Apache Spark is a popular open-source large data processing platform among data engineers due to its speed, scalability, and ease of use. Spark is intended to operate with enormous datasets in a distributed computing environment, allowing developers to create high-performance data pipelines capable of processing massive volumes of data quickly.
Data engineers often work in multiple, complicated environments and perform the complex, difficult, and, at times, tedious work necessary to make data systems operational. Their job is to get the data into a form where others in the data pipeline, like data scientists, can extract value from the data.
Spark has become the ultimate toolkit for data engineers because it simplifies the work environment by providing both a platform to organize and execute complex data pipelines, and a set of powerful tools for storing, retrieving, and transforming data.
Connect to different data sources in different locations, including cloud sources such as Amazon S3, databases, Hadoop file systems, data streams, web services, and flat files.
Convert different data types into a standard format. The Spark data processing API allows the use of multiple different types of input data. Spark then utilizes Resilient Distributed Datasets (RDDs) and Data Frames for simplified, yet advanced data processing.
Write programs that access, transform, and store the data. Many common programming languages have APIs to integrate Spark code directly, and Spark offers many powerful functions for performing complex ETL-style data cleaning and transformation functions. Spark also includes a high-level API that allows users to seamlessly write queries in SQL.
Integrate with almost every important tool for data wrangling, data profiling, data discovery, and data graphing.
Batch Processing
Spark is frequently used for batch processing of huge datasets. Spark reads data from multiple data sources, performs data transformations, and writes the results to a target data storage in this use case. The batch processing features of Spark make it perfect for jobs like ETL (Extract, Transform, Load), data warehousing, and data analytics.
Real-time Data Streaming
Spark may also be used to stream data in real time. Spark collects data from a real-time data source, such as sensors or social media, and performs real-time processing on the data stream in this use case.
Speed
Spark can analyze big datasets at breakneck speeds by leveraging in-memory computation and data partitioning techniques.
Scalability
Because Spark can grow horizontally over a cluster of nodes, it can handle enormous datasets without sacrificing performance.
Ease of Use
Spark offers an intuitive and user-friendly interface for building data pipelines, allowing developers to easily create complex data processing workflows.
Flexibility
Spark offers a wide range of data sources and data processing activities, allowing developers to create unique data pipelines that meet their individual requirements.
