Resource Categories: EBook

For healthcare and life sciences organizations, third-party data is increasingly important to achieve better patient outcomes and business success. Across these industries, organizations are using third-party data to empower their teams with data-driven insights, enhanced clinical and business decision-making, and optimized go-to-market and marketing strategies.

In this ebook, you will learn how to do the following:

• Access live third-party data without any ETL, making the data immediately available for analysis or to merge with your own data
  
• Easily discover third-party data sets, such as demographic data, anonymized prescription data, medical sales data, and more that best fit your business needs
  
• Leverage the Healthcare & Life Sciences Data Cloud to solve traditional data sharing challenges that enable seamless data collaboration and data exchange 

Get the ebook now.

The retail industry has come a long way from its brick-and-mortar legacy, to leveraging purchase data, demographic and psychographic data, location data, and other types of intelligence to personalize customer experiences, improve campaign performance, and, ultimately, increase basket size and revenue.

Retailers and their consumer company partners are also harnessing data on the supply chain and fulfillment end of the business as companies leverage weather data, transportation data, and more, to help make decisions about inventory management and staffing.

To empower their teams to make data-driven decisions, retail and consumer companies are increasingly leveraging third-party data and integrating it with their own first-party data. However, traditional methods for sourcing third-party data are inefficient, may not scale, and can require extensive engineering work before the data can be used.

In this ebook, you will learn how to:

• Easily discover and access third-party data without any ETL, making the data immediately available for analysis or to merge with your own data
• Unlock the value of your data with more powerful segmentation and targeting and personalize customer experiences
• Solve traditional data sharing challenges, such as manual and ad hoc methods of data sharing, and more.

Snowpark is a developer framework for Snowflake that brings data processing and pipelines written in Python, Java, and Scala to Snowflake’s elastic processing engine. Snowpark allows data engineers, data scientists, and data developers to execute pipelines feeding ML models and applications faster and more securely in a single Snowflake platform using their language of choice.

This ebook gives an overview of how to leverage Snowpark for Python and its partner ecosystem for data engineering and machine learning use cases, including:

• Deploying custom business logic with Python User Defined Functions
• Transforming data using both SQL and Python
• Scoring models and deploying ML features at scale

What can you build with Snowpark? Download our ebook to get started.

Want to turn your unstructured data from a curse into a blessing? Making this vast, potentially valuable resource of complex data into useful, accessible information can revolutionize your data-driven systems and processes—if you take the right approach. 

The variety, velocity and volume of today’s unstructured data can overwhelm traditional data platforms built for structured or semi-structured data. You need a modern approach that gives you an easy way to access, process, and govern the stores of unstructured data coming in from social media, image files, document scans, call center recordings, and more.

In this ebook, you’ll learn about the most common challenges for managing unstructured data and incorporating it into your analytics, as well as the three core capabilities of an effective unstructured data management solution. 

さまざまな最新アプローチを駆使してデータを管理、分析していても、バラバラのデータサイロをいくつも作り出し、それに頼るしかないアナリティクスソリューションは珍しくありません。このようなソリューションでは、ITスタッフの仕事が煩雑になり、企業が価値を得るまでに長い時間がかかってしまいます。多くの企業がデータやアナリティクスをクラウドに移行しているものの、移行後、さまざまなタイプのデータや多様なアナリティクスの取り組みを組み合わせて有機的な戦略を組み上げる段階で苦慮している企業は少なくありません。

本物のクラウドデータプラットフォームでは、最新のデータウェアハウスとデータレイクから最大の利益を引き出せるため、企業はインフラストラクチャーの管理に時間を取られずデータ管理に移行することができます。Snowflakeクラウドデータプラットフォームでは、バラバラのデータソースから多様タイプのデータを取り込み、保管、統合、共有し、さまざまなチームにそれぞれのデータからインサイトを引き出すために必要なリソース、柔軟性、インサイトを提供し、チームの能力を拡充することができます。

自社のサービスと商品を強化し、オペレーションを合理化し、顧客に関するインサイトを深める手段として、各種金融機関はクラウドベースのデータテクノロジーを重視しています。

この電子書籍の特徴は、Snowflakeクラウドデータプラットフォームを活用しているフィナンシャルサービス業者のサクセスストーリーです。導入によりデータ規模の上限が取り払われ、機密情報と規制対象情報を安全に保管できるようになった結果、各社各様のやり方で、あらゆる側面から顧客を把握し、金融分析を加速・向上させています。

金融機関はどのような方法で膨大なデータを自在に使いこなし、さまざまなインサイトを拾い上げて、より良い意思決定につなげているのでしょうか？

フィナンシャルサービス業界ではデータの価値を引き出すために、次の3つのトレンドが見られると業界の専門家らが認めています。

• より本格的なクラウド依存

• データコラボレーションの活発化

• 最新のデータ戦略とデータテクノロジーの採用

この電子書籍では、これらのトレンドの詳細を明らかにし、銀行、証券会社、保険会社、金融テクノロジー分野のスタートアップ企業がSnowflakeのプラットフォームを活用してデータを簡単かつ安全に収集・保管・分析・共有している様子をご紹介します。

There’s never been a better time to be an entrepreneur looking for investment funding. Global venture capital activity grew mightily in 2021, and the trend appears to be continuing into 2022. 

However, that doesn’t mean building a new company is any easier. The same inherent resource and growth challenges exist, and venture capitalists still want to see value creation and strong indicators for future success before they invest.

So how do you create a company that can grow and scale to win a large market (and gain investors’ interest)? In this ebook, we’ve distilled advice and lessons from the inaugural Snowflake Startup Challenge into five best practices for building a successful startup that can grow and scale to win a large addressable market:

• Deliver a unique solution that aligns with people’s needs
• Know your customer
• Refine your business model
• Assemble a robust team
• Execute and scale

Legacy pipelines designed to accommodate predictable, slow-moving, and easily categorized data via extract, transform, load (ETL) processes are no longer adequate for the diversity of data types and ingestion styles of the modern data landscape.

Modern data pipelines are designed to extract and load the data first and then transform the data once it reaches its intended destination—a cycle known as ELT. Modern ELT systems move transformation workloads to the cloud, enabling much greater scalability and elasticity.

In this ebook, we explore:

• the advantages and disadvantages of each approach
• how to establish a versatile data management strategy
• when to consider ETL vs ELT for your data pipelines

To learn more, download our ebook, Moving from On-Premises ETL to Cloud-Driven ELT.