A Strategic View of How Snowflake is Re-writing the Game of Data Analytics

Snowflake Data Analytics Game

A Strategic View of How Snowflake is Re-writing the Game of Data Analytics

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 (holding the data) tiers. This was an industry-first Innovation. 
  • Snowflake came up with a data cloud. This would enable seamless & global sharing of data on a common platform. This would pave the way for the Data economy and the 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 COVID-19 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. 

Most of the enterprise cloud ecosystem is hybrid in nature and multi-cloud focused. They use some combination of¬†AWS,¬†Azure,¬†Google,¬†or Oracle offerings. Each cloud vendor’s product works best within their ecosystems. It works “ok” when paired with other vendor’s products. Also,¬†not¬†all products from one Vendor are the best. Google has strengths in deep learning,¬†AI¬†& ML. AWS has depth and breadth, with an almost confusing toolset,¬†and so on.¬†

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. 

While Moore’s law paved way for innovations in past decades,¬†data insights¬†from Applied ML at scale will drive the next wave. The power of data is¬†just¬†beginning to unleash. Snowflake promises to be an excellent platform for that purpose. Data Marketplace¬†promises to lead¬†the¬†global¬†data¬†discovery, as more companies bring and share their data sets online. Larger efforts like COVID 19 or Cancer Research, or innovations in Aviation,¬†Automotive, or¬†Manufacturing¬†will further fuel the data economy.¬†However, there are headwinds for Snowflake, with the cloud vendors pushing their products like Redshift,¬†Big Query¬†harder and everyone focused on data. Only one thing is for sure, the road ahead is about to get interesting and rewarding for the enterprises and end customers.¬†

  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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