💥Top 5 Reasons to Use Apache Spark for Big Data Processing
Apache Spark is a popular open source Big Data framework for processing large amounts of data in a distributed environment. It is part of the Apache Hadoop project ecosystem. This framework is good because it has the following elements in its arsenal:
Wide API - Spark provides the developer with a fairly extensive API, which allows you to work with different programming languages, for example: Python, R, Scala and Java. Spark also offers the user a dataframe abstraction (dataframe), which uses object-oriented methods for transforming, combining data, filtering it, and many other useful features.
Pretty broad functionality - Spark has a wide range of functionality due to components such as:
1. Spark SQL - a module that serves for analytical data processing using SQL queries
2. Spark Streaming - a module that provides an add-on for processing streaming data online
3. MLLib - a module that provides a set of machine learning libraries in a distributed environment
Lazy evaluations - allow reducing the total amount of calculations and improving the performance of the program by reducing memory requirements. This type of calculation is very useful, as it allows you to determine the complex structure of transformations represented as objects. It is also possible to check the structure of the result without performing any intermediate steps. Spark also automatically checks the query execution plan or program for errors. This allows you to quickly catch bugs and debug them.
Open Source - Part of the Apache Software Foundation's line of projects, Spark continues to be actively developed through the developer community. In addition, despite the fact that Spark is a free tool, it has very detailed documentation: https://spark.apache.org/documentation.html
Distributed data processing - Apache Spark provides distributed data processing, including the concept of distributed datasets RDD (resilient distributed dataset) is a distributed data structure that resides in RAM. Each such dataset contains a fragment of data distributed over the nodes of the cluster. This makes it fault-tolerant: if a partition is lost due to a node failure, it can be restored from its original sources. Thus, Spark itself spreads the code across all nodes of the cluster, breaks it into subtasks, creates an execution plan and monitors the success of the execution.