How is Snowflake re-writing the game of Data Analytics: Strategy View

How is Snowflake rewriting the game of Data Analytics

How is Snowflake re-writing the game of Data Analytics: Strategy View

Snowflake started building a data platform quietly in 2012. Gartner, Forrester were tracking Snowflake for several years. They compared it to Teradata, AWS, GCP Big Query, and many other Product offerings. From a challenger in 2016/17 to the leader quadrant in 2018, Snowflake moved quickly. It tripled its customer base and gave triple-digit YoY growth. Then Snowflake became the poster boy of the tech and data world with its IPO in 2020. 

  • On-premise¬†support. Snowflake came in a ‘Cloud’ only Avatar.¬†

  • Snowflake separated the Compute (processing, Querying) and the Storage (hold the data) tiers. This was an Industry first Innovation. 
  • Snowflake came up with data cloud. This would enable seamless & global sharing of data on a common platform. This would pave way for the Data economy and term coined as ‘Data Marketplace’. 
  • Timely¬†and high-impact use of data exchange came along at the right time. Data Exchange enabled the world’s largest repository of HIPPA Compliant, Anonymized COVID19 data. It featured High-Quality data, Security, and Governance. Data From 30 healthcare companies across the world, shared in real-time for COVID 19 Research.¬†

  • Enterprises and Experts got used to elastic cloud computing and pay by usage models. Snowflake stuck to those industry practices. 
  • Enterprises trusted Cloud providers and their offerings. Snowflake inherited the reliability factor and extended it to become ‘cloud¬†agnostic’. It offered AWS, Azure, or Google cloud as a choice for storage.¬†

Image: AWS’s Reference architect for Data Lake 

Image: Azure’s Enterprise data warehouse architecture hosted on Azure Pipelines and storage 

  • Integration decisions
  • Elaborate tool selection, Pros Vs Cons
  • OpEx, CapEx, and ROI Analysis 
  • and hence, Significant Complexity

  1. Cost savings & Profits from accelerated time to market for new products and enhancements 
  2. Speeding up decisions by putting data in hands of business users and decision-makers 
  3. Productivity gains from Simplified data Operations. Self-Service, Data Discovery, and Data Sharing. 
  4. Infrastructure and data management cost savings 
  5. Monitoring Compliances and risks 

  1. Brings the best of shared-disk and shared-nothing architecture 
  2. Runs with AWS, Azure, or GCP. Abstract data engineering. 
  3. Provides data cloud. It is now known as a data marketplace. It connects hundreds of companies that could share their data. This opens new frontiers for revenue generation & global innovation. 
  4. Industry-first Innovations 
  5. Separate Storage and Compute. 
  6. Security for business at Access, Authentication, Authorization, Data, and infrastructure level. 
  7. Streamlines data governance, Access & sharing across and outside the organization 
  8. Killing the use of copies(redundancies) of data for sharing and other purposes.
  9. Schema on Read. Schema on Write. 

  1. Gartner ‚ÄúMagic Quadrant for Data Management Solutions for Analytics‚ÄĚ by Adam M. Ronthal, Roxane Edjlali, Rick Greenwald.
  2. Gartner, ‚ÄúGartner Peer Insights ‚ÄėVoice of the Customer‚Äô: Data Management Solutions for Analytics Market,‚ÄĚ Peer Contributors. August 6, 2018.?
  3. Snowflake dataset for corona virus research: https://www.snowflake.com/coronavirus-data-sets/
  4. Azure architecture for data lake: https://docs.microsoft.com/en-us/azure/architecture/sulution-ideas/articles/enterprise-data-warehouse

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