What is Apache Spark and Why It Is Ultimate for Working with Big Data

The study and big data analysis are complex but at the same time an important area that continues to develop and expand. Every year, a person generates an increasing amount of information, and the vast majority of it is in an unstructured form. Learning how to analyze information of this kind, to find connections between individual data sets, obtaining an organized data set with a clear structure as an output is the essential task of our time.

Working with big data is necessary for almost all areas: science, medicine, business. Cognitive technologies and Big Data processing are beneficial for creating business solutions. As for this area, the ability to quickly process unstructured data is one of the success factors. After all, this means the opportunity to get a wide selection of data about customers, potential buyers, or consumers of the service, to learn before competitors about market trends and the dynamics of its various sectors.

No matter what area you need to analyze big data, you need smart tools to transform data into a clear picture instantly. Today, the Apache Spark brand is increasingly popping up in various fields. However, not so many people know how to install Apache Spark on Windows, let alone perfectly understand this project’s essence, mission, goals, and value. Therefore, this article will answer the questions that still concern every expert working with big data: What is Apache Spark? What are the components of Apache Spark? Why use Apache Spark when working with big data?

History of Creation Apache Spark

The first framework for working with Big Data was Apache Hadoop, implemented based on MapReduce technology. In 2009, UC Berkeley graduate students developed an open-source cluster management system, Mesos. To showcase the power of their product and how easy it is to manage a Mesos-based framework, the same group of graduate students started working on Spark.

As planned by the creators, Spark was supposed to become an alternative to Hadoop and surpass it. The main difference between the two frameworks is the way data is accessed. Hadoop saves data to the hard disk at each step of the MapReduce algorithm, while Spark performs all operations in RAM. Thanks to this, Spark gains up to 100 times in performance and allows you to process data in a stream.

In 2010, the project was published under the BSD license, and in 2013 it was licensed by the Apache Software Foundation, which sponsors and develops promising projects. Mesos also caught the attention of Apache and came under its license but did not become as popular as Spark.

Meaning and Features of Apache Spark

What is Spark meaning in the 21st century? Apache Spark is a platform used in Big Data for cluster computing and large-scale data processing. Spark processes data in RAM and rarely access disk, so the platform runs very fast.

Spark is used for data processing, such as filtering, sorting, cleaning, validating, etc. Apache Spark is fully compatible with the Hadoop ecosystem and easily integrated into existing solutions. It does not have its data store and can work with various sources: HDFS, Hive, S3, HBase, Cassandra, etc. Apache Spark supports several programming languages: Scala, Python, Java, R, SQL.

Main Components of Apache Spark

The framework consists of five components: a core and four libraries, each solving a specific task. Let’s look at them in detail.

Apache Spark Core

Apache Spark Core is the underlying data engine that underpins the entire platform. The kernel interacts with storage systems, manages memory schedules, and distributes the load in the cluster. It is also responsible for supporting the API of programming languages.

Spark SQL

This module simplifies the work with structured data and executes queries in the SQL language. Its main task is to ensure that data engineers do not think about the distributed nature of data storage but focus on scenarios for their use.


Spark Streams provides scalable, high-performance, and fault-tolerant real-time streaming data processing. Kafka, Flume, Kinesis, and other systems can act as data sources for Spark.


MLlib is a scalable, low-level machine learning library. The library implements various machine learning algorithms – for example, clustering, regression, classification, and collaborative filtering.


GraphX is used to manipulate graphs and process them in parallel. GraphX can measure line graph connectivity, degree distribution, average path length, and more. It can also connect graphs and quickly transform them. In addition to built-in operations on graphs, GraphX also has a library implementing the PageRank algorithm.

6 Reasons Why You Should Use Apache Spark When Working With Big Data

One of the most significant advantages of Apache Spark is its speed. As we have already noted, unlike the classic Hadoop MapReduce, the platform allows you to process data directly in RAM. Due to this, many big data processing tasks are completed faster, which is especially important in machine learning. However, this is far from the only plus of the framework in question. From a practical point of view, the following properties are beneficial.

Rich API

Apache Spark provides the developer with a fairly extensive API, allowing them to work with different programming languages: Python, R, Scala, and Java. Spark offers the user an abstraction of the data frame, which uses object-oriented transformations, data aggregation, filtering, and many other valuable features. Also, the object approach allows the developer to create custom and reusable code that can be tested using various specialized methods and tools – for example, sending parameterized requests and creating different environments for the same requests.

Lazy Evaluation

Lazy evaluation reduces the overall amount of computation and improves program performance by reducing memory requirements. Such calculations are valuable since they allow you to determine the complex structure of transformations represented as objects. It is also possible to check the form of the final result without performing any intermediate steps. Spark also automatically checks the query execution plan or program for errors. This allows you to catch bugs and debug them quickly.

Easy Conversions

The Apache Spark community has released the PySpark tool, which offers a Pyspark Shell module that binds the Python API and the Spark context to support the Python language. Therefore, a Big Data application developer can use the toPandas method to convert a Spark DataFrame to Pandas seamlessly. This is necessary to save the processed files in CSV format more efficiently and speed up the processing of small data arrays.

Simple Data Rotations

Data rotation is considered a problem for many big data frameworks such as Apache Kafka or Flink. Typically, such operations require multiple case statements. Spark has a simple and intuitive way to rotate a data frame in its arsenal. The user only needs to perform a groupBy operation on the columns at the target index, rotating the target field for later use as columns. Then you can proceed directly to the aggregation itself.

Apache Spark is an Open Source Framework

As part of the Apache Software Foundation project lineup, Spark continues to be actively developed through the development community. Open-source enthusiasts improve the core software and offer additional packages. For example, in October 2017, developers published a native language processing library for Spark. This eliminated the need for the user to use other libraries or rely on slow user-defined functions for Python packages such as the Natural Language Toolkit.

Spark Libraries Deliver Very Broad Functionality

Today, the standard Spark libraries are a significant part of this open-source project. The core of Spark hasn’t changed much since it was released, but the libraries have grown to add even more functionality. Gradually, Spark has evolved into a multifunctional data analysis tool. Spark has SQL and structured data libraries, machine learning, streaming, and graph analytics. In addition to these libraries, there are hundreds of open third-party libraries, ranging from those that work with connectors to options for various storage systems and machine learning algorithms.


Apache Spark is the most popular and fastest-growing Big Data framework. Good technical parameters and four additional libraries allow you to enable Spark for a wide range of tasks. Therefore, use Apache Spark if you have not yet found an assistant for processing big data, building a plot diagram, or graphical analytics. All the necessary functions are collected in one tool!

Featured image by Lucas/Pexels

Naveen (NNK)

Naveen (NNK) is a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, He has honed his expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. In this blog, he shares his experiences with the data as he come across. Follow Naveen @ LinkedIn and Medium

Leave a Reply