Synapse vs Snowflake: Technical Comparison for Cloud Data Warehouses

In today's data-driven landscape, the decisions surrounding data management and infrastructure are more crucial than ever. With a plethora of platforms at their disposal, professionals tasked with these responsibilities face a complex array of options. Two standout choices in this realm are Snowflake and Azure Synapse, each with its unique approach to data warehousing and analytics.

This blog post is dedicated to those navigating these critical decisions, offering a deep dive into the technical nuances of Azure Synapse vs. Snowflake. We will explore:

• the intricacies of their deployment strategies
• the architecture of their storage layers
• data loading/unloading
• the complexities of compute models, including virtual warehouses, dedicated pools, and serverless options
  

Our goal is to illuminate the key distinctions and factors that should guide your choice between these powerful platforms, tailored to your specific technical requirements. Whether you're an architect designing the next-generation data platform or a strategist planning your organization's data future, this exploration will equip you with the insights needed to make informed decisions in the evolving landscape of data management and infrastructure.

Deployment Function 

The term "deployment function" refers to the methods and processes used to configure, launch, and manage database services within these cloud data warehouses. Specifically, it encompasses how resources are allocated, how data warehousing tasks are automated, and how scalability and maintenance operations are handled. Deployment function is crucial in understanding how each platform can be tailored to meet specific business needs, including ease of use, flexibility, and cost efficiency.

Snowflake has three key components: the storage layer, the compute layer, and the cloud services layer. The storage layer is intricately built upon cloud object storage within the designated cloud provider account, such as AWS S3, Azure Blob/ADLS, or Google Cloud Storage. On the other hand, the compute layer boasts serverless "virtual warehouses" that dynamically come to life during query execution, offering the flexibility to scale both vertically and horizontally to suit varying compute requirements. And overseeing it all is the cloud services layer, delivering a seamless SaaS experience that takes charge of administrative tasks, security/governance measures, and metadata functions within the Snowflake platform.

Azure Synapse offers two distinct compute functions in the form of pools. The Dedicated Pool provides a specific level of compute power for data warehousing tasks, while the Serverless Pool offers a flexible ad-hoc solution for querying data in various Microsoft storage options. These pools have individual pricing structures, which will be detailed in a later section. The Dedicated Pool utilizes Azure Storage in a columnar format under the hood, with more information on storage to follow in later sections. Within the Azure Synapse Workspace, you'll find the service layer components responsible for administering, governing, and provisioning Synapse artifacts.

Key Takeaway: Snowflake and Azure Synapse are structured very similarly with the primary difference being Azure Synapse having a dedicated option that is not dynamically provisioned like a Snowflake virtual warehouse. 

Storage Layer 

The storage layers in both Snowflake and Synapse are designed on top of columnar data, enhancing processing speed for OLAP use cases. Managed fully by the vendor, these layers prevent direct access to underlying files by users, who instead interact with data as relational tables, akin to a traditional RDBMS system, through the cloud services layer.

In Snowflake, data is loaded into the storage layer via a "stage", an object storage area managed by Snowflake (Internal) or the customer (External), where files are "copied" into Snowflake tables. Utilizing the staging system behind the scenes, Snowflake's batch-based loading tools make use of this mechanism. Additionally, Snowflake offers Snowpipe for streaming new data from object storage as it becomes available.

In Synapse Dedicated Pools, data can be loaded into the storage layer by staging data in Azure Blob/Data Lake storage using Polybase or utilizing the "Copy Data" tool in the Azure Synapse Studio. This tool leverages Azure Data Factory functionality to load data through a user interface from various Azure databases, applications, and systems. Data can also be streamed into Dedicated Pools using Azure Streaming Analytics.

Both platforms allow the extension of the storage layer to external locations, primarily data lake files. Snowflake enables the creation of "external tables" on files or directories in S3, ADLS, and GCS for querying. Synapse, on the other hand, leverages its Serverless Pool for querying data in ADLS, CosmosDB, and Dataverse. While data in these external locations may not provide the same query speed and performance as the primary storage layer, they are valuable for data profiling and ad-hoc analysis before full ingestion.

Both technologies offer the ability to unload data to object storage/stages in various formats (Parquet, CSV, JSON) and compression patterns (e.g., gzip). Unloading data has several advantages, including populating data lakes, creating data extracts, and establishing tiering patterns for historical data.

Furthermore, both Snowflake and Synapse support the creation of "zero copy clones" of tables, enabling cost-effective storage for historical snapshots, development, testing, and recovery scenarios.

Key Takeaway: Both platforms share similar functionalities, with Azure Synapse standing out for its user-friendly GUI that simplifies data loading from Azure environments. This slight edge in integrating data integration within the same UI as the data warehouse component enhances the overall user experience. 

Compute 

Snowflake's "virtual warehouses," also known simply as warehouses, serve as clusters of computing resources that supply the necessary CPU, memory, and temporary storage for executing operations. These warehouses are defined by specifications that dictate their size and behavior. When it comes to sizing, warehouses can scale vertically from X-Small to 6X-Large and horizontally from 1 to 10 clusters. Warehouses can be configured to run continuously or set to auto-resume upon query execution and auto-suspend after a designated period of inactivity. The auto-resume feature is typically instantaneous, leading to rapid provisioning times. Users have the flexibility to create as many virtual warehouses as needed and can strategize by assigning different warehouses to various use cases, tools, users, and groups to optimize workloads effectively. 

On the other hand, Synapse's Dedicated Pools function as clusters of computing resources that deliver the necessary CPU, memory, and temporary storage for executing operations. Unlike Snowflake, the options are more limited, with the ability to scale the "Data Warehouse Units (DWU)" unidirectionally from 100 to 30,000. Each increase in DWU signifies a doubling of performance and compute costs. Dedicated pools need to be manually started or scheduled to start and stop, as there is no automatic option available. 

In contrast, Synapse's Serverless Pools operate differently in that they can be initiated at any time without requiring a Dedicated Pool. There is no customization or tuning of the Serverless Pool, as all optimization occurs behind the scenes. 

Key Takeaway: Snowflake emerges as the clear frontrunner, showcasing its dominance and serving as a core advantage that we must highlight. In contrast, Azure falls short in terms of performance control, flexibility limitations, and a more rigid cost structure.

Data Loading/Unloading

Snowflake excels in handling structured and semi-structured data, showcasing its prowess when dealing with formats like JSON. By storing semi-structured data in a VARIANT column, Snowflake enables the querying of individual elements and the transformation into structured data. Additionally, Snowflake offers support for unstructured data from object storage, allowing seamless integration with Snowpark or UDFs.

On the other hand, Synapse shines in querying, loading, and unloading structured data through its Dedicated SQL pools, Polybase, and Copy Data functionality. For semi-structured data, Synapse leverages its Copy Data/ADF tools, while the Serverless pool is used for querying such data. Unstructured data processing is facilitated through Spark notebooks, showcasing the versatility of Synapse in handling diverse data types.

Key Takeaway: Structured and unstructured data may seem similar on the surface, but it's the semi-structured data where Snowflake truly shines. This is a clear illustration of how Synapse requires specific tools for different tasks, whereas Snowflake's ability to query semi-structured data in its raw form, with just a few SQL lines before any parsing or flattening, has proven invaluable in numerous customer implementations.

Conclusion

The journey through the technical terrains of Snowflake and Synapse has illuminated key aspects that are vital for professionals steering the data management and infrastructure decisions within their organizations. Snowflake's standout feature is its unparalleled flexibility in deployment across various cloud providers, offering a significant advantage for organizations operating in multi-cloud environments or those seeking the freedom to switch or integrate multiple cloud services seamlessly.

Both platforms exhibit robust capabilities in managing the lifecycles of data through efficient loading and unloading mechanisms, including options for leveraging external storage solutions. This flexibility ensures that organizations can tailor their data management practices to suit their specific needs, optimizing for performance, cost, or a balance of both.

Furthermore, the support for zero-copy clones by both Snowflake and Synapse underscores their advanced data management capabilities, allowing for efficient historical snapshots and intricate data management tasks without the overhead of duplicating data. This feature is a boon for organizations looking to implement complex data workflows, such as testing, development, and detailed analysis, with minimal additional cost and resource allocation.

In making a choice between Azure Synapse vs. Snowflake, the decision hinges on specific organizational needs and technical requirements. Snowflake's deployment flexibility might be the deciding factor for some, while others might prioritize the specific data loading, unloading, and management features that align best with their operational workflows. In either case, both platforms offer a robust set of tools designed to empower organizations in their quest to harness the full potential of their data, driving insights, innovation, and business value in an increasingly data-dominated world.