What is Modern Data Architecture?
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.
Why it matters
These characteristics enable organizations to handle increasingly sophisticated data environments while maintaining agility and responsiveness to business demands.
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Related concepts
Data fabric is an architectural approach addressing data silos through flexible, resilient integration of data sources across platforms and business users, ensuring data availability wherever needed. While not a single static technology, data fabric conceptually provides consistent visibility and unified controls for managing disparate data and diverse technologies deployed across multiple data centers and edge computing locations—both on-premises and cloud-based.
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.
DataOps encompasses practices, processes, and technologies that merge data management with agile software engineering principles. The approach emphasizes automation and workflow optimization to enhance quality, speed, and collaboration while fostering continuous improvement in analytics.
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.