Apache Spark and Hadoop MapReduce are two popular big data processing frameworks used in the industry. Both of these frameworks have the capability to handle large-scale data processing, but they differ in terms of their architecture and design.

Here are five key differences between MapReduce vs. Spark:

  • Processing speed: Apache Spark is much faster than Hadoop MapReduce.
  • Data processing paradigm: Hadoop MapReduce is designed for batch processing, while Apache Spark is more suited for real-time data processing and iterative analytics.
  • Ease of use: Apache Spark has a more user-friendly programming interface and supports multiple languages, while Hadoop MapReduce requires developers to write code in Java.
  • Fault tolerance: Apache Spark's Resilient Distributed Datasets (RDDs) offer better fault tolerance than Hadoop MapReduce's Hadoop Distributed File System (HDFS).
  • Integration: Apache Spark has a more extensive ecosystem and integrates well with other big data tools, while Hadoop MapReduce is primarily designed to work with Hadoop Distributed File System (HDFS).

Both of these frameworks have their advantages and disadvantages, and the choice between them depends on the specific needs of the project at hand. In this comparison, we will delve deeper into the differences between Apache Spark and Hadoop MapReduce and explore their strengths and weaknesses in various big data processing scenarios.



For years, Hadoop MapReduce was the undisputed champion of big data — until Apache Spark came along. Since its initial release in 2014, Apache Spark has been setting the world of big data on fire. With Spark's convenient APIs and promised speeds up to 100 times faster than Hadoop MapReduce, some analysts believe that Spark is the most powerful engine for data analytics.

While Spark seems like it might eventually replace Hadoop MapReduce, you shouldn't count MapReduce out just yet. In this post, Integrate.io compares the two platforms to see which one comes out on top.

What Is Apache Spark?

In its developer's words, Apache Spark is "a unified analytics engine for large-scale data processing." Spark is maintained by the nonprofit Apache Software Foundation, which has released hundreds of open-source software projects. More than 1,200 developers have contributed to Spark since the project's inception.

Originally developed at UC Berkeley's AMPLab, Spark was first released as an open-source project in 2010. Spark uses the Hadoop MapReduce distributed computing framework as its foundation. Spark's developers created it to improve on several aspects of the MapReduce project, such as performance and ease of use, while preserving many of MapReduce's benefits.

How can Spark, an open-source data processing framework, crunch all information so fast? The secret is that Spark runs in-memory on the cluster, and it isn’t tied to Hadoop’s MapReduce two-stage paradigm. That makes repeated access to the same data much faster. Spark can run as a standalone application or on top of Hadoop YARN, where it can read data directly from HDFS. Dozens of major tech companies such as Yahoo, Intel, Baidu, Yelp, and Zillow have used Spark as part of their technology stacks.

Spark includes a core data processing engine, as well as libraries for SQL, machine learning, and stream processing. With APIs for Java, Scala, Python, and R, Spark enjoys a broad appeal among developers — earning it the reputation of the "Swiss army knife" of big data processing.

What Is Hadoop MapReduce?

Hadoop MapReduce is described as "a software framework for easily writing applications which process vast amounts of data (multi-terabyte data sets) in parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner."

The MapReduce paradigm consists of two sequential tasks: Map and Reduce (hence the name). Here's how each task works:

  • Map filters and sorts data while converting it into key-value pairs. 
  • Reduce then takes this input and reduces its size by performing some kind of summary operation over the data set.

MapReduce can drastically speed up big data tasks by breaking down large data sets and processing them in parallel. Google employees Jeff Dean and Sanjay Ghemawat first proposed the MapReduce paradigm in 2004: Apache Hadoop later incorporated it into its framework for distributed processing. 

Learn more about Integrate.io's native Hadoop HDFS connector.

The Differences Between MapReduce vs. Spark

The main differences between MapReduce and Spark are:

  • Performance
  • Ease of use
  • Data processing
  • Security

However, there are also a few similarities between Spark and MapReduce — not surprising, since Spark uses MapReduce as its foundation. The points of similarity when making Spark vs. MapReduce comparisons include:

  • Cost
  • Compatibility
  • Failure tolerance

Below, learn more details about the differences between Spark and MapReduce and the similarities between these technologies.

MapReduce vs. Spark: Performance

Apache Spark processes data in random access memory (RAM), while Hadoop MapReduce persists data back to the disk after a map or reduce action. In theory, then, Spark should outperform Hadoop MapReduce. Nonetheless, Spark needs a lot of memory. Much like standard databases, Spark loads a process into memory and keeps it there until further notice for the sake of caching. If you run Spark on Hadoop YARN with other resource-demanding services, or if the data is too big to fit entirely into memory, then Spark could suffer major performance degradation.

MapReduce kills its processes as soon as a job is done, so it can easily run alongside other services with minor performance differences.

Spark has the upper hand for iterative computations that pass over the same data many times. But when it comes to one-pass ETL-like jobs — for example, data transformation or data integration — that's exactly when MapReduce excels.

Bottom line: Spark performs better when all the data fits in memory, especially on dedicated clusters. Hadoop MapReduce suits data that doesn’t fit in memory and can run well alongside other services.

MapReduce vs. Spark: Ease of Use

Spark has pre-built APIs for Java, Scala, and Python, and also includes Spark SQL (formerly known as Shark) for the SQL savvy. Thanks to Spark’s simple building blocks, it’s easy to write user-defined functions. Spark even includes an interactive mode for running commands with immediate feedback.

MapReduce is written in Java and is infamously very difficult to program. Apache Pig makes it easier (although it requires some time to learn the syntax), while Apache Hive adds SQL compatibility to the plate. Some Hadoop tools can also run MapReduce jobs without any programming. For example, Integrate.io is a data integration service built on top of Hadoop that does not require any programming or deployment.

In addition, MapReduce doesn’t have an interactive mode, although Hive includes a command-line interface. Projects like Apache Impala and Apache Tez want to bring full interactive querying to Hadoop.

When it comes to installation and maintenance, Spark isn’t bound to Hadoop. Both Spark and Hadoop MapReduce are available in distributions by Hortonworks and Cloudera.

Bottom line: Spark is easier to program and includes an interactive mode. Hadoop MapReduce is more difficult to program, but several tools are available to make it easier.

MapReduce vs. Spark: Cost

Spark and MapReduce are open-source solutions, but you still need to spend money on infrastructure and data engineers. (The average data engineer salary in the United States is $95,568 per year, as of March 2023.) Both Spark and MapReduce can use commodity servers and run on the cloud. In addition, both tools have similar hardware requirements:

  Apache Spark  Apache Hadoop balanced workload slaves
Cores 8–16 4
Memory 8 GB to hundreds of gigabytes 24 GB
Disk 4–8 4–6 one-TB disks
Network 10 GB or more 1 GB Ethernet all-to-all
  Source Source

The memory in the Spark cluster should be at least as large as the amount of data you need to process because the data has to fit in memory for optimal performance. If you need to process extremely large quantities of data, Hadoop will be the cheaper option, since hard disk space is much less expensive than memory space.

On the other hand, considering the performance of Spark and MapReduce, Spark should be more cost-effective. Spark requires less hardware to perform the same tasks much faster, especially on the cloud, where you pay for compute power per use.

What about the question of staffing? Hadoop has been around since 2005, but historically, there has been a shortage of MapReduce experts out there on the market. In 2023, experts still urge business intelligence professionals to learn Hadoop as more businesses invest in this technology. 

While Spark has a faster learning curve than MapReduce, it has also suffered from a shortage of qualified experts in the past. The good news is that a wide array of Hadoop-as-a-service offerings and Hadoop-based services (like Integrate.io's data integration service) help alleviate these hardware and staffing requirements. Meanwhile, Spark-as-a-service options are available through providers such as Amazon Web Services.

Bottom line: Spark is more cost-effective according to the benchmarks. Hadoop MapReduce is likely less expensive for massive data volumes.

MapReduce vs. Spark: Compatibility

Apache Spark can run as a standalone application on top of Hadoop YARN or Apache Mesos on-premise or in the cloud. Spark supports data sources that implement the Hadoop Input format, so it can integrate with all of the same data sources and file formats that Hadoop supports. 

Spark also works with business intelligence tools via JDBC and ODBC.

Bottom line: Spark’s compatibility with various data types and data sources is the same as Hadoop MapReduce.

MapReduce vs. Spark: Data Processing

Spark can do more than plain data processing. It can also process graphs and includes the MLlib machine learning library. Thanks to its high performance, Spark can execute real-time processing and batch processing. Spark offers a "one-size-fits-all" platform that you can use rather than splitting tasks across different platforms, which adds to your IT complexity.

Hadoop MapReduce is great for batch processing. If you want a real-time option, you’ll need to use another platform like Impala or Apache Storm, and for graph processing, you can use Apache Giraph. MapReduce used to have Apache Mahout for machine learning, but it's since been ditched in favor of Spark and H2O.

Bottom line: Spark is the Swiss army knife of data processing, while Hadoop MapReduce is the commando knife of batch processing.

MapReduce vs. Spark: Failure Tolerance

Spark has retries per task and speculative execution, just like MapReduce. Nonetheless, MapReduce has a slight advantage here because it relies on hard drives rather than RAM. If a MapReduce process crashes in the middle of execution, it can continue where it left off, whereas Spark will have to start processing from the beginning.

Bottom line: Spark and Hadoop MapReduce both have good failure tolerance, but Hadoop MapReduce is slightly more tolerant.

MapReduce vs. Spark: Security

In terms of security, Spark is less advanced than MapReduce. In fact, security in Spark is set to "off" by default, which can leave you vulnerable to attack. Authentication in Spark is supported for RPC channels via a shared secret. Spark includes event logging as a feature, and you can secure Web UIs via javax servlet filters. In addition, because Spark can run on YARN and use HDFS, it can also enjoy Kerberos authentication, HDFS file permissions, and encryption between nodes.

Hadoop MapReduce can utilize all the Hadoop security benefits and integrate with Hadoop security projects, like Knox Gateway. Apache Sentry, another popular Hadroop security project, was recently retired. Project Rhino, another retired tool, improved Hadoop’s security but only supported Spark in regard to Sentry. With few options available today, Spark developers will have to improve security themselves.

Bottom line: Spark security is still less developed than MapReduce, which has more security features and projects.

Common Use Cases for Spark

While both MapReduce and Spark are robust options for large-scale data processing, certain situations make one more ideal than the other. 

Streaming Data

As companies move toward digital transformation, they require ways to analyze data in real-time. Spark’s in-memory data processing makes it an ideal candidate for processing streaming data. Spark Streaming is a variant of Spark that makes this use case possible. So, what are some ways companies can take advantage of Spark Streaming?

Streaming ETL – In a traditional ETL process, data is read, converted to a compatible format, and saved to the target data store. The process is much more efficient with Streaming ETL in that the data is continually cleaned and aggregated in memory before being saved to the target data stores. That can reduce the costs associated with ETLing data on small servers and improve compute costs. Streaming ETL can also make data available for analysis in a quicker timeframe. 

Data Enrichment – Companies are in a constant state of change as they try to adapt and provide more enhanced customer experiences. By combining real-time data with static data, companies can build a more robust picture of customers to give them a personalized experience. That can improve brand awareness, conversions, and sales. With a 360-degree view of customers, companies can also fine-tune marketing campaigns and promote relevant services and products. 

Trigger Event Detection – The ability to respond to events in real-time is a vital business capability that facilitates agility and adaptability to change. With Spark Streaming, companies can analyze data in real-time to identify unusual activity that requires immediate attention. For example, businesses can identify trends and patterns in data that might jeopardize their operations and take quick action. 

Machine Learning

When it comes to predictive analysis, Spark’s Machine Learning Library (MLlib) provides a robust set of tools that let you get the job done. When users run repeated queries on a set of data, they are essentially building algorithms similar to machine learning. As an example, machine learning can help companies perform customer segmentation for marketing purposes, making it easier to personalize marketing based on demographic information. Machine learning can also help with #sentiment analysis and reveal what customers really think about a brand.

Interactive Queries

Imagine being able to perform interactive queries on live data. Essentially, you’d be able to analyze large datasets without relying on an external data store to process the information. With Spark Streaming, you can query streams of data without needing to persist it to an external database. That can free up time and resources. 

Common Use Cases for MapReduce

When processing data that is too large for in-memory operations, MapReduce is the way to go. As such, MapReduce is best for processing large sets of data.

Processing Large Datasets (Pentabyte or Terabyte)

Given the time and expense required to implement and maintain it, gigabyte sizes aren’t large enough to justify MapReduce. Organizations looking to manage Pentabyte or Terabyte data are ideal candidates for MapReduce. 

Storing Data in Different Formats

Companies can use MapReduce to process multiple file types, such as text, images, and plain text. As these files are too large for in-memory processing, using MapReduce to batch process is more economical.

Data Processing

MapReduce has robust capabilities for performing basic and complex analyses on large data sets. Tasks such as summarization, filtering, and joining large data sets are much more efficient by using disk-based storage rather than in-memory processing.

MapReduce vs. Spark Trends

As companies look for new ways to remain competitive in a crowded market, they need to adapt to upcoming trends in data management. These trends include:

XOps – Using the best practices from DevOps, XOps's goal is to achieve reliability, reusability, and repeatability in the data management process.

Data Fabric – As an architecture framework, a Data Fabric's goal is to combine multiple types of data storage, analytics, processing, and security in a seamless data management platform  

Data Analytics as a Core Business Function – Traditionally, a separate team that analyzes data and makes it available to key business leaders has handled data management. However, a new approach puts data directly in the hands of the organization’s leaders so they have immediate access to the information for decision-making.

Should You Choose MapReduce or Spark?

Choosing between MapReduce vs. Spark depends on your business use case. Spark has excellent performance and is highly cost-effective thanks to its in-memory data processing. It’s compatible with all of Hadoop’s data sources and file formats and also has a faster learning curve, with friendly APIs available for multiple programming languages. Spark even includes graph processing and machine learning capabilities.

Hadoop MapReduce is a more mature platform and was purpose-built for batch processing. MapReduce can be more cost-effective than Spark for extremely large data that doesn’t fit in memory, and it might be easier to find employees with experience in this technology. Furthermore, the MapReduce ecosystem is currently bigger thanks to many supporting projects, tools, and cloud services.

One more thing to note: If you choose Spark, chances are you won’t use it on its own. You still need HDFS to store the data, and you may want to use Apache Hive, HBase, Pig, Impala, or other Hadoop projects. (Learn more about Apache Hive and HBase here!) This means you’ll still need to run Hadoop and MapReduce alongside Spark for the full big data package!

What Do Experts Think About Spark vs. MapReduce?

Many experts have compared MapReduce with Spark. Here are some insights from reputable MapReduce vs. Spark online reviews: 


IBM says the primary difference between Spark and MapReduce is that the latter "processes and retains data in memory for subsequent steps." MapReduce, however, "processes data on disk." When comparing performance, IBM says Spark is faster because it utilizes RAM "instead of reading and writing intermediate data to disks," while Hadoop "stores data on multiple sources and processes it in batches via MapReduce."

With scalability, IBM notes that Hadoop "quickly scales" to meet demand via HDFS when data volume grows, while Spark "relies on the fault-tolerant HDFS for large volumes of data."


TutorialsPoint calls MapReduce a "data processing engine" and Spark a "framework that powers whole analytical solutions or applications." The website says the latter is a logical choice for data scientists. Moreover, MapReduce has a "greater latency in computing as a consequence of its lower performance in comparison to Spark," while developers can "take advantage" of Spark's superior speed and "low-latency processing capabilities." 

When it comes to Spark vs. MapReduce data processing, TutorialsPoint notes that MapReduce "was developed primarily for batch processing" and is "not effective when applied to use cases that require real-time analytics." Spark, on the other hand, supports the "effective management and processing of data coming from real-time live feeds such as Facebook, Twitter, and other similar platforms."


KnowledgeHut mentions how MapReduce involves "at least four disk operations," while Spark only involves two. The website says this is one reason "Spark is much faster than MapReduce." When talking about costs, KnowledgeHut says hardware is less expensive in MapReduce because "it works with smaller memory compared to Spark" and "even commodity hardware is sufficient." 

MapReduce vs. Spark: How Integrate.io Can Help

While Hadoop MapReduce and Apache Spark are both powerful technologies, there are major differences between them. Spark is faster, utilizes RAM not tied to Hadoop's two-stage paradigm, and works well for small data sets that fit into a server's RAM. MapReduce, on the other hand, is more cost-effective for processing large data sets and has more security features and projects. 

Apache Spark is the newer toy on the big data playground, but there are still use cases for using Hadoop MapReduce. Whether you choose Apache Spark or Hadoop MapReduce, Integrate.io can help transform your data. This no-code data pipeline platform is built on top of Hadoop and doesn't require any deployment or programming. Hadoop-based services like Integrate.io can also alleviate hardware and staffing requirements.

Integrate.io’s philosophy is to streamline data integration and connect disparate data, no matter what technologies you use. Schedule a demo now!