On December 4, 2019, Snowflake, a cloud-based data warehousing solution made a path-breaking announcement that had far-reaching ramifications in data management for organizations around the world. The message was that it was further strengthening its relationship with Amazon Web Services (AWS) to offer customers a more enriching and seamless experience. 

Further proof and testimony of AWS Snowflake is borne out by the Competency status recently achieved by Snowflake for Amazon Web Services Machine Learning (MI) and Artificial Intelligence (AI). The Competency program focuses on AWS Partner Network (APN) members passing a rigorous audit of their architecture, security, and customer adoption. They have to also show proven success in supporting specialized solution areas of customers. Achieving Competency status by Snowflake in these categories has shown that as an APN member, the cloud-based solution is capable of delivering highly dedicated technical proficiency. 

This announcement marks another milestone in the AWS Snowflake relationship of which there are several others too. The previous ones include – 

  • Integration of Snowflake with Amazon Forecast and Amazon Personalize. It enables organizations to benefit from the best practices in the retail world established by AI and ML services. 
  • Pre-configured integration between Snowflake and Amazon Sagemaker. The latter is a solution that provides data scientists and developers with the tools to train, build, and deploy ML models quickly. More information can be had in a recently-published eBook on the use of Amazon Sagemaker with Snowflake. 
  • Snowflake has achieved the AWS PrivateLink Ready designation that differentiates it as an APN member with product integration. It is particularly useful for regulated industries like healthcare, finance, and others that want to migrate to the cloud. 
  • Snowflake integrates with Amazon Managed Streaming for Kafka (Amazon MSK). It is a fully-managed service that makes it convenient for users to create and run applications that use Apache Kafka for processing streaming data. This AWS Snowflake integration is in addition to that with Amazon Kinesis and AWS Glue.
  • For helping customers cater to their Online Transactional Processing and Online Analytical Processing needs, Snowflake has integrated with Amazon Aurora.

After the announcement of AWS Snowflake integration in December 2019, the two have organized more than 300 “Zero to Snowflake” workshops, showcasing the benefits of migrating from Snowflake to AWS to thousands of technology experts. Snowflake is also available on AWS in Canada and Singapore. 

Enterprises across the world have welcomed this AWS Snowflake integration.  

Yahama’s Director of IT, Ishwar Bharbhari, said. “With Snowflake’s unique and forward-thinking cloud data platform solution and AWS’s reliable, scalable cloud services, we can now use data-driven decisions to create better products, provide better services and ultimately, develop even closer ties with our customers.”

Colleen Kapase, Vice President of WW Partners at Snowflake said, “Snowflake’s cloud data platform was designed to break down the technology and architecture barriers that hold back organizations from maximizing the full value of their data. We’re thrilled to have achieved AWS ML Competency and AWS Retail Competency status, and continue to build on our relationship with AWS to power our customers’ data analytics needs today and tomorrow.”

AWS Snowflake is an advanced data analytics and machine learning (AI) platform. It ensures reporting, executive dashboards, and advanced analytics across its entire data providing platform for all the company’s users. Most critically, it is affordable and controls random access to data, thereby heightening security protocols. 

Snowflake fits in perfectly with the AWS’s data ecosystem as it runs on Amazon Elastic Container Service (Amazon EC2) and Amazon Simple Storage Service (Amazon S3). There are also several other tie-ups.  

Amazon Kinesis, AWS PrivateLink, AWS Glue, Amazon EMR, and Amazon Managed Streaming for Kafka are used to get data into Snowflake. Amazon SageMaker, Amazon Forecast, and Amazon Personalize facilitate machine learning and AI. Amazon Quicksight is used as a BI tool. Overall, Snowflake is an APN Advanced Technology Partner and has met Data & Analytics Competency norms.  

AWS Snowflake optimization is mainly because of the many benefits of Snowflake. Some of them are – 

  • The ability to store semi-structured data such as JSON, Avro, ORC, Parquet, and XML alongside relational data.  All data can be queried with ACID-compliant SQL and dot notation.  
  • Supporting concurrent users with independent virtual warehouses.
  • Snowflake provides separate computing and storage facilities. Users can scale up or down in either of them by paying only for the quantum of resources used.  
  • Per-second pricing is available and organizations are billed only for the time used.   

AWS Snowflake integration is therefore a win-win situation, bringing massive operational data management benefits to all.