Data Architecture

Data Lake Zones is a framework that organizes data in different zones within a data lake to optimize data processing and analytics.

Data Lake Architecture is a modern data storage and processing framework that enables businesses to store and analyze large volumes of diverse data in a flexible and scalable manner.

Data Mesh is a decentralized approach to data architecture that aims to improve data processing and analytics in organizations.

Infrastructure Abstraction is the process of separating the underlying infrastructure from the application layer, allowing businesses to focus on data processing and analytics.

Namespaces is a data management feature that organizes and separates data within a distributed file system.

Multi-tenant application is a software architecture that allows multiple users or organizations to share a single instance of an application.

Hierarchical namespace is a data management system that organizes data in a hierarchical structure, allowing for efficient data processing and analytics.

Entity-Relationship Model is a conceptual modeling technique used to represent the structure and relationships between entities in a database.

Centralized Data Architecture is a data management approach that centralizes data into a single repository, improving data processing, analytics, and decision-making.

Explore data modeling, its importance, and how it helps organizations manage data effectively, optimize performance, and drive decision-making.

Some commonly used open-source tools for building a data mesh architecture include Apache Kafka, and Apache Spark, among others.

A data fabric is a unified and integrated data management framework that enables organizations to manage data seamlessly across various data sources, locations, and formats.

Data mesh is a relatively new concept in the field of data architecture that emphasizes the importance of decentralizing data ownership and management.

This page provides an overview of the benefits of a semantic layer, common use cases, and best practices for building and maintaining a semantic layer.