What Is Snowflake?

Snowflake is a cloud-based data warehousing solution offering a single data storage, processing, and analysis platform. It was founded in 2012 in San Mateo, California.  Snowflake’s founders wanted to build a data platform that would harness the immense power of the cloud.  Snowflake enables organizations to efficiently manage and analyze large volumes of structured and semi-structured data through a scalable and fully managed platform.  The platform supports the ETL process of 

• extracting data from various sources, 

• transforming the data for analysis, and 

• loading it into the data warehouse.

In this blog, we’ll introduce Snowflake, explore its architecture, key features and benefits, and highlight challenges to consider, particularly the cost of cloud computing, (with pointers on how to address them). Let’s dive in!

What is Snowflake’s architecture?

Snowflake’s architecture is designed to leverage the power of the cloud, offering unique flexibility and scalability for modern data needs. Unlike traditional data warehouses that tightly couple storage and compute resources, Snowflake’s decoupled architecture allows for independent scaling of these components, optimizing both performance and cost. This design is one of the core reasons Snowflake has become a popular choice for businesses seeking to handle large-scale data operations efficiently.

Snowflake has a decoupled architecture with three key, decoupled layers:

Data storage layer:  Snowflake stores data in an optimized, compressed, and columnar format in cloud storage. This separation of storage ensures that data is highly available, secure, and easily scalable. In other words, this enables users to scale storage without affecting the compute layer.

Query processing layer: Snowflake processes queries using ‘virtual warehouses’, which are clusters of compute resources.  These warehouses are built using a Massively Parallel Processing (MPP) compute cluster composed of multiple compute nodes; thus, there is no sharing of compute resources among the virtual warehouses. This means users can run multiple workloads concurrently without the risk of resource contention, ensuring consistent performance across different queries.

Cloud services layer:  This describes a collection of services that manage activities across Snowflake, including authentication, infrastructure management, metadata management, and query optimization.  These services work collaboratively to process all types of user requests from login to query processing.  The cloud services run on separate compute nodes provisioned by Snowflake from the cloud provider.

The decoupled nature of Snowflake’s architecture offers businesses valuable flexibility. By allowing storage and compute to scale independently, organizations can optimize their cloud usage, paying only for the resources they need. Additionally, this structure supports scalability, making it an excellent fit for organizations of all sizes.

Snowflake’s unique, decoupled architecture is a key differentiator, enabling businesses to process data more efficiently and cost-effectively as their data estates expand.

What are the key features and benefits of Snowflake?

Snowflake’s cloud-native design makes it a robust platform for modern data management. Here are some of Snowflake’s most notable features:

Scalable: Snowflake can scale up or scale down depending on the evolving data needs with great agility. As data volumes fluctuate, Snowflake’s architecture allows businesses to dynamically adjust compute power and storage independently. This ensures cost-effective resource management and makes it easier for companies to adapt to growing data needs without disrupting operations.

Cloud-based: Snowflake is cloud-based, which means there is no big cost or effort to set it up. You also don’t need to pay for expensive hardware, while compute and storage can be optimized based on needs and cost targets.

Secure data management: Snowflake handles both structured and semi-structured data in a very secure and efficient manner.  Data encryption, network security, access control, multi-factor authentication, and compliance with industry standards such as GDPR and HIPAA make Snowflake a trusted solution for businesses handling sensitive information. 

Improved collaboration: Snowflake provides excellent data-sharing capabilities while keeping the data isolated and secure.  Snowflake provides extensive connectivity with a broad ecosystem of third-party partners and technologies, and provides an extensible framework for creating applications to share data content and application logic among Snowflake accounts, promoting interoperability between partners while maintaining data isolation and security.

Replication and failover: Business continuity is a critical concern for any organization, and Snowflake addresses this with its built-in replication and failover capabilities. Snowflake supports replicating data across multiple accounts, regions, and cloud platforms, ensuring that in the event of a failure, organizations can continue accessing their data with minimal disruption. This multi-region support enhances data resilience and provides peace of mind in disaster recovery scenarios.

Snowflake’s key features—scalability, secure data management, enhanced collaboration, and replication—make it a robust platform for organizations looking to modernize their data infrastructure. By leveraging these capabilities, businesses can not only scale operations efficiently but also ensure security, foster collaboration, and maintain high availability, all while keeping costs in check.

What are the main challenges of managing Snowflake data?

Managing data within Snowflake offers numerous benefits, but it also comes with its own set of challenges. As organizations scale their data infrastructure, these challenges can quickly affect performance, data governance, and cost management. Understanding these hurdles is key to optimizing the value of Snowflake while controlling costs.

Lack of visibility into data consumption

A common challenge for businesses using Snowflake is the lack of detailed visibility into how resources are being consumed. Without proper monitoring, organizations can over-allocate compute resources or store unnecessary data, driving up costs. 

To mitigate this, businesses need comprehensive dashboards to track real-time consumption. This visibility ensures that resources are used efficiently, helping to optimize costs.

Unused or underutilized data silos

Data silos—where information is isolated in separate systems—can persist after migrating to Snowflake, leading to inefficient data usage and inflated storage costs. Without tools to identify and decommission outdated silos, businesses risk maintaining redundant data. 

By leveraging the right data governance tools, organizations can manage Snowflake data more effectively, retiring silos and streamlining cloud operations, ultimately reducing costs.

Managing query performance

Snowflake’s scalability allows for flexible compute resources, but poorly optimized queries can lead to resource inefficiencies and higher expenses. Slow-running queries not only impact performance but also drive up compute costs unnecessarily. 

Businesses need to continuously monitor and refine their queries to ensure optimal performance, which is key to managing both efficiency and cost in a Snowflake environment. Tools that provide insights into query performance can significantly help businesses reduce operational expenses.

Ensuring compliance and data governance

As organizations grow, ensuring compliance with data governance standards becomes more complex. Businesses using Snowflake must implement strong data governance frameworks to protect sensitive information and adhere to regulatory requirements. 

Implementing a data catalog that integrates with Snowflake can help streamline governance processes by providing a clear view of data usage and ownership across the organization. This also aligns with cost optimization, as good governance helps prevent the misuse of resources and data, contributing to more efficient and controlled Snowflake environments.

Effectively managing Snowflake data requires overcoming challenges like visibility, unused data silos, query performance, and governance. Addressing these challenges not only improves operational efficiency but also plays a critical role in controlling costs. By investing in the right tools and strategies, organizations can maximize and optimize the value of their Snowflake data, leading to a more agile and efficient data infrastructure.

What are common use cases of Snowflake? 

Snowflake's versatility as a cloud-based data platform opens up a wide array of use cases for organizations looking to maximize the value of their data. Here are some of the most common use cases, highlighting how Snowflake, when combined with data governance tools like Alation, can drive data intelligence, operational efficiency, and innovation.

Defining data use policies and roles

One critical use case for Snowflake is establishing and enforcing data governance policies. By integrating Snowflake with a data catalog like Alation, organizations can ensure that data policies around privacy, security, and access are automatically applied across all data sources. 

For example, Domain Group leveraged Alation’s Data Intelligence Platform to govern nearly 100 million data points daily within their Snowflake environment. Alation’s ease of use and innovative features allowed Domain to ensure compliance with data privacy regulations while empowering teams to understand and access the data they need.

Migrating the right data to the cloud

When moving to a cloud platform like Snowflake, it's essential to migrate only the necessary and valuable data to avoid overwhelming the system and driving up costs. Companies like Discover Financial Services, use Snowflake to manage a migration of hundreds of petabytes of data efficiently. By integrating Snowflake with Alation, Discover automated metadata-driven processes that helped identify high-quality datasets, significantly cutting down pipeline creation time from 30 days to just two days.

Boosting analyst and developer productivity

Snowflake’s architecture allows for rapid scaling and parallel processing, which can dramatically boost productivity for both analysts and developers. Discover Financial Services, for instance, has seen significant gains in analyst efficiency by integrating Snowflake with Alation. With Alation acting as a "single pane of glass" for metadata, over 2,500 users can now find, use, and enrich metadata in Snowflake within minutes, saving the organization 200,000 hours. This boost in productivity has freed up time for innovation, allowing teams to develop and deploy new financial models more quickly.

Promoting self-service analytics

Empowering teams with self-service analytics is another powerful use case for Snowflake. By enabling analysts, data scientists, and business users to access and analyze data independently, organizations can accelerate decision-making and foster a culture of data-driven insights. Alation, integrated with Snowflake, enhances this use case by providing a comprehensive data catalog that makes data discovery faster and more intuitive. As demonstrated by Discover, their team can now access critical Snowflake data in minutes, promoting trust and transparency while enabling quicker insights.

These use cases showcase how Snowflake’s flexible architecture, combined with the right data governance tools, can transform how businesses use, manage, and scale their data. Whether it's enforcing data policies, optimizing cloud migration, enhancing productivity, or promoting self-service analytics, Snowflake empowers organizations to unlock the full potential of their data.

How can businesses optimize Snowflake costs?

As more enterprises migrate to cloud-based platforms like Snowflake, optimizing costs becomes a critical factor. While Snowflake offers immense scalability and flexibility, without proper cost management, IT leaders can find themselves exceeding budgets. To address this, businesses need to adopt strategies and tools that enhance visibility, improve resource allocation, and ultimately lower operational expenses.

Leverage consumption dashboards for visibility

One of the primary reasons cloud migrations fail or go over budget is the lack of visibility into data consumption and usage patterns. In fact, according to Gartner, more than 50% of data migrations exceed their budget. By using consumption tracking tools, businesses can track Snowflake’s resource usage in real-time through ready-to-use dashboards. This transparency enables IT leaders to identify underused resources, optimize query performance, and make more informed decisions about scaling.

Optimize workloads post-migration

After a successful migration to Snowflake, the next step is to fine-tune workloads. During the initial migration, it’s common for legacy data silos to remain, which can increase storage and compute costs. Snowflake’s elasticity allows businesses to scale resources up or down depending on demand, and with the right tools, organizations can decommission these silos more quickly. This reduces unnecessary costs and ensures that only the most relevant data is being processed in Snowflake, improving cost efficiency.

Decommission legacy data silos

The faster businesses can eliminate old, redundant data silos, the quicker they can realize cost savings. Consumption tracking tools like Peak Performance assist leaders in identifying which datasets and resources are critical and which can be safely retired. This approach accelerates cloud adoption while maintaining data governance policies, reducing both storage costs and operational complexity.

Enhance query performance

Optimizing query performance is another way businesses can reduce Snowflake costs. Inefficient queries can drain resources and lead to higher compute costs. By using monitoring tools to analyze query execution and adjust computing resources dynamically, businesses can reduce execution times and ensure resources are used optimally. This not only cuts costs but also boosts the overall performance of the Snowflake environment, ensuring that critical queries, such as those for real-time analytics, run smoothly.

Right-size cloud resources

Snowflake’s ability to scale resources on demand is one of its biggest strengths. However, it’s crucial for businesses to continuously assess and adjust their resource usage. By right-sizing cloud resources—scaling up or down based on real-time needs—organizations can avoid paying for excess capacity. With detailed consumption metrics in hand, data teams can pinpoint where resources are being underutilized and make adjustments accordingly, ensuring cost efficiency without sacrificing performance.

Optimizing the costs of Snowflake is all about visibility, resource management, and continuous optimization. By leveraging tools like Alation’s Peak Performance, businesses gain insights into their data consumption, allowing them to decommission legacy systems, improve query performance, and right-size their cloud resources. As a result, organizations can maximize their Snowflake investment, ensuring scalability and agility while keeping costs under control.

Integration of Alation with Snowflake

Alation Data Intelligence Platform supports a deep integration with Snowflake offering the following features:

• Metadata Extraction (MDE) & Query Logs Ingestion from Snowflake, inclusive of basic, key-pair, and agent-based authentication, as well as query-based MDE using customer queries, enabling users to see the popularity of a given data object. 

• Sampling and profiling of tables and columns

• Table and column-level lineage

• Extraction of Snowflake policies

• Snowflake tags synchronization

• SQL querying support from Alation

By integration the Alation platform, Snowflake users can gain a better understanding of their Snowflake data and thus maximize the value of their Data Cloud.

Curious to learn more about how Alation can up-level the data management of your Snowflake data? Book a demo with us today.