Resource Categories: EBook

Sharing data creates shared business opportunities. However, it can only go so far before issues with data sprawl, governance, and cost make it difficult to extract value from data—keeping teams from realizing mutually beneficial opportunities.

To truly collaborate with data, organizations need to go beyond data sharing. Collaboration technologies—like Snowflake’s privacy-preserving collaboration capabilities—can help organizations accelerate revenue growth, optimize operations, and improve data security and governance.

But why does collaboration matter? And how can you move your business from just sharing data to sharing services and apps, or to collaborating with partners by bringing data together for joint analysis in a data clean room?

Our ebook has everything you need to understand the data collaboration advantage, including:

• The relationship between data sharing and data collaboration

• What a data collaboration solution should look like

• How Snowflake enables new modes of collaboration with capabilities that allow you to analyze data without the need to share it

The past few years may be etched in the minds of manufacturing professionals for a long time. Disruption has become the new norm as supply and distribution lines have been impacted by the global pandemic, geopolitical conflict, and an uncertain economy. 

Fortunately, there are bright spots in all the turbulence—neither business nor innovation slowed down despite these global pressures. The resilience and innovation of those using or quickly moving into Industry 4.0 with its emphasis on AI/machine learning, smart automation, and data-driven insight and collaboration suggest that maybe the troubles of the past few years have indeed moved us into a better future. 

Read on to find out how Snowflake customers are optimizing product performance through access to high-volume, connected (IoT) data and service history.

Brand advertisers and ad agencies are rapidly adapting to a changing digital landscape. Consumers are taking steps to remove advertising and tracking from their online experiences. The industry faces increased regulatory and privacy scrutiny as consumers and regulators demand transparency into what data is captured and how it is used. 

Now more than ever, the industry is poised to reap the benefits of an effective data cloud solution to meet these challenges and leverage market opportunities. 

Read this ebook to learn how media companies and ad agencies are:

• Optimizing for advertising effectiveness
• Drive privacy-enhanced advertising
• Leverage data from planning to measurement
• Build your own tech stack to power collaboration and efficiencies

Get the ebook now.

At Snowflake, we find that as companies seek to digitize their operations, moving an increasing amount of functionality to a variety of cloud applications and partners, the need for a centralized data platform has never been greater.

This is especially true as companies aspire to make data-driven decisions across their business, even as they collect and store data from an increasingly wide range of sources. Data warehousing solutions available within data platforms like Snowflake allow for companies to establish both a centralized data repository as well as a mechanism through which queries can be processed and analyses can be conducted at speed and scale. Ideally, the right data warehouse solution enables companies to access a complete, governed set of data and generate key business insights to operate effectively while generating potentially significant cost savings.

In this ebook, we explore six case studies featuring innovative companies that have used Snowflake for data warehousing to address a wide range of common business challenges–and achieved considerable, positive business outcomes as a result.

It’s a challenge familiar to many data-driven organizations: how to make data insights available to the business in a way that drives real value. Data applications can present machine learning (ML) insights to business users in the terms and metrics they use every day, but data scientists have not had a way to build and deliver those apps easily. As a result, far too many ML-based models have not been fully consumed by the business because they are not accessible to less-technical audiences. 

In this ebook, you’ll learn how data scientists can now drive better value for the business by building data applications in their preferred language—Python. By using Snowflake to build and deploy ML models at scale and Streamlit to build interactive apps in Python, you can bridge the gap that’s prevented your organizations from fully transforming into a data-first company. 

Read on to find out how you can:

• Use Snowflake to build and deploy ML models at scale
• Use Streamlit to build interactive apps in Python
• Leverage Streamlit-built apps to securely iterate with business teams to ensure your ML-derived insights deliver maximum value

Stop bad actors before attacks escalate into breaches with a faster, easier, and more comprehensive way to capture and analyze years of security data. To do this, security operations centers (SOCs) must modernize their cybersecurity systems to enable fast analytics on petabytes of data.

Standalone information event management systems (SIEMs) can’t scale for the growing size and complexity of security data, and they often have weak analytical and reporting capabilities.

This ebook describes how a modern security data lake, deployed in the Snowflake Data Cloud can deliver comprehensive visibility and powerful automation across five security use cases:

1. Conducting effective threat hunting
2. Detecting compromised employee credentials
3. Proactively applying IOC data to existing logs
4. Investigating breaches 
5. Tracking and analyzing security metrics