Hadoop vs. Spark: What’s the Difference? | Coursera

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Hadoop and Spark are both smart options for big-scale data processing. Learn more about the similarities and differences between Hadoop and Spark, when to use Spark versus Hadoop, and how to choose between Apache Hadoop and Apache Spark.
Apache Spark and Apache Hadoop are two open-source data processing frameworks that data professionals use to analyze huge sets of information.
Developing expertise in Spark can boost your earning potential, with average salaries for Spark‑skilled professionals reaching $123,000 a year [1].
Spark can run up to 100 times faster than Hadoop for large‑scale workloads, giving teams a major performance advantage when speed is the priority [2]
You can strategically pair the two systems, using Hadoop for large, long‑running batch jobs and Spark for fast, iterative, or streaming analysis, to leverage the strengths of both.
Learn more about Hadoop versus Spark, alongside the advantages and challenges of both open-source data processing frameworks. If this interests you, consider preparing for a career as a data engineer by earning your Data Engineering Professional Certificate from IBM. You can build your job-ready capabilities and must-have AI skills for an in-demand career without needing any prior experience.
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.
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.
Your primary 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. Hadoop clusters depend on a Master/Worker (Leader/Follower) architecture. A master node oversees the worker nodes. This self-monitoring provides high availability, meaning you can run clusters for long periods of time without having to intervene.
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.
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.
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.
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