What is Spark?

As a data engineer or scientist, you will need the latest technologies to execute tasks and solve problems in the data pipeline. Apache Spark is one of these technologies and provides data professionals with a unified analytics engine for data processing at scale. Data engineers use Spark to query and analyze data and turn it into the correct format for analysis. However, data scientists can use it later on in the pipeline.

Have you ever wondered, “What is Spark?” This glossary entry provides more information about this platform and its many benefits. 

Apache Spark Meaning

Apache Spark describes itself as:

“A multi-language engine for executing data engineering, data science and machine learning on single-node machines or clusters.”

Apache says 80% of Fortune 500 companies use this platform for big data workloads. 

The primary goal of Apache Spark is to execute data processing jobs on large data sets quickly. The platform distributes these tasks across clusters of computers itself or with the help of other distributed computing tools. 

As more companies invest in big data and machine learning, the ability to quickly perform data tasks has become more critical and a tool like Spark can streamline this process. Manual data processing requires building complex big-data pipelines and lots of code. So Spark makes life easier with its API that handles distributed computing jobs. 

Spark started as a project at the University of California, Berkeley, to make Hadoop MapReduce easier to use. The college donated Spark to the Apache Software Foundation in 2013. 

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What Is Spark? Features

Here are some of the most popular features of Apache Spark:

Batch/streaming data

Spark can unify data processing in batches and real-time streaming via multiple languages such as SQL, Python and Java. 

Spark SQL

What is Spark SQL? It’s an interface that allows developers to create applications. Spark SQL handles large amounts of structured data, helping developers process that data quickly. 

SQL analytics

Spark can facilitate distributed ANSI SQL queries for reporting and dashboarding, providing users with real-time intelligence about data processing workflows. 

Machine learning

Spark users can train machine learning algorithms and use their preferred language to scale fault-tolerant clusters. 

Benefits of Apache Spark

Here are some of the benefits of Apache Spark:

High speeds

Spark is well-regarded among data professionals because of its fast speeds. You can carry out large-scale data processing and manage clustered data exceptionally quickly. 

More successful machine learning

Spark offers various libraries to achieve machine learning objectives. Data professionals can use these libraries to select, extract and transform data sets. 


Spark isn’t just a valuable platform for data engineers and scientists but developers. It comes with developer-friendly tools that allow organizations to create various applications. 

Other benefits of Spark include:

    • Adaptive query execution
    • Ease-of-use
    • Easy access to big data
    • Support for ANSI SQL
    • Support for both structured and unstructured data
    • Support for lazy evaluation

Spark also has an active community of users that provide resources and improve the platform. 

Apache Spark Use Cases

Here are some use cases for Apache Spark

  • An organization requires the execution of batch and real-time processing. Apache Spark supports both data integration methods, allowing the organization to use one tool to process data sets. 
  • An organization wants to perform interactive analysis, which makes business intelligence more effective. Spark is quick enough to perform queries without sampling, making it an excellent choice for interactive analysis. 

What Is Spark? Final Word

Apache Spark is one of the most popular tools used by successful data engineers and scientists. Understanding this platform can help you query data across multiple clusters of computers and handle distributed computing jobs.

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