What is Big Data?
Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software can't manage them. The concept is characterized by three key attributes: unusually high data volumes, high velocity (the rate at which data changes, is collected, and grows), and significant variety in dimensionality and format.
Why it matters
Big data analytics empowers organizations to extract meaningful insights from intricate and heterogeneous data sources. This capability has been enabled by progress in parallel processing technologies and the availability of affordable computational resources.
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Related concepts
Data analytics is the systemic computational analysis of data. It is used for the discovery, interpretation and communication of meaningful patterns in data. The discipline also encompasses applying discovered data patterns to inform effective business decision-making.
Advanced analytics leverages sophisticated autonomous and semi-autonomous tools to evaluate large datasets of real-time and historical information. These tools—including artificial intelligence and machine learning algorithms—can process both structured and unstructured data, though text-based unstructured data typically requires preprocessing through text mining before becoming actionable.
Data architecture comprises the components that collectively fulfill an organization's data needs, including acquisition, storage, preparation, and analysis. Modern data architecture has been substantially shaped by concurrent advancements in big data, machine learning/AI, and cloud computing. It is designed proactively with scalability and flexibility in mind, anticipating complex data needs.
A data pipeline constitutes a sequence of data processing steps. If data isn't already loaded into the data platform, ingestion occurs at the pipeline's start. Subsequently, each step produces output serving as input for the next step, continuing until completion. Some independent steps may execute in parallel.