Hadoop and Spark differ in architecture, performance, scalability, cost and deployment. They offer distinct strengths for modern cloud-native data pipelines.
To the uninitiated, it might seem that Hadoop is to Spark what Pepsi is to Coke: similar, broadly interchangeable brands with some subtle but important differences.
Hadoop and Spark are two of the most widely used frameworks for processing large-scale data. They can address many of the same use cases and challenges, but their design priorities differ.
When you dig into the details, it becomes clear that there are key differences between Hadoop and Spark regarding how they process data. Hadoop is optimized for batch processing and persistent storage, so it supports scalable, cost-effective data management. Spark emphasizes in-memory processing and real-time analytics, making it a better fit for low-latency workloads.
Sometimes Hadoop and Spark work best when paired together. Understanding how these frameworks compare in terms of performance, scalability, deployment and cost is essential for selecting the right tool to use or deciding when to use them together.
Batch vs. stream processing and persistent vs. in-memory storage
Before getting further into a comparison of Hadoop and Spark, it helps to define two core concepts behind their designs.
Batch processing and stream processing
A team can process data in either batches or streams. Batch processing groups data and handles it collectively, whereas stream processing handles each data object as it arrives, without waiting to group it. Batch processing can be more resource-efficient for large volumes of data, but stream processing supports real-time management that doesn't need to wait for batching.
Persistent storage and in-memory storage
Persistent storage, such as hard disks, retains data over time and is relatively inexpensive but slow to read/write data. In-memory storage uses hardware, such as RAM, to store information temporarily; this enables much faster access speeds, which translates to better performance. The downside is that in-memory storage media is expensive, and any data stored in memory will disappear permanently if the machine hosting it shuts down.
What is Apache Hadoop?
Hadoop is an open source framework for processing large data sets. Organizations typically use it for batch workloads and persistent storage, though it can support stream processing with add-on tools. Hadoop primarily works with data that is stored on disk, but it can work with data stored in volatile memory under certain circumstances.
Apache Hadoop originated in 2006 as an open source alternative to MapReduce, a distributed computing framework used internally by Google. Hadoop aimed to democratize big data processing by making it possible for any organization to work with very large data sets spread across clusters of servers.
The term large is relative when describing the size of a data set. There's no data processing threshold for when to use Hadoop. In general, Hadoop is best suited for use cases that involve so much data that it can't feasibly reside on a single server and must instead be spread across multiple machines. Hadoop excels in these circumstances because it can pull data from a cluster of servers and process it in parallel.
What is Apache Spark?
Apache Spark is an open source framework for big data processing. It focuses primarily on stream processing and in-memory storage. Spark began as an academic research project at the University of California, Berkeley in 2009 and became open source in 2010. While Hadoop already existed, Spark addressed large-scale data processing use cases where Hadoop didn't excel, specifically those that required processing data very quickly and/or in real time.
Like Hadoop, Spark processes data distributed across a cluster of servers, and it does it in parallel. It works with data on virtually any scale.
Similarities between Hadoop and Spark
Hadoop and Spark both pull data from a cluster of servers and process that data quickly and efficiently. This means that either framework can support different use cases:
Data analytics, which involves parsing data sets to identify trends or anomalies.
Data transformation, or the process of converting data from one format to another.
Over time, Hadoop and Spark's capabilities have overlapped because each can be configured to support batch or stream processing and work with data stored persistently or in memory. Historically, this was not the case. But today, the two frameworks have evolved in ways that have made them more flexible, enabling organizations to adapt either tool to meet a variety of workload demands. This overlapping functionality has blurred the distinctions between Hadoop and Spark.
The key differences for Hadoop vs. Spark
Although it is technically possible to configure Hadoop and Spark to work in the same ways under many circumstances, they remain distinct and still favor different use cases.
Batch vs. stream processing
Hadoop's focus is on processing data in batches, while Spark is designed for stream processing. However, Hadoop is simpler to deploy than Spark and more efficient when used for batch workloads, while Spark is more intuitive to use for stream applications.
As a result, teams choose Hadoop when data accumulates before it can be analyzed or transformed, such as with log analytics or generating product recommendations on a retail website, and they choose Spark when data must be processed in real time, such as for anomaly detection in a cybersecurity context or fraud detection in payment processing.
Persistent vs. in-memory storage
Hadoop is designed to read/write data from persistent storage, but it can be adapted to access data in memory. In-memory processing with Hadoop typically requires running Spark on top of a Hadoop cluster or using abstractions that connect the system to in-memory storage arrays while presenting it to Hadoop as persistent storage.
Spark processes data in memory by default, but it can also use persistence or caching features to fetch data that is stored on disk.
Although both frameworks can work with both types of data, it doesn't mean that they're equally simple to deploy in all circumstances. They can support these respective use cases without requiring add-ons or abstractions. This means that they are easier to configure, but it also leads to better performance because excessive extensions can increase resources required to run Hadoop or Spark, resulting in fewer resources available for actual data processing.
Performance
Spark generally outperforms Hadoop, particularly for processes that benefit from streaming and in-memory data processing. Even when Hadoop is configured to use the same processing techniques, Spark will generally be able to read/write and transform information faster.
As a result, Spark is generally the better choice when performance is a priority.
Scalability
Hadoop is more scalable than Spark because it runs efficiently on low-cost servers. Hadoop clusters are generally easier to expand because they don't require extensive memory resources, which means organizations can typically scale up Hadoop by adding cheap servers.
Spark also scales, but scaling up a Spark cluster requires adding more memory-rich servers, which is more expensive.
As a result, performance gains in Hadoop are achieved by simply adding more servers to it. With Spark, practical limitations related to the cost of memory make it more challenging to increase cluster size to boost performance.
Deployment options
Hadoop and Spark support flexible deployment across most infrastructure types -- including on-premises servers and cloud servers -- both where an organization sets up and manages Hadoop or Spark itself and on fully managed cloud services that offer hosted versions of Hadoop and Spark. Both frameworks can also run directly on servers or be orchestrated through platforms such as Kubernetes.
However, how an organization deploys Hadoop or Spark affects performance. For example, when running Spark on Kubernetes, stream processing capabilities might perform suboptimally due to the time required for Kubernetes to start a Spark instance. Though this is only a matter of seconds, it is still a delay. Kubernetes-based deployment poses less of an issue for Hadoop since Hadoop's batch processing model doesn't require real-time startup.
Cost
As open source technologies, Hadoop and Spark are both free to use, though commercial distributions might require licensing fees. However, their operational models can have additional cost implications.
Spark typically costs more because it relies on in-memory storage, which is more expensive than disk-based alternatives. Due to the higher cost of the host infrastructure, organizations will typically end up paying more to operate a Spark cluster, whether they set up servers on their own or pay for a managed, cloud-based Spark service.
That said, the architecture and configuration can have a major effect on cost. A poorly tuned Hadoop cluster that includes unnecessary nodes or inefficient data transformations might cost more to operate than a cost-optimized Spark cluster. Organizations should decide based on the details of each environment rather than assume that Hadoop is universally cheaper to use than Spark.
Hadoop or Spark, or Hadoop and Spark?
Which framework is best for a given use case? Hadoop is well suited for workloads where scalability and cost-efficiency are priorities. It also works best when data doesn't need to be streamed, although some extensions help make Hadoop capable of stream processing.
Hadoop and Spark are not mutually exclusive. Many organizations deploy both using a hybrid setup, with each framework serving a distinct role.
Meanwhile, Spark is a better fit if performance and low latency are the top priorities. Spark clusters usually cost more than Hadoop clusters, but Spark's ability to work with data stored in memory yields faster data processing.
However, one must consider that Hadoop and Spark are not mutually exclusive. Many organizations deploy both using a hybrid setup, with each framework serving a distinct role:
The "base" cluster runs on Hadoop for data storage and initial processing.
Spark operates on top of Hadoop, integrated using its resource manager as needed.
Hadoop is responsible for ingesting data and performing initial processing.
Data tasks or pipelines that require additional processing, and for which high performance is key, can be handed off to Spark.
The output of jobs processed by Spark can be pushed back to Hadoop for additional processing, if desired.
This setup lets users benefit from Hadoop's scalability and cost-effectiveness while still providing access to Spark for faster processing.
One example of an effective hybrid architecture is in payment processing operations. For the most part, payment data can be processed in batches because it's acceptable to have some delay between when data originates and when processing is complete. However, as part of payment processing operations, a business might want to detect fraudulent payments to block them in real time.
Spark would be useful for this part of the payment processing workflow because it could identify anomalous payment data faster than Hadoop. The business might use Hadoop for most aspects of payment processing but integrate with Spark for fraud detection. If workloads fall cleanly within the categories that align best with either Hadoop or Spark, there's no reason to deploy both solutions together, and doing so would add unnecessary cost and complexity.
Chris Tozzi is a freelance writer, research adviser, and professor of IT and society. He has previously worked as a journalist and Linux systems administrator.
