Why I Love Snowflake?

Let's start with the first question. Why do I love Snowflake? Because its a data strategy, not just a tool or a database and it sits in my domain of Data Unification (Unified Profiles) and AI. I was going to write a review but decided to just copy and past a lot of rapid fire Q&A with associated Use Cases to get us going. Enjoy.

What is Snowflake? A Snowflake database is where an organization's uploaded structured and semistructured data sets are held for processing and analysis. Snowflake automatically manages all parts of the data storage process, including organization, structure, metadata, file size, compression, and statistics. (*Note: Salesforce is not a database for managing large data volumes, its build for speed, action and insight - hence why Snowflake is such a valued partner)

Let me explain with another question. What is the relationship between Snowflake and Salesforce? Snowflake and Salesforce have built on their existing partnership to unify the full breadth of customer and business data and generate actionable insights for our customers.

Salesforce's Data Cloud has embraced high standards that enable the ability to share live data with Snowflake. It allows Snowflake to access and query Salesforce data without copying all the data from Salesforce to Snowflake on a batch or event basis. (nice...)

Why does Salesforce use Snowflake? Salesforce data sharing to Snowflake reduces the overhead and effort to maintain multiple copies of the same data. Salesforce Data Cloud has introduced consumption-based pricing that aligns with platform and infrastructure service solution providers such as AWS, Google, Azure, and Snowflake.

See Article here on 'Customer Insights with Salesforce and Snowflake Data Sharing-Based Integration'

• Organizations can leverage Salesforce data directly in Snowflake via zero-ETL data sharing to accelerate decision-making and help streamline business processes.?
• New data integration paradigm that eliminates friction and accelerates time to insight
• *Build powerful AI solutions to drive visibility, actionable insight.?(See example below)
• This integration makes it easy to convert data into insights. For example, a retail business can quickly combine its Salesforce sales data with external market trends from Snowflake. This helps them identify changes in customer behavior, allowing for smarter inventory management and marketing decisions, all leading to better customer experiences through AI-driven insights.
• *Use Cases at the bottom.

What is the difference between Snowflake and Data Cloud? First, what is Salesforce Data Cloud? Data Cloud is the fastest growing organically built product in Salesforce's history (i.e. Salesforce built it themselves, not via acquisitions). Data Cloud could be described as the 'Holy Grail of CRM', meaning that the data problem that's existed since the infancy of CRM is now finally solvable. CRM+Data Cloud+AI.

Salesforce Data Cloud specializes in data enrichment, personalization (Unified Profile), and real-time updates, while Snowflake boasts scalable data warehousing and powerful analytics capabilities. Understanding the strengths & weaknesses of each platform is crucial for businesses seeking to make informed data-related decisions.

What type of database is Snowflake? Snowflake is a verticalized and industry focused cloud-hosted relational database for building data warehouses. It's built on AWS, Azure, and Google cloud platforms and combines the functionalities of traditional databases with a suite of new and creative capabilities.

What is Snowflake and why it is used? Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings.

Designed with a patented new architecture to handle all aspects of data and analytics, it combines high performance, high concurrency, simplicity, and affordability at levels not possible with other data warehouses.

The Snowflake data platform is not built on any existing database technology or “big data” software platforms such as Hadoop.

What is the difference between SQL and Snowflake? This means that Snowflake can be a more cost-effective option for organizations with large amounts of data to process, while SQL Server may be more economical for smaller organizations with fewer data processing needs. Another key difference between Snowflake and SQL Server is their approach to data modeling.

Why use Snowflake instead of AWS? Snowflake is so popular because of its unique data platform architecture; separate compute and storage, which helps speed up operations and extensibility. Also, Snowflake integrates with the major public clouds, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

Why companies are moving to Snowflake? Snowflake provides a unified data platform or single source of truth (SSOT) that allows businesses to consolidate their data in a single location. By migrating their data to Snowflake, businesses can break down data silos and create a centralized and holistic view of their data.

Snowflake Use Cases with Salesforce:

Here are five ways customers are already using it to gain new insights and make their business operations more efficient:

• Unified customer insights: Bringing together customer data from Salesforce applications and Data Cloud objects into Snowflake, and combining it with data from ERP and line-of-business applications to create unified customer insights, and act on those insights in near real time.

• AI and machine learning (Next Best Action, Propensity to Buy): Building AI and machine learning models natively in Snowflake to determine customer purchase propensity scores for specific product categories. By joining Salesforce objects such as profiles and website visits with Snowflake’s POS Data and product category data, organizations are able to unlock valuable insights that are actionable.

• Near real-time forecasting: Access to opportunity data shared in near real time from Salesforce and combined with ERP and finance data in Snowflake to build near real-time, end-of-month/quarter/year forecasts that leverage their enterprise data.

• Enriched segmentation that incorporates customer data from Salesforce, product telemetry data in Snowflake, and applied data science algorithms in Snowflake.
• Campaign performance analytics: Merging click data coming from Salesforce with finance data in Snowflake to optimize campaigns by channel. That's it for today. More to come later. Peace.