As a data consultant, I have the unique opportunity to use a variety of tools to manage and analyze data assets dependent on the project or client. Sometimes organizations are already using tools that make sense for them, other times the current stack is outdated and struggling to keep up with growth and scaling modern business.
There is a constant search for efficient, scalable, and secure platforms to support businesses’ data management strategy. Snowflake has emerged as a leading solution, offering a cloud-native data platform that addresses the changing needs of modern businesses.
This article explores why organizations are choosing Snowflake, focusing on its architecture, scalability, ease of use, and cost efficiency.
Cloud-Native Architecture
Separation of Storage and Compute
Snowflake’s cloud-native architecture separates storage and compute. This architecture allows businesses to independently scale their storage and compute resources based on their specific needs. An organization can increase storage capacity without affecting compute, and vice versa. The flexibility ensures that companies can optimize both performance and cost.
This differs from the traditional on-premises data warehouse model, older relational database systems like Oracle, IBM DB2, or Microsoft SQL Server before they were adapted for cloud environments. In a traditional model, storage and compute resources are tightly coupled within the same physical infrastructure. In these systems the storage (disk space) and compute (CPU, memory resources) are part of the same system, typically a server or a cluster of servers, which can lead to limitations and a lack of flexibility (i.e., if you need more compute, you’ll likely need to add storage, because the two are coupled).
Elasticity
Snowflake’s architecture is inherently elastic – it can automatically scale up or down based on demand. During peak usage periods, Snowflake can dynamically allocate more resources to maintain performance. Similarly, during off-peak times, Snowflake can scale down to save costs. The decoupled storage and compute allow you to easily have more of one without the other.
Persistence
Snowflake’s Virtual Warehouses (VWs, i.e. compute clusters) are lightweight and exist in a suspended state, so new physical resources do not need to be provisioned and configured each time a cluster is asked to start up. This differs from other alternatives, such as Databricks, which runs on a collection of Virtual Machines (VMs) that take time to spin up and initialize Spark. Since Snowflake’s VWs can be suspended and resumed, their state can be preserved, resulting in quicker start up times that allow for near instantaneous querying.
Scalability and Performance
Massive Parallel Processing (MPP)
Snowflake leverages Massive Parallel Processing (MPP) to handle large volumes of data efficiently. MPP enables the platform to distribute tasks across multiple nodes, allowing for faster processing times and improved performance.
If your organization is dealing with big data – customer transactions, website interactions, supply chain logistics, etc. – MPP is necessary. If the data is coming from multiple locations, ERPs, or CRMs, even more so.
Concurrency Handling
Snowflake also addresses the challenge of managing multiple users or workloads acting simultaneously with robust concurrency handling. It allows multiple users to run queries and access data without any performance degradation.
Consider an example, where the marketing team needs to run queries to identify which promotions are most effective in driving sales, while the finance team needs to calculate real-time revenue, and the operations team needs to continuously monitor stock levels to avoid any inventory shortages.
Concurrency processing is essential when multiple teams or users need to access and analyze data simultaneously.
MPP and concurrent processing radicate many of the issues often experienced by traditional on-premises data warehouse architecture; such as fixed resource allocation, effort or downtime required for scaling, resource wastage, operational complexity, and performance bottlenecks.
Ease of Use and Accessibility
User-Friendly Interface and Low Barrier to Entry
Snowflake’s user-friendly interface is designed to cater to users of varying technical expertise. Whether you’re a data engineer, a business analyst, or in a more non-technical role, Snowflake’s intuitive UI makes it easy to access and manage data. Snowflake is committed to continuously improving their user experience as well, noted by the new(er) Snowsight UI and (what I, personally was waiting for) the release of Dark Mode.
Teams can quickly start analyzing data and generating insights without the steep learning curve often associated with traditional data platforms. Even other modern data platforms like Google BigQuery and Databricks, both serverless and highly scalable, require a higher understanding of SQL and how to optimize queries to manage costs and performance before being able to use them effectively. While Apache Hadoop and Spark (and often Databricks, which has more transparency to its Spark engine under the hood), require a deep understanding of distributed systems. These platforms are powerful and capable, but their complexity can be a barrier to entry for users who do not have a strong background in data engineering or database management.
SQL-Based Querying
Snowflake supports standard SQL, allowing data professionals to use their existing skills without needing to learn a new query language. This ease of use accelerates the adoption of Snowflake within organizations and enables faster time-to-insight.
Snowflake also offers some really neat enhancements that I use often in my day to day data engineering tasks. These include Time Travel which allows users to access historical data as it existed at any point within a defined retention period, Zero-Copy Cloning which consumes no additional storage until changes are made, Materialized Views optimized for minimum overhead, External Functions for APIs or services that can be accessed within a SQL workflow, handling of semi-structured data with the Variant Datatype, and many others which I will discuss in further detail in a future article.
Snowpark for Python, Java, and Scala
Snowpark expands Snowflake’s versatility, making it an even more powerful platform for modern data teams. By enabling Python, Java, and/or Scala-based data processing and analysis directly within the Snowflake environment, organizations can break down silos between data engineering and data science, streamline workflows even further.
In my experience, the accessibility offered by Snowflake is second to none. The ease of use ensures that organizations can democratize data usage, allowing more team members to leverage data insights for decision-making.
Cost Efficiency
Pay-as-You-Go Pricing
Snowflake’s pay-as-you-go pricing model is designed to offer cost efficiency to organizations of all sizes. Instead of paying for a fixed number of resources, businesses only pay for what they use. The consumption model is advantageous for organizations with varying workloads, allowing for the scaling of resources up or down based on actual demand, ensuring that they are not paying for unused capacity.
No Infrastructure Maintenance
Snowflake is a fully managed service, eliminating the need for organizations to maintain hardware, manage updates, or perform system administration tasks. This reduces operational overhead and allows businesses to focus on deriving value from their data rather than managing infrastructure. The combination of reduced operational costs and flexible pricing makes Snowflake a cost-effective solution for many organizations.
Conclusion
Snowflake’s cloud-native architecture, scalability, ease of use, and cost efficiency make it an attractive choice for organizations looking to modernize their data infrastructure. By leveraging Snowflake, businesses can achieve optimized performance, reduced costs, while offering improved agility, and democratization of their data access across business units.
If your organization is looking for support in implementing a modern data management strategy, and/or scaling with Snowflake which stands out as a compelling option, reach out to our team at Datalere. We offer expert consulting services in designing and developing custom data solutions tailored to your needs.
