Hadoop vs. Spark: What’s the Difference?

Apache Spark and Apache Hadoop are two different open-source data processing frameworks that data professionals use to analyze immense sets of information. While each one has its own specific strengths and weaknesses, they are similar in that they are both distributed systems that allow you to process data as it scales. They are both created from multiple software modules that coordinate and work together to create a functional system. With both Hadoop and Spark, you have the ability to prepare, process, maintain, manage, and analyze huge amounts of real-time data.

Regarding the differences between these two systems: While Apache Hadoop permits you to join several computers together to analyze vast data sets faster, Apache Spark allows you to make speedy analytic queries within data sets ranging from large to small. Spark accomplishes this by utilizing in-memory caching along with advanced query performance.

Additionally, Spark uses artificial intelligence and machine learning to achieve its goals, which is another major difference between the two systems. That said, many businesses incorporate both Spark and Hadoop simultaneously to reach their objectives. Learn more about the similarities and differences between Hadoop and Spark, when to use Spark versus Hadoop, and how to choose between Apache and Spark.

Hadoop

Apache Hadoop is open-source software that processes and analyzes data sets using a network of computers called nodes. While other systems might use one single computer, Hadoop’s interconnected network consists of multiple computers known as Hadoop clusters. Each computer is responsible for storing and processing a section of a massive data set, and the clusters can quickly analyze enormous data sets simultaneously.

What is Hadoop used for?

Your main use for Hadoop is the advanced analysis of stored data sets. It allows large analysis tasks to be split into smaller tasks, performing them simultaneously for quicker processing. Hadoop uses four main modules to analyze data: the Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop Common. These components work together to successfully store, process, and analyze information.

Hadoop is unique in that its computer clusters allow the system to catch potential failures early, thereby protecting the data itself. The clusters themselves might have two computers or a thousand. Each cluster handles a chunk of data and monitors itself for any issues or vulnerabilities that may occur, and this self-monitoring provides high availability, meaning you can run clusters for long periods of time without having to intervene.

Advantages of Hadoop

Hadoop has several advantages for your business, ranging from costing less than Spark to stronger security. One is its robust security infrastructure to protect data from breaches or loss. Another is that Hadoop is easily scalable. All you need to do is add another computer to the cluster. Hadoop is useful for batch processing and linear data processing, and it will most likely cost you less to run than Spark. Hadoop is also more fault-tolerant because the data itself is replicated across many computers—or nodes—within the cluster, which means if one computer fails, another one can reconstruct the information stored on the failing one.

Disadvantages of Hadoop

While Hadoop can process immense amounts of data, the sheer size of the computer clusters handling the information means that it might be slower to process data than Spark. Hadoop tends to be more complex to design and manage, which might be frustrating if you are a beginner in data analysis. Hadoop is also unable to do real-time processing.

Spark

Apache Spark is an open-source processing system that is used to process and analyze big data workloads. It uses a feature called in-memory caching, which makes it very efficient for analysis. You can use it for data science, machine learning, and data engineering. Spark processes data with a resilient distributed data set (RDD) system. While Hadoop uses a file system, Spark processes its data within its own software, utilizing its random access memory (RAM) to temporarily store and immediately access the information.

Spark’s design works with machine learning algorithms, and it can run in conjunction with Hadoop, using the computer clusters as a data source for its own processes. Spark uses the following components to analyze data: Spark Core, Spark SQL, Spark Streaming and Structured Streaming, Machine Learning Library (MLlib), and GraphX.

What is Spark used for?

If you are a data scientist, you might use Spark to fill in the gaps and address the limitations of Hadoop’s MapReduce feature. Spark processes data in memory, using its RAM, and replicates data across multiple operations, streamlining the entire process into a single step. This can provide you with much faster results than you might receive from Hadoop. Data scientists tend to use Spark when they want real-time processing and when working with any sort of machine learning.

Advantages of Spark

Spark’s advantages include speed and ease of use, so several big internet companies, such as eBay, Netflix, and Yahoo, employ this technology. Its ability to process in-memory means it can analyze your data efficiently and quickly. It’s adaptable to multiple programming languages, so your developers can choose which one to build an interface with. Spark also applies to machine learning processes and software, running multiple applications simultaneously. Finally, if you become proficient in Spark, you can expect to earn a great salary. According to Glassdoor, the average annual salary for a Spark developer is $110,628 [1].

Disadvantages of Spark

Spark’s disadvantages include a tendency to struggle with large data sets since the in-memory processes themselves take a lot of processing power. It can also be expensive for you to build and maintain the infrastructure necessary to support Spark. Spark’s security features aren’t as robust as Hadoop, so you’ll need to ensure you have other security to protect data successfully.

Other things to consider

When choosing between Apache Hadoop and Apache Spark, it’s important to consider your goals for data analysis. Spark is a good choice if you’re working with machine learning algorithms or large-scale data. If you’re working with giant data sets and want to store and process them, Hadoop is a better option.

Hadoop is more cost-effective and easily scalable than Spark. To increase Hadoop's processing capacity, you need only add more computers. However, Spark requires more RAM to increase its in-memory processing capabilities, which can be expensive.

Many data scientists tend to use Hadoop and Spark together while having the systems focus on different tasks. For example, with a massive data set, you might use Hadoop for large batch processing and then use Spark for more specific real-time or graph analytics tasks.

Getting started with Coursera

Sharpen your data science skills and learn more about the features, benefits, and use cases of Apache Hadoop and Apache Spark with courses and Professional Certificates on Coursera. With choices such as IBM’s Machine Learning with Apache Spark or Introduction to Data Analytics, you’ll learn foundational knowledge about data science and how to get the most out of software like Hadoop and Spark. Explore what is available on Coursera today to learn more about the different benefits these data processing systems can offer you and your business.