Large and complex datasets that traditional data processing software cannot manage effectively.

An open-source distributed computing system for big data processing, known for speed and ease of use.

A series of data processing steps that involve data ingestion, transformation, and storage.

The process of collecting and importing data for immediate use or storage.

A distributed collection of data organized into named columns, similar to a table in a database.

A fundamental data structure of Apache Spark, representing a distributed collection of objects.

A Spark module for structured data processing, allowing queries using SQL syntax.

Optimizing Spark applications to improve their execution speed and resource utilization.

A distributed event streaming platform used for building real-time data pipelines and streaming applications.

The execution of a series of jobs on a dataset collected over a period, processed as a single unit.

The immediate processing of data as it becomes available, allowing for instant insights.

The process of converting data from one format or structure into another for analysis.

A machine learning library for Apache Spark, providing algorithms for classification, regression, and clustering.

The ability of a system to handle growing amounts of work or its potential to accommodate growth.

The condition of a dataset, determined by accuracy, completeness, reliability, and relevance.

The science of analyzing raw data to make conclusions about that information.

A field of computer science that deals with designing algorithms and systems that run on multiple computers.

Protecting data from unauthorized access and data breaches, ensuring confidentiality and integrity.

The graphical representation of information and data to communicate insights effectively.

A set of machines that run Spark applications, providing distributed processing capabilities.

A data processing framework that involves extracting data from sources, transforming it, and loading it into a target system.

An open-source framework for distributed storage and processing of large datasets using a cluster of computers.

Delivery of computing services over the internet, enabling flexible resources and scalability.

The management of data availability, usability, integrity, and security in an organization.