Apache Spark: Framework for Infinite Data Streams

Apache Spark is the most popular tool among data engineers. Its main task is data processing. Using Spark, users can connect to any data source, read big data sets, and process them in RAM using distributed computing.

It is the open-source Big Data framework for processing large amounts of data. Spark is a part of the Apache Hadoop ecosystem of projects but compares favorably with its other components.

Main Components of Apache Spark

The components included in the framework make up its unified stack. With their help, Apache Spark has become a powerful tool of processing big data and can support many use cases.

1. Spark Core. It implements the basic functionality of Spark for task scheduling, memory management, error handling, and interaction with storage systems.

2. Spark SQL. It is a structured data processing and analysis package that allows users to extract data using statements in the SQL queries language.

3. Spark Streaming. This tool easily integrates with popular data sources and enables online streaming processing.

4. MLlib machine learning library assists in the launching of machine learning mechanisms. It supports various machine learning algorithms, including classification, regression, clustering, collaborative filtration, and others.

5. GraphX is the library for processing graphs and performing parallel calculations.

6. Cluster managers. Spark's internal implementation provides efficient scaling from one to several thousand nodes. This flexibility is achieved through Hadoop YARN, Apache Mesos, Standalone, Kubernetes Schedule cluster managers.

Combining all components within a single framework makes Spark useful for each big data specialist.

Key Benefits of Spark

Fast processing speed

The first and foremost advantage of using Apache Spark to handle big data is that it provides 100 times the speed in memory and ten times the speed on disk in Hadoop clusters. Setting a world record for sorting data on a disk, Apache Spark demonstrates lightning speed when a vast amount of data is stored on the disk.

Appealing APIs and lazy execution

Spark as a development tool is much more convenient than MapReduce and more convenient than tools like Apache Crunch and other tools from the "second" generation. And also, it is more flexible than Hive and not limited by SQL.

Open-source community

The framework has a huge open-source community that is massive, qualified, and friendly. It improves the core software and contributes practical add-on packages. A considerable number of extensions are being written for Spark.


Spark Streaming is another attractive part of Spark's capabilities. It is the processing of streaming data from, for example, Kafka, ZeroMQ, and others by the same means as the data taken from the database. The funds are precisely the same. Users will practically not have to change anything in the program to start processing data from Kafka.

Apache Spark is a part of the Databricks platform. Users can get corporate support on it. Also, Databricks has effective solutions, such as Delta Lake, which ensures the reliability of data lakes.

Apache Spark Languages

One more reason for Spark's popularity is its support for several development languages: Scala, Java, Python, and R.

After launching Apache Spark, developers are faced with the question of which language to choose for work. If a developer needs the ability to select his language or support several languages, this will result in a proliferation of code and tools. The R interface is not rich enough for this. Java has too redundant code and a difficult interface. So, the choice falls on Python or Scala. Scala has the main advantage: it is in this language that the Apache Spark platform runs. Scala, running on JVM, is as powerful as Java. But the code on Scala looks neater.

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