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Data & Analytics

Pipelines, data mesh, lakehouses and the data foundations AI depends on.

6 resources · 1 articles · 2 partners

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Guide

The Essential Guide to Data Sharing

The ability to share data quickly and securely, both inside and outside an organization, is mission-critical to success in the age of AI. However, traditional methods for data sharing can be cumbersome, resulting in stale, out-of-date data and delayed time to insights.

SnowflakeData
Whitepaper

How to Knit Your Data Mesh on Snowflake

Stung by organizational bottlenecks and data governance shortfalls, companies in all industries are choosing data mesh to decentralize data management and improve data agility.

SnowflakeData
Whitepaper

7 Snowflake Reference Architectures for Application Builders

Data application developers need a data stack architecture with built-in scalability and connectivity. A cloud data platform delivers the performance and nearly infinite autoscaling needed to launch and scale applications quickly and cost-effectively.

SnowflakeData
Report

Data Strategies for AI Leaders

As generative AI (gen AI) adoption transforms the competitive landscape, businesses without a strong data strategy will find their ambitions limited, according to a new report by MIT Technology Review Insights, in partnership with Snowflake.

SnowflakeData
eBook

Big Book of Data Engineering

As data volume and complexity increase, engineers are left figuring out how to manage, monitor and maintain fragile pipelines while also handling fragmented tools.

DatabricksData
eBook

Lakebase For Dummies

For decades, databases bundled compute and storage. This made provisioning new databases slow and locked data into proprietary formats. Today, teams ship code in seconds and build apps, agents and AI products faster than ever.

DatabricksData
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