Snowflake is the most popular (and widely regarded as the most usable) data warehouse tool available. With nearly a 20% share of the entire data warehousing market, this cloud data warehousing solution is the market leader by quite a wide margin.
For data professionals, it’s easy to set up, maintain, and scale, making it ideal for businesses that need an enterprise data solution. With its cloud-based architecture, Snowflake is also highly reliable, flexible, and secure. In this guide, we’ll simplify how Snowflake works so you can get the best out of it.
How does Snowflake work?
The core of how Snowflake works is its unique architecture. By separating storage, compute, and cloud services, Snowflake can optimize cost and performance while supporting data modernization. We can define Snowflake by three core characteristics:
- Elastic scalability
- Automated resource allocation
- Native support for semi-structured data
Snowflake has redefined the landscape of data warehousing with its ability to dynamically scale resources. This feature, known as “elastic scalability,” addresses both the challenges of unpredictable data demands and the efficient utilization of resources. That’s what makes Snowflake pricing so attractive. Since you only pay for the resources you use, it is very easy to scale those resources up or down as needed.
Automated scale up/down compute resources
With Snowflake, businesses aren’t confined to a fixed set of resources. Instead, the platform can automatically scale up or down its compute clusters based on the workload, whether it’s for batch processing, real-time analytics, or intricate data operations. This adaptability ensures optimal performance without manual intervention.
Concurrency without contention
Imagine different departments in a company wanting to execute diverse queries on the same data simultaneously. In traditional systems, this would lead to resource contention, slowing down the process for everyone involved.
Snowflake’s solution is separate virtual warehouses for distinct teams or workloads. Each virtual warehouse operates independently, ensuring that various tasks can be performed concurrently without any interference or slowdown.
Automatic cluster duplication for high demand
Snowflake’s ingenuity doesn’t stop at creating separate virtual warehouses. When a particular compute cluster is overwhelmed with queries, Snowflake instantaneously initiates another cluster, distributing the workload between them. This dynamic response ensures consistent high performance and virtually eliminates the possibility of downtime due to excessive demand.
Eliminating capacity planning and overspending
Historically, data teams had to meticulously plan for peak demand, often resulting in over-provisioning and underutilization. With Snowflake, the need for cumbersome planning is obsolete. The system scales based on actual demand, ensuring Snowflake users neither overpay for dormant resources nor suffer from a lack of them during high-demand periods.
Adaptive to diverse workloads
From real-time analytics to complex data pipelines, Snowflake’s elasticity is adaptive to various workloads. Whether it’s understanding user engagement or deriving insights about customer acquisition, the platform ensures that every query gets the computational power it deserves.
Automated resource allocation
Snowflake’s architecture stands out primarily for its ability to automate resource allocation. It optimizes workload performance without requiring manual management. Let’s break down how this works.
Unlike traditional systems that intertwine storage, computation, and service functionalities, Snowflake distinctly separates these components. This is what paves the way for flexible, dynamic, and autonomous resource management.
At its foundation, Snowflake employs a cloud storage service that promises scalability, high availability, and robust data replication. This storage capability provides fault tolerance and allows users to structure data in custom databases.
Massively parallel processing (MPP) clusters allocate the necessary resources for various tasks, from data ingestion and transformation to querying. What sets Snowflake apart is its concept of virtual warehouses. Users can create these warehouses to isolate specific workloads, ensuring dedicated computational power. Moreover, these virtual warehouses can be granted selective access to the storage databases, enhancing data security and query efficiency.
Cloud services layer
Beyond storage and computation, Snowflake encompasses a suite of cloud services handling metadata management, security protocols, access control, and infrastructure oversight. These services seamlessly interact with various client interfaces, be it Snowflake’s web UI or third-party JDBC/ODBC connections.
Dynamic resource scalability
Snowflake’s architecture isn’t just about separating functionalities. Its true strength lies in the ability to independently scale these components based on real-time demand. Whether it’s ramping up storage for burgeoning datasets or amplifying computational power during heavy query loads, Snowflake autonomously scales resources.
Unified data handling
Snowflake’s versatile system removes the need for specialized databases for different data types. Its architecture, combined with automated resource management, ensures all data formats are catered to within a singular platform.
User-friendly resource adaptation
Gone are the days when data teams had to manually juggle resource allocations. Snowflake’s intelligence automatically adjusts resources according to the scenario at hand, removing the hassle of manual management and the risk of resource misallocation.
Native support for semi-structured data
Traditional relational databases are rooted in a rigid schema-based structure. While this brings benefits like efficient indexing and data pruning, it struggles when confronted with data that doesn’t adhere to a predefined schema. The dynamic nature of semi-structured data, often outputted by modern tools and applications, challenges the fixed-column foundation of conventional databases.
Forced conformity and its drawbacks
Historically, to make semi-structured data compatible with these databases, data teams resorted to ‘force-fitting’ the data into schemas. This could mean loss of crucial information, reduced flexibility, and potential disruption to existing data pipelines when new fields are introduced.
Before Snowflake, some systems tried to accommodate by treating semi-structured data as unique complex objects, but this too was limited in its searching, indexing, and performance inefficiencies.
Snowflake’s VARIANT data type
Snowflake’s solution to this quandary is the VARIANT data type. This flexible data type empowers users to store semi-structured data in its native form within a relational table framework.
Whether it’s JSON, Avro, XML, or Parquet, VARIANT can ingest and store it without predefined schema. Users can load data directly without data loss or adverse impacts on performance. Snowflake’s support for semi-structured formats extends beyond ingestion. You can query directly on these formats, without requiring transformation or ETL.
The best of both worlds
Snowflake understands semi-structured data, but it doesn’t compromise the advantages structured data systems offer. Users can still perform advanced analytics, employ powerful query mechanisms, and ensure data security, all while benefiting from the flexibility and comprehensiveness of semi-structured data storage.
Snowflake presents a unified solution for handling disparate data types and workloads. For data professionals, it’s a platform that’s scalable, secure, and user-friendly. That’s why it’s the go-to choice for data teams that need a modernized approach to analytics and data management.