tolerance. With HDFS, enormous amounts of data— structured and unstructured—can be stored in a distributed fashion. Another option, with Hadoop, is to store the data in HBase. Part of the Apache Hadoop project, HBase is an open-source, NoSQL, distributed, scalable database that runs on top of HDFS. Modeled after Google’s Bigtable, it brings similar capabilities to Hadoop, allowing rapid record-level data access. HBase is designed to support data warehouse/data lake-style use cases, and is not typically utilized for distributed web and mobile applications that need a high-performance online database.
- MapReduce is a programming paradigm for processing and handling large datasets. It splits requests into smaller requests which are sent to many small servers to be processed in a parallel manner. As a result, you can quickly process very large datasets.
- Common/Core is a package containing libraries and utilities to support Hadoop modules.
- YARN, or Yet Another Resource Negotiator, is a resource management platform included for managing computing resources and scheduling Hadoop tasks.
A Cassandra vs. Hadoop comparison: Architecture and more
Cassandra and Hadoop’s ecosystem of components have many similarities. Here are some notable ones:
- Members of the Apache Software Foundation
- Built to store and process massive amounts of data
- Distributed and scalable architecture
- Designed to take advantage of low-cost commodity servers
- Apply replication
- Support indexing
- Deliver analytics
However, they use different approaches to tackle various tasks, and, because of that, each is more effective than the other in certain areas. Let’s review their architecture and several other factors to see how they compare, where they excel, and where they’re lacking.
| Cassandra | Hadoop | |
| Architecture | Peer-to-peer architecture where data is distributed across nodes in a cluster. There is no central hub or master node. Instead, every node has the same role, and each can accept read and write requests. | Master/slave architecture. This approach has a master node, and multiple slave nodes. Only the master node can write to the database. It’s also responsible for managing key operations, and storing data in HDFS. |
| Data center distribution | Highly distributed. Can be deployed across regions, and even globally. | Typically deployed in a single data center, or geographically co-located with other servers. |
| Replication and fault tolerance | By default, data is replicated to all the nodes in the cluster. However, the number of replicas can be customized. Since Cassandra normally has more nodes storing replicas than Hadoop, it is considered more reliable and fault tolerant. The gossip protocol is used to broadcast ongoing status to all the peer nodes in the cluster, and, if one goes offline, the others take on its responsibility and carry on without a blip. | Hadoop creates three copies by default. As with Cassandra, you can alter this number. If the master and secondary nodes fail, all data will be lost. |
| Latency | Low, since it’s based on NoSQL. Read/write functions are fast. | Higher. Write latency is less than reading. |
| Data formats | Handles most structured, semi-structured and unstructured data, except for images. | Handles everything Cassandra does, plus images. |
| Indexing | Easy. Key-value pair data storage makes it straightforward. Allows the creation of multiple indexes, and includes many ways to quickly retrieve data. | Difficult. |
| CAP theorem (Consistency, availability, partition tolerance)</strong > | Supports availability and partition tolerance. That said, a trade-off can be made between consistency and availability. Consistency can be gained if replication and data consistency factors are tuned, but the availability guarantee will be lost. | Achieves consistency and partition tolerance with HDFS. |
| Access / query method | Uses Cassandra Query language (CQL). Since it’s similar to SQL, most developers will pick it up quickly. | Uses MapReduce to read/write. |
| Data compression | Its compression algorithm can reduce storage by up to 80%, without any overhead. | Between 10-15% |
And, when specifically compared to HBase, Cassandra supplies:
- Higher performance
- True continuous, “always-on” availability, with no single point of failure
- Powerful and easy multi-data center / cloud availability zone support
- A simpler architecture (masterless), with easier setup and fewer requirements
- Easier development (SQL-like language with CQL, and more)
Use cases for Cassandra vs. Hadoop
As we saw in the last section, Cassandra and Hadoop have their own strengths and weaknesses. But how do those attributes apply to real-world usage? What kinds of situations should you use one over the other?
Cassandra is likely a better fit for:
- Real-time processing of data
- Always-on, high-speed, online transactional data and applications
- Handling a high level of interaction and concurrent traffic.
- Situations where each interaction is processing a small amount of data
- Applications requiring high availability of large-scale reads
- Data warehousing structured data, since HDFS doesn’t have record-level indexing
Note: HBase is sometimes used for an online application just because an existing Hadoop implementation exists at a site, not because it’s the right fit for the application. HBase is typically not a good choice for developing always-on online applications and is nearly two-to-three years behind Cassandra in many technical respects.
Here are just a few of themany ways Cassandra is used</a >:
- E-commerce and inventory management
- Personalization, recommendations, and customer experience
- Internet of things and edge computing
- Fraud detection and authentication
Hadoop delivers considerable value to organizations by providing cost-effective processing and analysis of vast amounts of data. That analysis is often used to make mission critical decisions.
With that in mind, you should consider Hadoop for:
- Running near-time, big data analytics on historical data
- Batch processing and storing extremely large volumes of data
- Data lakes and data warehousing (with the exception noted above for structured data)
Here are examples of how Hadoop and its components are used:
- Retail analytics
- Financial risk analysis, trading, and forecasting
- Healthcare applications, such as predicting patients at risk of serious illness
- Social networking sites that process incredibly high volumes, such as Twitter and Facebook
How Cassandra and Hadoop complement each other
While Cassandra and Hadoop are just the right fit for many situations, there are other times when it would be nice to take advantage of what each does best. Luckily, it’s possible to run them side-by-side or to tightly integrate them so you can do just that.
Here are a couple of scenarios highlighting when it might make sense to use Cassandra and Hadoop together.
Run Cassandra And Hadoop Side-By-Side To Take On Hot And Cold Data
As with legacy relational database applications, there is typically a need in modern web, mobile and IoT applications to have a database devoted to online operations (that includes analytics on hot data), and a batch-oriented data warehouse environment that supports the processing of colder data for analytic purposes.
As we’ve discussed, Cassandra is a perfect database choice for online web and mobile applications, while Hadoop targets the processing of colder, historical data in data lakes, warehouses, etc. By leaning on both Cassandra and Hadoop, an IT organization can effectively support the different analytic “tempos” needed to satisfy customer requirements and run the business.
Deploy Hadoop On Top Of Cassandra For Convenient Data Analytics And Reporting
Cassandra provides highly fault tolerant storage for online systems, and Hadoop excels at data analytics. Layering Hadoop on top of Cassandra. It turns out you can have the best of both worlds by deploying Hadoop on top of Cassandra. This allows companies to conveniently leverage the immense amount of data they already have in Cassandra, in real time, to conduct the level of operational analytics and reporting Hadoop provides. Otherwise, you’d have to move data off Cassandra into HDFS to gain the level of operational analytics and reporting Hadoop provides. This also avoids the hassle of having to deal with the resource-intensive and complicated step of moving data from Cassandra to HDFS to accomplish the same thing.
Cassandra or Hadoop? There’s no wrong answer
So, what’s the right choice: Cassandra or Hadoop? If you need high availability and performance, with low latency, and need to conduct real-time processing of an online application, then Cassandra is likely the right choice. On the other hand, if you need batch processing or big data analytics on extremely large volumes of historical data, Hadoop is probably the way to go. There is no right or wrong answer, and many organizations run Cassandra and Hadoop side by side, or integrate them, to access the benefits of both.
