Cloud Data Lake 比较指南：探索来自 AWS、Azure、Google、Cloudera、Databricks 和 Snowflake 的解决方案.pdf

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2022-06-12

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Cloud Data Lake

Comparison Guide

AWS, Azure, Google, Cloudera, Databricks, and Snowflake

First created to overcome the limitations of the traditional

data warehouse, data lakes oer the scalability, speed,

and cost eectiveness to help you manage large volumes

and multiple types of data across your various analytic

initiatives – AI, machine learning, streaming analytics, BI,

and more.

While early on-premise solutions like Hadoop paved the way for data lakes and created

their initial architectural framework, cloud platform providers and other cloud-native

innovators have further enhanced the possibilities. Today, with data lakes increasingly

migrating to the cloud, you can take advantage of new benefits – avoiding the high

upfront costs of setting up and maintaining a lake, and focusing on getting the most

value out of your data.

However, with a variety of cloud data lake providers, from traditional vendors to

newer data lakehouse oerings, selecting the right solution can be a challenge. This

eBook provides a better understanding of the core dierences between platforms,

so you can make the right decision for your needs.

Data lakes make their move to the cloud.

LIMITATIONS OF ON-PREMISE LAKES

Elasticity

Lack of security and governance

High maintenance costs

ADVANTAGES OF CLOUD-BASED LAKES

Decoupled storage and compute

Built-in security and encryption

Transparent scaling

Flexible on-demand infrastructure

Consumption-based pricing

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