The widespread adoption of big data across various business sectors has led to an increased demand for specialized systems to process it. In this article, we will discuss one of the most popular among them – Apache Spark. You will learn about its working principle, features and functionalities, advantages and disadvantages, areas of application, and whether Apache Spark is free or not.

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What is Apache Spark

Apache Spark is a versatile open-source engine for big data processing. It is used for handling streaming data, business analytics, and building artificial intelligence (AI) and machine learning (ML) models.

Spark is optimized for high-load operations. It can process hundreds of millions of records with remarkable speed and stability. The framework also demonstrates excellent performance with smaller data volumes. By processing data in memory, this platform often operates 10–100 times faster than traditional systems like Hadoop MapReduce. The engine distributes tasks across large computer clusters, supporting parallelism and high fault tolerance. This ensures efficient scalability of computational processes.

Apache Spark


Spark Apache was developed at the University of California, Berkeley, in 2009 as part of the AMPLab project, which specializes in big data processing systems (Apache Mesos, Alluxio). The project was led by Matei Zaharia, a renowned computer science expert. The goal of the project was to create a scalable and fault-tolerant platform for high-performance computing, optimized for data processing and AI/ML models. In 2014, Spark officially became an Apache project. To commercialize the technology, Matei Zaharia and his colleagues founded Databricks in 2013, a company focused on developing a cloud platform based on Spark.    

Today, Apache Spark is one of the most in-demand open-source engines for scalable computing. It is used by thousands of companies, including many on the Fortune 500 list. The project is maintained by the Apache Software Foundation and supported by an active community of over 2,000 contributors from various business and scientific fields. The Spark ecosystem integrates seamlessly with popular tools and frameworks for data science, machine learning, and data analysis, such as TensorFlow, PyTorch, Pandas, and SQL tools.

Key Features and Benefits

Apache Spark has become one of the most sought-after tools for big data processing due to its numerous useful features and advantages. Among these, the following stand out:

  • High speed. Spark optimizes data processing by caching it in memory while performing parallel operations. This significantly accelerates the handling of large datasets, reducing the need for disk reads and writes. For memory-intensive tasks, Spark delivers performance that is 10–100 times faster than Apache Hadoop. This speed boost is especially noticeable in complex computations and queries.
  • Stream processing. Spark Streaming enables the processing of data streams in micro-batches, bringing the system closer to real-time processing. For more complex scenarios, the Structured Streaming API is available, offering high performance and reliability when working with data streams.
  • Multitasking. The framework supports executing multiple tasks simultaneously, including interactive queries, stream analytics, graph processing, and machine learning model training through the MLlib library.
  • Accessibility. Spark is free, open-source software that can be integrated with existing applications or used via popular cloud platforms such as AWS, Azure, Google Cloud, and Databricks. While Spark itself is free, cloud-based solutions may involve additional costs.
  • Enhanced usability. The big data processing platform supports popular programming languages such as Java, Scala, Python, R, and SQL. This makes Spark a versatile tool suitable for both developers and data analysts. Its support for these languages allows users to create, scale, and maintain a wide range of applications, from analytics platforms to streaming systems.    
  • Versatile functionality. Apache Spark includes tools for SQL programming (Spark SQL), machine learning (MLlib), stream processing (Spark Streaming), and graph processing (GraphX). These components simplify the development of applications requiring complex data analysis and make the platform appealing to a wide range of users.
  • Accelerated implementation. APIs (DataFrame API and Dataset API) simplify data manipulation and reduce development complexity. This significantly speeds up the creation of distributed applications and requires minimal code.
  • Fast memory access. Spark provides centralized access to cached in-memory data through interfaces supporting Python, R, and Spark SQL. This makes it convenient for analytics requiring rapid processing of large data volumes.

By exploring the key features and benefits of Apache Spark, users can also gain insights into what Apache Spark does. The capabilities it offers make it a crucial tool for developing scalable and efficient solutions in data processing and analysis.    

Architecture and Key Components

Apache Spark is built on the Hadoop MapReduce model but supports a broader range of computations, including interactive queries and stream processing. The platform provides native support for Java, Scala, Python, and R, alongside a suite of libraries for building machine learning applications (MLlib), stream processing (Spark Streaming), and graph processing (GraphX).

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The system has a hierarchical structure with primary and secondary components. The main node, Spark Driver, manages the cluster manager, which coordinates secondary nodes and delivers processed data to the client application. Based on the application code, it generates a SparkContext, which works with the Standalone Cluster Manager or other cluster managers (Hadoop YARN, Kubernetes, Mesos). Secondary nodes monitor and distribute workload across the cluster while creating resilient distributed datasets (RDDs).

The core component of the system is Spark Core, which enables distributed task execution, scheduling, and input/output operations. Spark Core utilizes the concept of resilient distributed datasets (RDDs) to aggregate data and its partitions across a server cluster. Once aggregated, the data can be processed through analytical models or transferred to another data storage. 

In addition to Spark Core, the Apache Spark architecture includes the following components:

  • MLlib. This is a library of algorithms for scalable machine learning projects. It supports training models on data from Hadoop, HDFS, Apache Cassandra, HBase, and local files using R and Python. Trained models can be saved and integrated into pipelines built with Java or Scala. MLlib addresses key machine learning tasks, including classification, regression, clustering, collaborative filtering, and pattern analysis.    
  • Spark Streaming. This module operates in a micro-batch processing mode, leveraging the engine’s core resources for streaming analytics. It processes data in mini-batches and analyzes it using batch analytics tools. This allows developers to use a unified codebase for both batch processing and real-time data streaming applications, greatly simplifying development. Spark Streaming ingests data from sources like Kafka, Flume, HDFS, and ZeroMQ, and integrates with Spark Packages ecosystem resources.    
  • Spark SQL. This module performs distributed interactive queries with low latency, up to 100 times faster than MapReduce. Its tools include a columnar storage system, a cost-based optimizer, and a code generator scalable to thousands of nodes. It supports standard SQL, Hive Query Language for querying data, APIs, and various out-of-the-box data sources such as JDBC, ODBC, JSON, HDFS, Hive, ORC, and Parquet.    
  • Spark GraphX. This is a platform for distributed graph processing. It supports exploratory analysis, iterative computations, and integration into ETL pipelines. Users can interactively create and transform graph data structures at any scale. GraphX features a flexible API and a set of distributed algorithms, including PageRank, Connected Components, and Triangle Counting.    

What Is Apache Spark Used For

Apache Spark is widely used by thousands of companies around the world. The platform has gained the most popularity in the following industries:

  • Finance. Storing and processing big data helps banks and other financial organizations track customer actions and recommend their products. Using Spark, companies analyze data from accounts and transactions, social media, support tickets, and other sources. The insights gained enable them to make informed decisions in assessing credit risks and detecting signs of illegal activity.    
  • Retail and e-commerce. The engine's capabilities allow businesses to collect and process data on customer behavior and interactions with brands and products. Spark tools analyze audience activities (views, orders, transactions, comments, support requests) in real time. This enables companies to more effectively attract and retain customers through personalized recommendations.    
  • Healthcare and science. Medicine is one of the most relevant and progressive Apache Spark use cases. Many companies and organizations in this field use the framework for automated analysis of patient clinical records. Based on the data obtained, specialists predict disease progression and prescribe the most effective treatment. In genomic sequencing, the system significantly accelerates genome data processing.
  • Media and entertainment. Popular social networks, media resources, and streaming platforms leverage Spark's capabilities for more precise personalization of news feeds, recommendations, and targeted advertising. Their machine learning algorithms are trained on hundreds of millions of datasets using Spark's resources.    
  • Gaming. Spark tools are employed in the gaming industry to identify in-game patterns in real time. This allows developers and publishers to collect data about their audience and use it to commercialize products, as well as attract and retain players.

How to Use Apache Spark

Working with Apache Spark can be challenging for beginners, but thanks to detailed documentation and a large community, mastering this tool becomes quite manageable with practice and experience. To get started with Spark, you need to follow these steps:

1. Install Apache Spark. To begin working with Apache Spark, you need to install it on your computer or cluster. Spark can be downloaded from its official website and supports Linux, macOS, and Windows operating systems. For easier installation, you can use cloud platforms like Databricks and Amazon EMR, where Spark is pre-installed.    

2. Start a Spark session. After installation, you need to create and start a Spark session. In Python, this can be done using the PySpark library. For example:    

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("example").getOrCreate()

This creates a Spark object that allows you to work with data and perform computations.

3. Load data. Apache Spark supports various data formats, including CSV, JSON, Parquet, HDFS, and many others. To load data into Spark, you can use built-in functions. For example:    

df = spark.read.csv("path/to/your/data.csv", header=True, inferSchema=True)

4. Process data. Once the data is loaded, you can use Spark to process it, such as filtering, grouping, or performing aggregations with built-in functions:  

df_filtered = df.filter(df['column_name'] > 100)
df_grouped = df.groupBy('column_name').count()

5. Use machine learning. The MLlib library provides tools for building models for classification, regression, and clustering. It also includes resources for algorithms aimed at dimensionality reduction, recommendations, and other ML tasks. For example:    

from pyspark.ml.classification import RandomForestClassifier
rf = RandomForestClassifier(labelCol="label", featuresCol="features")
model = rf.fit(trainingData)

6. Run on a cluster. For handling large datasets, you can use Spark on a distributed cluster. This involves setting up clusters with Spark and running processing tasks across multiple machines to improve performance.    

7. Use SQL queries. Spark supports executing SQL queries through its built-in Spark SQL support. For example:    

df.createOrReplaceTempView("table_name")
result = spark.sql("SELECT * FROM table_name WHERE column_name > 100")

By following these basic steps, you can efficiently process and analyze data using Apache Spark.

Challenges and Limitations

Apache Spark is a powerful tool for working with big data, but it has certain limitations that can complicate its use in specific scenarios. Understanding these challenges can help you make an informed decision about adopting and configuring the platform.

Key limitations of the system:

  • Resource intensity. Spark is a resource-intensive engine. It consumes a significant amount of memory for computations to deliver high-speed data processing. This increased memory consumption leads to higher operational costs, which can be a substantial issue for many users. The need to invest in powerful and often expensive hardware can create barriers to implementing and scaling this system.    
  • Complex architecture. Despite the apparent simplicity of its components, Apache Spark's functionality is quite challenging to master. Even basic tools of the engine – such as distributed storage, in-memory processing, and column formatting – can pose difficulties for beginners.    
  • Delays in data processing. The system is not ideal for real-time data processing. Spark processes data in micro-batches, with a maximum latency of around 100 ms. If real-time processing is a critical requirement, it may be better to consider an alternative, such as Apache Flink.    
  • Challenges with small files. Many Spark users encounter difficulties when working with a large number of small files. The increased number of tasks and the large volume of metadata to analyze often slow down the engine's performance.    

Conclusion

Apache Spark is rightfully considered one of the most in-demand solutions in the software market for big data processing and analysis. This open-source engine offers numerous advantages, including high processing speed, stream processing via micro-batches, accessibility, versatile functionality, multitasking, and fast memory access.

On the other hand, Spark has a relatively complex architecture and high resource requirements, which can pose challenges for beginners and companies with limited capabilities. Nevertheless, this versatile system is actively used by organizations across various industries, including finance, retail, healthcare, and gaming.

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