Snowflake's Evolution: A Look at the New Features in 2024 - Beinex

Snowflake’s Evolution: A Look at the New Features in 2024

Snowflake's journey since its 2014 release as a "cloud data warehouse" has been nothing short of remarkable. It has continuously evolved, integrating functionalities from various systems into a unified platform. This transformation is evident in its current branding as a "cloud data platform" or the "data cloud."

A significant transformation kickstarted in Snowflake after the onset of 2024, with the introduction of features like full support for transactional workloads, advanced capabilities for Generative AI/LLMs (Large Language Models), and a fully containerized solution for app hosting. Let's delve into the most interesting and innovative features:

19 Apr 2024
Sumi S

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    1. Snowpark Container Services:

    This innovative feature allows you to deploy and manage containers directly within Snowflake. Imagine leveraging secure and scalable infrastructure, similar to Kubernetes, without leaving the Snowflake environment. Use containers to build and run data products like large language models and full-stack applications. You can even utilize containers from the Snowflake marketplace or create custom ones for sharing.

    Getting started is simple. Create a Docker image with your code and dependencies, push it to a Snowflake registry, and then create a service, job, or function using the Snowpark API. Snowflake handles the rest, including provisioning, scaling, and monitoring your containers.

    2. Snowpark Model Registry:

    Data scientists and ML engineers require a secure and efficient way to manage and deploy machine learning models. Snowpark Model Registry, currently in public preview, addresses this need by providing a native solution within Snowflake. This integrated registry allows you to register, manage, and use models and their metadata directly in the platform.

    The benefits of the Snowpark Model Registry are numerous:

  • Centralized Management: Consolidate all your models in a single location, simplifying discovery and access.
  • Improved Governance: Enforce version control, manage dependencies, and track lineage for better model transparency and auditability.
  • Simplified Deployment: Seamlessly deploy models into production environments within Snowflake.
  • With the Snowpark Model Registry, data science teams can focus on building and iterating models, while Snowflake ensures secure and streamlined management.

    3. Streamlit in Snowflake for Azure:

    Snowflake’s commitment to platform flexibility is evident in its expansion to Azure. The general availability of Streamlit in Snowflake for Azure empowers Python developers to create data applications directly within the Snowflake environment. Streamlit simplifies the creation of interactive dashboards and data visualizations, bridging the gap between data and actionable insights for business teams.

    Here’s how Streamlit in Snowflake for Azure benefits users:

  • Simplified Development: Leverage Python skills to build data applications without the complexities of traditional web development.
  • Seamless Integration: Data applications reside within Snowflake, ensuring secure access to governed data.
  • Empowered Business Teams: Business users can interact with data visualizations and gain insights without relying on IT teams.
  • Streamlit in Snowflake for Azure empowers a wider range of users to leverage the power of Snowflake data.

    4. Security Enhancements in Snowflake Horizon:

    Security is paramount for any data platform. Snowflake Horizon takes data security to the next level with a range of enhancements:

  • Network Isolation: Network isolation for internal stages using AWS PrivateLink ensures secure and isolated data transfer between your environment and Snowflake.
  • Granular Network Rules: Define network access based on identifiers, offering more control and flexibility for security admins.
  • Enhanced Authentication: New authentication features allow for customization of the login experience and support for federated authentication using multiple identity providers.
  • These security enhancements within Snowflake Horizon empower organizations to meet stringent compliance requirements and safeguard their valuable data assets.

    5. Snowflake Unistore:

    Snowflake Unistore is a game-changer for working with both transactional and analytical data within a single platform, often referred to as Hybrid Transactional/Analytical Processing (HTAP). This feature introduces the ‘hybrid’ table type, supporting fast single-row operations.

    Hybrid tables utilize a new row-oriented store within Snowflake, enabling functionalities typically associated with transactional data stores:

  • Primary keys for enforcing uniqueness
  • Additional unique constraints
  • Referential integrity through foreign key relationships
  • Declaring secondary indexes
  • Snowflake’s global services layer and query engine seamlessly manage the underlying row and column stores, allowing you to join hybrid and standard tables natively.

    6. Snowflake Iceberg:

    The Apache Iceberg format acts as a metadata layer for data files stored in open formats like Parquet and ORC. It enables querying this data using SQL, regardless of the specific query engine (Spark, Hive, Impala).

    ‘Snowflake Iceberg’ leverages this open format, allowing you to directly query data files stored in cloud storage services like S3 buckets. This eliminates the need to move or copy data into Snowflake while maintaining interoperability for users already working with that data location.

    Snowflake Iceberg offers significant advantages over the existing external table type:

  • Advanced features like snapshot isolation, efficient metadata management, time travel, and schema evolution.
  • 7. Document AI:

    Typically, data systems struggle with unstructured data formats like PDFs. Snowflake’s answer to this challenge is Document AI, a tool currently in private preview. It allows you to process any document and answer questions using natural language without requiring machine learning expertise.

    Document AI draws power from Snowflake’s first-party large language model (LLM) built on Applica’s generative AI technology. This, combined with Snowflake’s support for unstructured data (announced in June 2023), empowers you to store, query, and analyze all data types within the platform.

    Document AI represents just one facet of Snowflake’s vision for generative AI and LLMs, a dominant trend in 2023 that is poised to continue its dominance in 2024.

    Looking Ahead

    Snowflake started strongly in 2024 with exciting releases like Snowpark Model Registry, Streamlit in Snowflake for Azure, and security enhancements in Snowflake Horizon. These anticipated features position Snowflake as a frontrunner in the data cloud landscape, offering a comprehensive data management, analytics, and application development platform.

    Beinex + Snowflake Partnership

    Beinex is a Snowflake Services Partner Premier Tier, and the partnership reaffirms Beinex’s commitment to delivering exceptional data solutions and positions the company at the forefront of industry advancements. Harnessing the true potential of the data, partnership drives innovation and success in the digital era. Belonging to Snowflake Services Partner Premier Tier, Beinex leverages Snowflake’s advanced capabilities and seamlessly integrates them into its comprehensive data solutions.