Snowflake Arctic, Snowflake Data Cleanroom, Upcoming Jaipur Virtual Meetup, and More...

April is ending today, and with that I am back again with my latest monthly newsletter series on Discover Snowflake Superpowers!

In last 30 day, lot of things have happened (literally a lot!!) like various Snowflake User Groups Meet-up in India, launch of Snowflake's first LLM open source model, Snowflake Copilot, Data Cleanrooms and lot more. If you missed out any of these, then here's a quick wrap-up:

Starting with Snowflake India User Groups

This month, Snowflake India community has introduced various Community chapters in different regions. Every Saturday, there were meet-up been organized by Snowflake India Community in these new regions. Out of these, I am happy to mention that it was a great opportunity for me to kick off Jaipur chapter as a leader. Here are some glimpse of the events.

Linkedin Post from Divyansh Saxena on #Jaipur Meet-up:

Linkedin Post from MAATHU .S on #Chennai Meet-up:

Linkedin Post from Madhivanan Anbalagan on surpassing 1000 users mark in #Bangaluru:

Linkedin Post From Satish T on #Hyderabad Meet-up:

New Virtual Session - Understanding Poorly Performing Queries and Their Optimization

As a Jaipur Community leader, I will be organizing a virtual session on Understanding and Identifying poorly performing queries in snowflake and how to optimize them as part of performance tuning and cost optimization.

The session will be organized in May, so stay tuned and register on the below Jaipur community page for the updates:

Snowflake Arctic: The Best LLM for Enterprise AI — Efficiently Intelligent, Truly Open

Snowflake Arctic, a top-tier enterprise-focused LLM that pushes the frontiers of cost-effective training and openness. Arctic is efficiently intelligent and truly open.

Snowflake Arctic is available from Hugging Face, NVIDIA API catalog and Replicate today or via your model garden or catalog of choice, including Snowflake Cortex, Amazon Web Services (AWS), Microsoft Azure, Lamini, Perplexity and Together over the coming days.

Arctic is on par or better than both LLAMA 3 8B and LLAMA 2 70B on enterprise metrics, while using less than ? of the training compute budget. Similarly, despite using 17x less compute budget, Arctic is on par with Llama3 70B in enterprise metrics. It does so while remaining competitive on overall performance.

Arctic uses a unique Dense-MoE Hybrid transformer architecture. It combines a 10B dense transformer model with a residual 128×3.66B MoE MLP resulting in 480B total and 17B active parameters chosen using a top-2 gating.

Training efficiency represents only one side of the efficient intelligence of Arctic. Inference efficiency is equally critical to allow for the practical deployment of the model at a low cost. Arctic represents a leap in MoE model scale, using more experts and total parameters than any other open sourced auto-regressive MoE model.

Arctic was built upon the collective experiences of Snowflake diverse team, as well as major insights and learnings from the community. Open collaboration is key to innovation, and Arctic would not have been possible without open source code and open research insights from the community.

Read more about Snowflake Arctic on Official release at:

Snowflake Data Cleanrooms:

Data clean rooms offer a secure way to gain valuable insights while protecting sensitive information. They allow you to combine and analyze data from different parties without worrying about the privacy concerns that go with sharing raw data. With data clean rooms, multiple parties can collaborate without revealing their underlying data.

Benefits of data clean rooms include:

• Enhanced privacy — Protects sensitive data while enabling collaboration.
• Deeper insights — Combines data from multiple sources for richer analysis.
• Increased security — Reduces the risk of unauthorized access.

With Snowflake Data Clean Rooms, all analyses are conducted within the secure environment of the clean room. Collaborators are able to return aggregated results and insights, but cannot directly query the raw data in the clean room. The collaborator who is sharing their data can define what analyses are available to the other collaborators, allowing them to tightly control how their data is used.

Wanted to get started with Data Cleanrooms?

Use the below link to get started:

Snowflake Copilot - Your SQL Guru!

Snowflake Copilot is an LLM-powered assistant that simplifies data analysis while maintaining robust data governance, and seamlessly integrates into your existing Snowflake workflow.

Snowflake Copilot is powered by a model fine-tuned by Snowflake that runs securely inside Snowflake Cortex, Snowflake’s intelligent, fully managed AI service. This approach means that your enterprise data and metadata always stay securely inside Snowflake. Snowflake Copilot also fully respects RBAC and provides suggestions based only on the datasets that you can access.

Snowflake Copilot uses natural language requests to enable data analysis from start to finish. To start, Copilot can help answer questions about how your data is structured and guide you in exploring a new dataset. You can then ask Copilot to generate and refine SQL queries to extract useful information from your data. Snowflake Copilot can even help improve your SQL query by recommending optimizations or suggesting fixes for possible issues.

Supported use cases

• Explore your data by asking open-ended questions to learn about the structure and nuances of a new dataset.
• Generate SQL queries with questions in plain English.
• Try out the SQL query suggested by Snowflake Copilot with the click of a button. You can also edit the query before running it.
• Build complex queries through a conversation with Snowflake Copilot by asking follow-up questions to refine the suggested SQL query and dig deeper into the analysis.
• Learn about Snowflake by asking questions about Snowflake concepts, capabilities, and features.
• Improve your queries by asking Snowflake Copilot to help you assess query efficiency, find optimizations, or explain what the query does.
• Provide feedback (thumbs up or thumbs down) on each response from Snowflake Copilot, which will be used to improve the product.

Read more on Snowflake Copilot at:

Snowflake Reposted my Article on Pyspark VS Snowpark!

I was so excited when I noticed that Snowflake reposted my article where I breakdown between using #Snowpark and Pyspark by providing a walk through of setup configs, code execution performance and dataframe transformations comparison for local environment setup.

About Me:

Hi there! I am Divyansh Saxena

I am an experienced Data Engineer with a proven track record of success in Snowflake Data Cloud technology. Highly skilled in designing, implementing, and maintaining data pipelines, ETL workflows, and data warehousing solutions. Possessing advanced knowledge of Snowflake’s features and functionality, I am a Snowflake Data superhero & and Snowflake Snowpro Core SME. With a major career in Snowflake Data Cloud, I have a deep understanding of cloud-native data architecture and can leverage it to deliver high-performing, scalable, and secure data solutions.

Follow me on Medium for regular updates on Snowflake Best Practices and other trending topics: