2024年7月25日 · OneLake is a single, unified, logical data lake for your whole organization. A data Lake processes large volumes of data from various sources. Like OneDrive, OneLake comes automatically with every Microsoft Fabric tenant and is designed to …

2025年2月3日 · Each reference architecture has a downloadable PDF in 11 x 17 (A3) format. While the lakehouse on Databricks is an open platform that integrates with a large ecosystem of partner tools, the reference architectures focus only on …

2023年8月29日 · A data lake is a centralized repository designed to hold vast volumes of data in its native, raw format — be it structured, semi-structured, or unstructured. A data lake stores data before a specific use case has been identified. This flexibility makes it easier to accommodate various data types and analytics needs as they evolve over time.

2024年8月12日 · "Data Lake Architecture" explores the foundational principles and practical steps for building a scalable and efficient data lake. It covers key components such as data ingestion, storage, processing, and governance to ensure effective management and analysis of large-scale, diverse data sets.

Lake, as defined in the IBM Redbook, is a set of centralized repositories containing vast amounts of raw data (either structured or unstructured), described by metadata, organized into...

The following diagram shows this guide's reference architecture for growing and scaling a data lake on the AWS Cloud. A data producer layer in different AWS accounts. A data consumer layer in different AWS accounts. A centralized catalog in an AWS account.

2025年1月13日 · Learn how to set up an analytics lakehouse. Secure an analytics lakehouse using a common governance layer. Build dashboards from the data to perform data analysis. Create a machine learning model...

2023年6月29日 · Understand data lake architecture: Explore key components, layers, and diagrams revealing how these repositories store and analyze massive datasets. Data lake architecture is the foundation for data analytics, reporting, and decision-making.

Use Creately’s easy online diagram editor to edit this diagram, collaborate with others and export results to multiple image formats. Related Templates. You can easily edit this template using Creately.

In the following sections, we look at the key responsibilities, capabilities, and integrations of each logical layer. The ingestion layer is responsible for bringing data into the data lake. It provides the ability to connect to internal and external data sources over a variety of protocols.