What is Snowflake?
Snowflake is a Data Cloud platform designed to unify, integrate, analyse, and share data at incredible speed and scale. It efficiently handles structured, semi-structured, and unstructured data from various sources, including databases, files, and data streams. Snowflake operates across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, spanning over 20 global regions.
From its inception, Snowflake aimed to eliminate data silos by creating a unified, secure environment for all data. It supports a single governance model across all platform use cases. Although initially focused on analytics, Snowflake now encompasses data engineering, data science, machine learning, and artificial intelligence applications. Snowflake strives to enable seamless collaboration across data, using a high-performance compute engine to deliver valuable insights.
Who benefits from Snowflake?
Snowflake benefits organisations, particularly those with complex international data environments or limited internal IT resources.
As a fully managed cloud service, Snowflake eliminates the need for hardware configuration or maintenance. Users don’t need to install, configure, or manage software. Snowflake handles all upgrades, management, and tuning automatically. Running entirely on cloud infrastructure, it ensures a smooth user experience, free from operational overhead.
Exploring the Snowflake data cloud architecture
Snowflake’s architecture features a central data repository accessible by all compute nodes. It processes queries with massively parallel processing (MPP) clusters, where each node manages a portion of the data. This structure simplifies data management while delivering outstanding performance and scalability.
Snowflake’s architecture comprises three layers:
- Database Storage: Snowflake organises and compresses data in a columnar format, accessible only through SQL queries.
- Query Processing: It uses virtual warehouses—independent MPP clusters—to process queries without sharing compute resources, ensuring stable performance.
- Cloud Services: This layer manages essential activities, including authentication, access control, query parsing, and infrastructure management.
Snowflake’s micro-partition format allows high concurrency without common issues like data skew. The Time Travel feature enables users to query past data versions, while cloning capabilities reduce the need for separate test environments.
Data sharing with Snowflake
Snowflake allows users to share live, query-ready data across accounts within the same cloud region. Beyond data, users can also share business logic and services, providing ecosystems with essential tools. When data sets appear on the Snowflake Marketplace or a private exchange, they can be distributed across clouds and regions with secure, native replication.
Snowflake’s fine-grained governance and access controls ensure compliance with varied security requirements across industries and regions.
Powering AI/ML with Snowflake
Snowflake centralises data, simplifying machine learning (ML) and AI processes. Running ML or AI algorithms close to the data removes the need for complex data pipelines, speeding up operations and shortening time to market for data products. Snowflake’s streamlined operations enable businesses to implement AI applications directly within the platform.
Snowflake also offers Snowpark, a developer environment supporting Java, Python, and Scala for building ML models and applications. Snowpark ML Modeling, currently in open preview, includes Python APIs for data preprocessing and model training.
Key benefits of Snowflake
- Eliminate data silos: Snowflake’s scalability and data-sharing features allow seamless data access across departments, regions, and partners.
- Accelerate query performance: Materialised views (available in the Enterprise Edition) provide pre-computed datasets, speeding up queries on large data sets.
- Built-in governance: Snowflake Horizon ensures data compliance, security, privacy, and lineage management within the Data Cloud.
- Use AI to maximise data value: Snowflake’s new features enable users to incorporate large language models (LLMs) in analytics, build GenAI applications, and fine-tune foundational models.
In conclusion
Snowflake combines data warehousing, data lake, data engineering, data science, business applications, and data-sharing services into a single platform. It breaks down technical and organisational barriers, democratising data access across enterprises. With its multi-cloud, multi-region architecture and unique AI capabilities, Snowflake empowers analysts, data scientists, developers, and business leaders to unlock the full potential of their data.
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This article and infographic are part of a larger series centred around the technologies and themes found within the 2023 edition of the TechRadar by Devoteam report. To learn more about Snowflake and other technologies you need to know about, please explore the TechRadar by Devoteam.
