What Is Apache Spark? How It Works and Why It’s Used

Key takeaways

Apache Spark is a popular tool with a range of components for key big data tasks.

• Apache reports that 80 percent of Fortune 500 companies utilize Apache Spark [1].

• By integrating with various frameworks, Apache Spark can expand its capabilities in areas like data science, machine learning, data storage, and business intelligence.

• You can gain value by adding Apache Spark to your skill set as a data scientist or data engineer.

Explore how Apache Spark can transform the way you work with big data. If you’re ready to start building in-demand data skills, enroll in the IBM Data Science Professional Certificate, where you will have the opportunity to learn the tools and technologies data scientists use, like generative AI, Jupyter, and SQL, in as little as four months.

What is Apache Spark?

Apache Spark is an open source framework supporting a range of big data tasks such as machine learning, exploratory data analysis, and data processing in various programming languages. Initially developed in 2009 as part of a research project at UC Berkeley, Apache Spark has grown to become a long-lasting staple in the big data community, with 80 percent of Fortune 500 companies implementing the platform [1]. Apache Spark builds on the capabilities of its predecessor, Apache Hadoop, by replacing Hadoop’s original MapReduce programming model with more advanced machine learning features. Although Apache Spark can be used by itself, some companies choose to use it simultaneously with Apache Hadoop, depending on the project.

Understanding Spark's core principles and architecture

Apache Spark works by providing application programming interfaces (APIs) for developers, whether programming in Python, Java, SQL, R, or Scala, so that you can perform your data processing tasks in a single location, while also enabling integration with different libraries for additional data manipulation features. Ultimately, this creates an environment that promotes faster processing and application development, scalability, memory access, and simplified data management.

Core components of Apache Spark

Apache Spark's core components include Spark Core, MLlib, Spark SQL, GraphX, and Spark Streaming. Explore how you can use each:

• Spark Core: Apache Spark's primary component is Spark Core, which powers distributed computing, storage access, memory management, scheduling, and fault recovery.

• MLlib: Apache Spark's machine learning algorithm library, MLlib, supports scalable machine learning with model training in Python and R for widely used algorithms like clustering, regression, and classification.

• Spark SQL: When working with structured data, Spark SQL enables efficient querying using SQL, as well as APIs for data manipulation in Python, Java, Scala, and R. 

• GraphX: Built to help you develop graph solutions, GraphX integrates with graphing databases and makes it easier for you to transform graph data, combining extract, transform, and load (ETL) and graph computation capabilities into Spark.

• Spark Streaming: Giving Spark the ability to process data in real-time, Spark Streaming makes it possible for you to analyze data through live data sources and develop solutions in Python, Scala, or Java.

How Spark processes data in memory

One of Spark's features is its in-memory processing, which gives Spark the ability to upload data into memory at exceptionally high speeds. This in-memory feature plays a large role in Spark's advancement beyond the capabilities of Hadoop. In-memory processing is beneficial because it enables faster performance for tasks like querying, as well as algorithmic functions, in comparison to the alternative, disk-based processing.

Read more: Hadoop vs. Spark: What’s the Difference?

The role of Spark in distributed computing

Apache Spark is a distributed computing system, enabling it to share tasks among multiple components, whether that's memory management, querying, or processing. Distributed computing is advantageous because dividing tasks among the different components of Spark reduces latency to help create a more resilient, efficient, and scalable system for users.

Is Apache Spark an ETL?

Yes, Apache Spark is a tool that you can use for ETL purposes, helping you consolidate large-scale data in one location and efficiently extract and transform large data sets with in-memory processing.

Apache Spark software and the data ecosystem

Apache Spark software integrates with an ecosystem of frameworks for use cases in data science and machine learning, SQL and business intelligence, and storage infrastructure. The many tools and frameworks you can use alongside Spark include:

Data science and machine learning

• PyTorch

• NumPy

• TensorFlow

• Pandas

• Scikit-learn

SQL and business intelligence

• Tableau

• Microsoft PowerBI

• Apache Superset

• Looker

Storage and infrastructure

• MongoDB

• Kubernetes

• Apache Kafka

• Apache ORC

Who benefits from using Apache Spark and why it matters

Apache Spark is a valuable addition to the skill set of data scientists and data engineers, making it easier for them to access and process large volumes of data. Discover how you can use Spark in these roles:

• Data scientists: As a data scientist, implementing Apache Spark enables you to speed up the many processes involved with preparing data and extracting insights from it, all in popular data science programming languages like R and Python, as well as in integrated development environments (IDEs) such as Jupyter.

• Data engineers: In data engineering, Apache Spark assists with developing data pipelines for ETL processes to help ensure data scientists have what they need to create applications for big data analytics. 

What is Apache Spark vs. Kafka?

Kafka is another tool offered by Apache, and while both Spark and Kafka process data, they do so differently. Spark performs distributed processing, which divides tasks across processors, while Kafka actively performs real-time processing, ideal for event streaming.  

Why Apache Spark remains relevant

Because Spark features an open-source community, it’s constantly undergoing improvements through additional features as users make contributions. Additionally, Apache periodically releases updated versions of Spark to incorporate new features and correct any flaws in previous versions. For example, the latest version as of January 2026, Apache Spark 4.1.1, includes security improvements, and Apache Spark 4.2.0 is available for a preview release, allowing community members to help test the future version. 

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