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January 14, 2025

Apache Spark vs. Hadoop: Key Differences and Use Cases

William Crowell
William Crowell

Hadoop,

Databases

Apache Spark vs. Hadoop isn't the 1:1 comparison that many seem to think it is. While they are both involved in processing and analyzing Big Data, Spark and Hadoop are actually used for different purposes. Depending on your Big Data strategy, it might make sense to use one over the other, or use them together.

In this blog, our expert breaks down the primary differences between Spark vs. Hadoop, considering factors like speed and scalability, and the ideal use cases for each.

Editor's Note: This blog was originally published in 2021 and was updated and expanded in 2025. 

Table of Contents

  1. What Is Apache Spark?
  2. What Is Apache Hadoop?
  3. Apache Spark vs. Hadoop at a Glance
  4. Spark vs. Hadoop: Key Differences
  5. Spark Use Cases
  6. Hadoop Use Cases
  7. Using Hadoop and Spark Together
  8. Final Thoughts
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What Is Apache Spark?

Apache Spark was developed in 2009 and then open sourced in 2010. It is now covered under the Apache License 2.0. Its foundational concept is a read-only set of data distributed over a cluster of machines, which is called a resilient distributed dataset (RDD). 

RDDs were developed due to limitations in MapReduce computing, which read data from disk by reducing the results into a map. RDDs work faster on a working set of data which is stored in memory which is ideal for real-time processing and analytics. When Spark processes data, the least-recent data is evicted from RAM to keep the memory footprint manageable since disk access can be expensive. 

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What Is Apache Hadoop?

Hadoop is a data-processing technology that uses a network of computers to solve large data computation via the MapReduce programming model. 

Compared to Spark, Hadoop is a slightly older technology. Hadoop is also fault tolerant. It knows hardware failures can and will happen and adjusts accordingly. Hadoop splits the data across the cluster and each node in the cluster processes the data in parallel very similar to divide-and-conquer problem solving.

For managing and provisioning Hadoop clusters, the top two orchestration tools are Apache Ambari and Cloudera Manager. Most comparisons of Ambari vs. Cloudera Manager come down to the pros and cons of using open source or proprietary software.

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Apache Spark vs. Hadoop at a Glance

The main difference between Apache Spark vs. Hadoop is that Spark is a real-time data analyzer, whereas Hadoop is a processing engine for very large data sets that do not fit in memory. 

Hadoop can handle batching of sizable data proficiently, whereas Spark processes data in real-time such as streaming feeds from Facebook and Twitter/X. Spark has an interactive mode allowing the user more control during job runs. Spark is the faster option for ingesting real-time data, including unstructured data streams. 

Hadoop is optimal for running analytics using SQL because of Hive, a data warehouse system that is built on top of Hadoop. Hive integrates with Hadoop by providing an SQL-like interface to query structured and unstructured data across a Hadoop cluster by abstracting away the complexity that would otherwise be required to write a Hadoop job to query the same dataset. Spark also has a similar interface, Spark SQL, which is part of the distribution and does not have to be added later. 

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Spark vs. Hadoop: Key Differences

In this section, let's compare the two technologies in a little more depth. 

Ecosystem

The core computation engines of Hadoop and Spark differ in the way they process data. Hadoop uses a MapReduce paradigm that has a map phase to filter and sort data and a reduce phase for aggregating and summarizing data. MapReduce is disk-based, whereas Spark uses in-memory processing of Resilient Distributed Datasets (RDDs), which is great for iterative algorithms such as machine learning and graph processing. 

Hadoop comes with its own distributed storage system, the Hadoop Distributed File System (HDFS), which is designed for storing large files across a cluster of machines. Spark can use Hadoop’s HDFS as its primary storage system, but it also supports other storage systems like S3, Azure Blob Storage, Google Cloud Storage, Cassandra, and HBase.

Hadoop and Spark include various data processing APIs for different use cases. Spark Core provides functionality for Spark jobs like task scheduling, fault tolerance, and memory management. Spark SQL allows SQL-like queries on large datasets and integrates well with structured data. It supports querying both structured and semi-structured data. The Spark Streaming component provides real-time stream processing by dividing data streams into small batches. MLlib and GraphX are libraries for machine learning algorithms and graph processing, respectively, that run on Spark.

Hadoop includes MapReduce, which is the core API for data processing in Hadoop.  The following tools can be added to Hadoop for data processing: 

  • Apache Hive is a data warehouse system built on top of Hadoop for querying and managing large datasets using a SQL-like language. 

  • Apache HBase is a distributed NoSQL database that runs on top of HDFS and is used for real-time access to large datasets.  

  • Apache Pig is a platform for analyzing large datasets that uses a scripting language (Pig Latin) to express data transformations.

For cluster management, YARN (Yet Another Resource Manager) is the most common approach to run Spark applications to run transparently in tandem with Hadoop jobs in the same cluster which provides resource isolation, scalability, and centralized management. 

Spark does have a few more cluster management configurations than Hadoop.  Apache Mesos is a distributed systems kernel that can run Spark, and Spark also has native support for Kubernetes, which can be used for containerized deployment and scaling capabilities in Spark clusters. 

For fault tolerance, Hadoop has data block replication that ensures data accessibility if a node fails, and Spark uses RDDs to reconstruct data in the event of failure. 

Real-time processing and machine learning are both included with Spark. Spark Streaming natively supports real-time data processing with low latency, but Hadoop requires tools like Apache Storm or Apache Flink to accomplish this task. MLLib is Spark’s machine learning library, and Apache Mahout can be used with Hadoop for machine learning.  

Features

Hadoop has its own distributed file system, cluster manager, and data processing. In addition, it provides resource allocation and job scheduling as well as fault tolerance, flexibility, and ease of use.

Spark includes libraries for performing sophisticated analytics related to machine learning, AI, and a graphing engine. The scheduling implementation between Hadoop and Spark also differs. Spark provides a graphical view of where a job is currently running, has a more intuitive job scheduler, and includes a history server, which is a web interface to view job runs.  

Performance and Cost Comparison

Hadoop accesses the disk frequently when processing data with MapReduce, which can yield a slower job run. In fact, Spark has been benchmarked to be up to 100 times faster than Hadoop for certain workloads. 

However, because Spark does not access to disk as much, it relies on data being stored in memory. Consequently, this makes Spark more expensive due to memory requirements. Another factor that makes Hadoop more cost-effective is its scalability; Hadoop mixes nodes of varying specifications (e.g. CPU, RAM, and disk) to process a data set. Cheaper commodity hardware can be used with Hadoop. 

Other Considerations

Hadoop requires additional tools for Machine Learning and streaming which come included in Spark. Hadoop can also be very complex to use with its low-level APIs, while Spark abstracts away these details using high-level operators. Spark is generally considered to be more developer-friendly and easy to use. 

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Spark Use Cases

Spark is great for processing real-time, unstructured data from various sources such as IoT, sensors, or financial systems and using that for analytics. The analytics can be used to target groups for campaigns or machine learning. Spark has support for multiple languages like Java, Python, Scala, and R, which is helpful if a team already has experience in these languages. 

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Hadoop Use Cases

Hadoop is great for parallel processing of diverse sets of large amounts of data. There is no limit to the type and amount of data that can be stored in a Hadoop cluster. Additional data nodes can be added to address this requirement. It also integrates well with analytic tools like Apache Mahout, R, Python, MongoDB, HBase, and Pentaho.

It's also worth noting that Hadoop is the foundation of some proprietary Big Data platforms, but organizations that want to go 100% open source with their Big Data management and have more control over their data should consider the Hadoop Service Bundle as an alternative. 

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Using Hadoop and Spark Together

Using Hadoop and Spark together is a great way to build a powerful, flexible big data architecture. Typical use cases are large-scale ETL pipelines, data lakes and analytics, and machine learning. Hadoop’s scalable storage via HDFS can be used for storing large datasets and Spark can perform distributed data processing and analytics. Hadoop jobs can be used for large and long-running batch processes, and Spark can read data from HDFS and perform complex transformations, machine learning, or interactive SQL queries. Spark jobs can run on top of a Hadoop cluster using Hadoop YARN as the resource manager. This leverages both Hadoop’s storage and Spark’s faster processing, combining the strengths of both technologies.

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Final Thoughts

Organizations today have more data at their disposal than ever before, and both Hadoop and Spark have a solid future in the realm of open source Big Data infrastructure. Spark has a vibrant and active community including 2,000 developers from thousands of companies which include 80% of the Fortune 500.

For those thinking that Spark will replace Hadoop, it won't. In fact, Hadoop adoption is increasing, especially in banking, entertainment, communication, healthcare, education, and government. It's clear that there's enough room for both to thrive, and plenty of use cases to go around for both of these open source technologies.

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