Snowflake Ecosystem: Unlock New Innovations and Stay Ahead

Snowflake recently hosted its Snowday 2023 event. The company announced a wave of groundbreaking updates. These enhancements span the entire Snowflake ecosystem. They include improvements to data governance, performance, and AI integration. These innovations are poised to revolutionise how businesses manage and leverage data. This post explores the key announcements from Snowday 2023. We’ll also examine their potential impact on the future of the Snowflake ecosystem.

Christian Kleinerman, SVP of Product at Snowflake, led the presentations. He unveiled a myriad of innovative features. Let’s delve into the details.

Data foundation: Elevating the core of the platform

The Data Foundation segment showcased enhancements to the core platform. These include interface improvements and new features. These aim to simplify user interactions.

Unistore for mixed workloads

Unistore was initially introduced at the SUMMIT. It has now progressed to a public preview of hybrid tables. This is slated for release in late 2023. This feature allows the merging of OLTP and OLAP workloads. This enables real-time analytics. For instance, an e-commerce company could use Unistore to capture user interactions immediately. This data can then fuel real-time analytics, leading to a more personalised user experience.

Moreover, Unistore can streamline operations. It can reduce the need for multiple databases and pipelines. This is achieved by enabling fast, single-point operations within Snowflake. This, in turn, accelerates development. New features and use cases can be implemented more quickly.

Enhanced data lake versatility with Iceberg tables

Another significant development is the integration with Apache Iceberg tables. This feature, previously highlighted on the TechRadar by Devoteam, is entering public preview. It offers two distinct options: unmanaged and fully managed Iceberg tables. This integration brings increased versatility to all workloads within the Snowflake ecosystem. This includes data warehousing, data lakes, and data lakehouses.

Performance improvements

Snowflake announced a range of performance enhancements. These primarily target corporate data lakes. New features for handling semi-structured data are now available. Snowflake also introduced dynamic file processing with Snowpark. This supports both Python and Scala.

These improvements extend to traditional data warehouses as well. Enhancements include automatic clustering cost estimation. Materialised view refresh has also been improved. Furthermore, Snowflake now supports INSERT statements for its query acceleration service.

These enhancements collectively contribute to the Snowflake Performance Index. This index reflects the overall performance of the platform. It is accessible through Snowflake’s web interface. Data shows a 15% performance improvement compared to the previous year.

Ensuring robust data governance within the Snowflake ecosystem

Snowflake Horizon is a major announcement. This is Snowflake’s comprehensive data governance framework. It addresses key aspects of data management. These include compliance, security, privacy, interoperability, and access. Horizon emphasises providing granular control over data. This applies across all platforms and regions within the Snowflake ecosystem. It helps ensure compliance with various regulations.

Several security enhancements were also announced:

• Enhanced network security: This includes improved management of IP access lists. This feature is currently in public preview.
• Improved authentication: Snowflake is enhancing its authentication mechanisms. This is coming soon to public preview.
• Database roles: New roles at the database level expand RBAC capabilities. This feature is generally available.

A particularly noteworthy announcement is the CIS Snowflake Foundation Benchmark. This provides a valuable resource for security best practices. It helps ensure consistent and robust security policies. Combined with the upcoming Trust Center and a new security section in the user interface, Snowflake is significantly bolstering its security capabilities.

Privacy is another key focus area. Snowflake announced differential privacy policies. These are still under development. They aim to add “noise” to data, making it less identifiable as granularity increases.

Interoperability centres on compatibility with various standards. This includes support for new cataloging standards, such as external catalogs and Iceberg table catalogs. Snowflake also announced access to Iceberg tables from REST APIs for Snowpark.

The final pillar of Horizon is access control. Snowflake introduced auto-classification and custom classifiers. These features leverage LLMs and AI to automatically classify objects.

Snowflake also unveiled Snowflake Copilot. This feature, currently in private preview, introduces natural language interaction capabilities. This is similar to ChatGPT. It promises to simplify the creation of SQL statements.

Cost management: Enhancing financial control

Snowflake introduced a new cost management interface. This empowers administrators with tools for greater financial control. Key features include Cost Insights, which offers real-world examples for optimisation. The Budgeting view provides a comprehensive overview of spending.

Enhanced visibility is provided through charts and dashboards. These visualisations display workload metrics for each warehouse. This aids in evaluating scaling needs and considering multi-clustering. Cost-per-query charts help identify queries that may require re-engineering.

Snowflake is actively promoting optimisation. The Cost Insights section provides practical examples. It identifies specific situations where improvements are possible. It also offers explanations and guides on applying best practices.

Snowflake also reviewed the Budgeting view. This allows users to track their spending over time. This tracking can be automated with email or message notifications. Budgets can be configured for individual resources, such as databases, schemas, tables, or warehouses.

How the Snowflake ecosystem empowers machine learning

Snowflake announced the upcoming general availability of its ML Modeling API. This API introduces several key features, including feature engineering, model training, the Snowflake Model Registry, and the Snowflake Feature Store. These aim to enhance machine learning capabilities within the Snowflake ecosystem.

To facilitate the transition for data analysts and data scientists, Snowflake is introducing Snowflake Notebooks. This feature is currently in private preview. This new interface, built using Streamlit, allows for cell-based development. These cells support various languages and formats, including Python, Streamlit, SQL, and Markdown.

For more demanding machine learning applications, Snowflake offers the Snowpark Container Service. This will soon be in public preview. This service enables the deployment of containerised applications. These applications can be developed using Snowpark and run within the Snowflake ecosystem.

Snowflake Cortex: Elevating AI accessibility

Snowflake is committed to democratising access to AI. This is evident in the Cortex engine. Cortex comprises several serverless services managed by Snowflake. These services provide easy access to state-of-the-art LLMs and AI models.

Snowflake introduced specialised functions. These are currently in private preview. They incorporate capabilities such as language translation, sentiment analysis, text summarisation, and answer extraction.

Snowflake also introduced general functions. These leverage existing functions from leading LLMs, such as Llama 2.

These new features underscore Snowflake’s dedication to making Cortex a key differentiator.

Scale with applications: Native integration evolution

Snowflake provided updates on native application integration. They announced Database Change Management. This feature is in private preview. It allows for the execution of scripts directly within your Snowflake account. These scripts can be sourced from a Git repository, thanks to the GitHub integration announced at the SUMMIT. This feature supports DML statements and enables CI/CD for Snowflake development.

Another announcement is the Native App Framework. This is currently in private preview and will soon be generally available on AWS. It provides an environment for deploying and running applications within the Data Cloud. To accelerate the development of native apps, Snowflake announced a $100 million investment fund.

Future-proof your business with the evolving Snowflake ecosystem

Snowflake has delivered a robust set of new features and enhancements. These strengthen its position as a leading cloud data platform. The platform’s focus on performance, governance, AI, and cost control makes it a comprehensive solution. It caters to the needs of both technical specialists and organisations.

Snowflake’s continuous innovation is reshaping the data landscape. We are dedicated to keeping you informed about these transformative advancements.