Last Updated on January 4, 2023 by Ashish
Snowflake vs Synapse, in the blog, we will try to understand the differences between both these services. At the pinnacle of technological innovation, most businesses are migrating to the cloud to store vital data and information. Cloud storage preserves critical advantages such as easy data access, lower storage costs, and simple handling and maintenance. Companies have accepted the concept of cloud storage for their organizations due to the aforementioned benefits.
Currently, two platforms dominate the entire cloud storage facilities industry, namely Azure Synapse and Snowflake. These two ETL solutions are now among the finest in terms of premium cloud-based data storage alternatives. However, because there are so many cloud services on the market, businesses are unsure which one is best for their needs.
Synapse and Snowflake are the two most recommended ETL platforms for storing and processing massive amounts of data. These two cloud platforms may also distribute large amounts of data over multiple nodes utilizing a parallel processing technique. Among the numerous advantages provided by these two cloud platforms, firms must choose the best one for their needs.
There are certainly major differences between these two ETL platforms that set them apart. These distinguishing qualities highlight their unique computational strengths. Companies should select the best one for their specific needs. In this post, we’ll examine some of the key distinctions, benefits, and drawbacks of these two ETL platforms.
What is Azure Synapse?
Microsoft Azure Synapse is a cloud-based data warehouse service. Azure Synapse was once known as the Azure SQL data warehouse. Synapse is part of the Azure family, which also includes Databricks, Power BI, and Cosmos DB.
Azure Synapse is a platform that combines big data analytics and data storage into a single platform to improve user experience with a single burden. Advanced machine learning and business intelligence technologies fuel Synapse. Synapse manages data processing, data exploration, data management, and data communication efficiently in accordance with ML and BI requirements. The following are some of Azure Synapses’ important characteristics.
- End-to-end encryption
- Parallel processing technique
- In-built tools for governing data
- Excellent integration, configuration and compatibility with other Microsoft Azure products
What is Snowflake?
Snowflake is yet another cloud-based platform built on a fully controllable PaaS architecture that aims to improve data science and data warehousing capabilities. To maximize data safety and security, Snowflake is connected with both SaaS and PaaS applications. Snowflake is not like other data warehouse systems, such as Amazon Redshift. Snowflake was designed primarily with BI automation in mind.
Snowflake is highly scalable and secures real-time data sharing. Snowflake also aids with data storage management by storing structured and unstructured data on-premises utilizing the Azure data lake architecture. Snowflake works well with Microsoft Azure and AWS cloud-based frameworks. Snowflake is based on these two infrastructures and can be significantly customized with AWS and Azure.
Server cloud Snowflake uses several data protection control security systems to highly secure storage resources. Snowflake uses security tools like S3 policy control and SAS tokens powered by Amazon, and Google permissions to access data storage, SSO, etc. Workload segmentation and intelligence automation are also built into the Snowflake cloud storage system to scale data storage as per the business requirements. Here are some of the key features of Snowflake’s cloud data warehouse.
- Sharing of data
- Cloning of data
- Scalability of computation
- High interoperability with third-party software like AWS and Microsoft Azure
Similarities between Azure and Synapse
Although each ETL platform provides certain unique features, there are some aspects that are shared by both. Both Azure and Synapse act as data warehouses. Both platforms aid in the storage and administration of data. In this part, we shall compare the services provided by these two ETL tools.
- Both systems provide a wide range of storage and computing functions. These two platforms separate storage and computing by distinct levels of abstraction.
- Scaling, restarting, and halting computing services are all possible with Azure and Synapse.
- Synapse and Snowflake both support the ANSI and API structural query languages (SQL).
- They can both handle on-premise, structured and unstructured data.
- Data virtualization is conceivable in both scenarios. To drop the files and put the query, format specifications such as JSON and CSV are created.
- Data lake management systems are supported by both Synapse and Snowflake. In the case of Snowflake, however, further file manifestation is required.
As previously stated, there are many similarities between Synapse and Snowflake. However, in order to select the best one for your needs, you must first understand the differences between these two ETLs. The fundamental distinctions allow customers to sort out the strengths and drawbacks of these two cloud platforms, which aids in the selection of the best one for operations. Here are some key distinctions between Synapse and Snowflake cloud data warehouses.
SaaS vs PaaS
One of the most significant distinctions between Azure Synapse and Snowflake is the selling option for these two cloud platforms. Synapse is built on PaaS infrastructure that is integrated with the Microsoft Azure workspace ecosystem. As a result, because the workplace environment is made by the same company, compatibility concerns never arise in the case of Azure Synapse. Furthermore, money is necessary just for the Azure installation system.
Snowflake, on the other hand, is a Software as a Service (SaaS) driven framework that is based on the Azure, Amazon, or Google cloud structures. Using an abstraction layer, Snowflake separates storage and computes credits. In the case of Snowflake, payment for actual cloud storage and underlying cloud computing services is required separately.
Synapse and Snowflake both have their own method of data processing and resource computation strategies. Azure Synapse builds a long-term SQL database center in order to provide the best data warehousing facility. A SQL pool system, on the other hand, is incorporated specifically to serve the stated objective.
Although Snowflake aids in the creation of data warehouse SQL databases, the computing resources are separate from the SQL databases. Snowflake allows you to load and query a computational warehouse on any SQL database.
It is true, adopting the concept of a cloud storage data warehouse is more expensive than a standard on-premise data center. However, cloud service providers consistently strive to give the finest and most flexible pricing models possible. Vendors choose multiple levels of subscription pricing models, such as hourly, monthly, and yearly, and businesses can adopt the most appropriate plan based on their needs. Cloud vendors also provide cheaper packs so that small businesses may afford them, while large corporations and global corporations can choose the most expensive option.
Azure Synapse uses an hourly price model. For example, if the data warehouse is only operational for 10 hours each month, payment is only paid for those 10 hours. However, if the data warehouse is activated for less than an hour, say 45 minutes or 30 minutes, payment should be made for only one hour. As a result, the minimum time base restriction for Azure Synapse is one hour.
In the instance of Snowflake, it provides a per-second charging approach, where users must pay for the seconds they use the service. Snowflake provides auto-resume and auto-suspend functionalities, which allow users to suspend the virtual warehouse after running a query. For example, if a query takes 5 minutes to execute and the data warehouse is paused, payment is only required for those 5 minutes. However, the minimum time base limit is 60 seconds, which implies you must pay for at least 60 seconds even if the query execution time is less than that.
Serverless and dedicated pools are the two scalable SQL alternatives available in Azure Synapse. Serverless SQL allows for automation and automatically meets scaling requirements. Dedicated SQL, on the other hand, is integrated with the data warehouse unit system to pre-define a scaling unit.
Snowflake, on the other hand, is more scalable than Synapse. Snowflake significantly reduces computational downtime while also providing infinite growth of virtual data warehouses. Snowflake is based on a data-sharing and multi-cluster architectural architecture that allows workloads to be separated in a data-sharing layer.
Administration and management
Azure Synapse requires more upkeep and a proper management system. The performance must be tuned and monitored by a team of administrators.
Snowflake, on the other hand, takes little to no upkeep. Because Snowflake is built on SaaS architecture, it is easy to automate clustering and materialize maintenance. Snowflake can also optimize built-in performance. Companies do not need to hire full-time IT personnel to manage Snowflake due to its low upkeep.
One of the most significant advantages of Azure Synapse is that it is specifically intended to run on Microsoft Azure infrastructure. Synapse is simple to integrate and configure with the Microsoft Azure environment. As a result, Synapse is significantly more compatible with Azure services than Snowflake.
Snowflake does not have a built-in architecture. Snowflake is typically reliant on another cloud architecture. Microsoft Azure, AWS, and Google cloud are the key cloud data warehouse structures that enable Snowflake to run and operate. Snowflake uses an abstraction layer to separate cloud storage and pulls credits from third-party cloud storage-capable vendors.
Furthermore, Snowflake runs computing on separate clusters. Because resources are not shared among clusters in Snowflake, the performance of one warehouse is unaffected by the performance of another.
Data protection and security
To provide effective user management and permission control, Azure Synapse provides data encryption and role-based access control (RBAC). Synapse uses a multi-factor authentication technique to establish a secure connection via a VPN network.
Azure Synapse also includes a threat defense system to detect potential threats. Microsoft Defender monitors and safeguards stored resources against malicious and hacker activity. Furthermore, Azure Synapse is extremely compatible with other Microsoft products such as SOC, Azure private link, and so on.
Similarly, Snowflake incorporates nearly all of the data protection characteristics and functionality found in Azure Synapse. Snowflake also provides great data protection and security for the resources it stores. Because Snowflake is built on third-party infrastructures such as AWS, Azure, and Google, trust issues may develop. In addition to Azure private link, Snowflake offers AWS and Google private links.
In terms of data types supported, Snowflake and Synapse both support structured, non-structured, and semi-structured data. Synapse supports and may be configured with a variety of programming languages, including Java, Python, R, and SQL. Because of its integration with the Azure Data Lake developer tool, Synapse is also a viable alternative for unstructured data types.
Snowflake has recently launched Snowpark, a software development tool for dealing with unstructured data types. Snowpark supports a variety of programming languages, including Scala, Java, and Python.
Virtual Private Network (VPN)
It is tough to compare Synapse and Snowflake in terms of performance because each has some distinct significant advantages that set both solutions apart from one another. Snowflake, on the other hand, outperforms Synapse. Snowflake is a SaaS-based service, which allows it to outperform PaaS-based Synapse.
That is not the case; Synapse is a far lesser solution, but competing with Snowflake’s performance requires significant effort to optimize. In terms of total data infrastructure and data lake system, Synapse clearly outperforms Snowflake. However, in terms of data warehousing, Snowflake clearly wins.
In a nutshell, Snowflake is more adaptable than Azure Synapse. Although both were designed to make data storage and analysis easier to handle, Snowflake is better suited for typical small-scale data analytics and business intelligence. Snowflake provides automated clustering processes as well as data performance optimization tools. Snowflake’s functioning also has nearly no maintenance costs.
Snowflake also enables enterprises to run the data warehouse system without the need for full-time IT personnel. The Snowflake platform is so easy that any non-IT administrator can manage it.
Synapse, on the other hand, is designed by Microsoft, therefore it is easier to integrate and has superior compatibility with native Microsoft technologies such as Azure Delta Lake and Spark Pool. Overall, Synapse is the best solution for data streaming, AI, ML, and big data applications.
To properly operate the Synapse platform, a dedicated analytics team of IT specialists with in-depth knowledge and expertise is required. Synapse demands constant monitoring and attention to the service in order to get positive results. Synapse may also produce slower outcomes than Snowflake.
As previously stated throughout the paper, both Synapse and Snowflake provide significant benefits to the companies in a variety of ways. In this section, we will look into synapse and Snowflake separately, focusing on their specific benefits.
The following are some of the Snowflake data warehouse system’s strengths.
- Users can get a trial version of Snowflake. As a result, before investing large sums of money in data warehouse services, organizations can test the overall system’s functionality to see if it is advantageous to their operations.
- In general, Snowflake can resume faster than Azure Synapse.
- Snowflake is capable of analyzing XML data.
- Snowflake provides a better JSON experience than Synapse.
- Snowflake reduces costs by including a ‘Auto-Pause’ option for resource computation.
- Snowflake may exchange private data, allowing for front-end streamlining features.
The Azure Synapse data warehouse facility system has the following advantages.
- Synapse is more interoperable and easier to configure with the Microsoft Azure ecosystem than Snowflake.
- In Synapse, Azure DevOps provides CD/CI, whereas Snowflake requires a considerable amount of manual processes.
- The Azure Synapse connection feature aids in the establishment of an HTAP model.
- Synapse is equipped with an in-built business intelligence data analytics structure that enables quick data analysis and visualization.
- Azure Synapse allows the execution and execution of ad-hoc sparks and pipeline-based notebooks.
Whatever a cloud data warehouse’s merits are, restrictions are always a component of any technology. This section goes over some of the flaws of the Synapse and Snowflake cloud storage systems.
The following are the Snowflake data warehouse’s limitations.
- Snowflake is more expensive than competing cloud database storage platforms.
- Snowflake’s user interface may appear difficult to newcomers. It’s not very welcoming.
- Snowflake allows you to submit queries with a maximum size of 1MB.
- Snowflake does not provide on-premise data storage.
- Snowflake is not appropriate for online transaction processing.
- Snowflake is deprived of the advantages of a more tightly linked cloud environment.
Azure Synapse possesses the following weaknesses as listed below.
- To access and operate Synapse efficiently, you must have prior experience; otherwise, the learning process and time may be longer for inexperienced users.
- In Azure Synapse, separate compute virtual machines cannot be assigned to each group.
- Synchronization of discrete workload session management.
- Synapse does not support querying between databases.
- The setup of the Azure Synapse cloud database system is time-consuming.
- Synapse requires a lengthy data loading time, as well as halting and restarting.
- Users may have connectivity troubles when using Azure Synapse.
Use Cases of Snowflake and Synapse
This section examines the Snowflake and Synapse data warehouse use cases. A use case highlights the important features of any technology. In this section, we will organize, clarify, and identify the system requirements.
Among the countless use cases, two notable Snowflake applications have been identified and are shown below.
Transaction analysis for retailing
Companies in the retail industry must deal with a large number of resources including data and information. As a result, processing vast amounts of data is difficult for data analysis. Not only that, but the data must be updated on a regular basis. Furthermore, when a company expands, so does the volume of data. As a result, enough backup is also essential.
Backup issues for huge volumes of data can be resolved by building additional servers and expanding storage capacity, but these will persist in the future as resource usage increases. Snowflake is developed with all of these considerations in mind, including backup and abstraction, to provide a great answer to these difficulties.
Making analytics for healthcare
Health organizations must collect massive amounts of public health data in order to enhance medical facilities, employee behavior, patient circumstances, and other aspects. Because relying solely on one’s own data resources is insufficient, healthcare practitioners gather additional resources in various formats from other organizations. As a result, Snowflake is built to handle and analyze any type of data format, including XML, JSON, and CSV.
As with Snowflake, Azure Synapse has various distinguishing characteristics that allow it to be used in a variety of fields, as listed below.
Reporting, supply chain & forecasting analytics
Azure Synapse has a significant impact on supply chain management. At the moment, most businesses are incorporating big data analytics concepts into their ERP and SCM systems in order to increase delivery time efficiency and reduce inventory and transportation costs. However, Azure Synapse assists in achieving the following goals in the field of SCM.
- Training and preparation of predictive pipelines.
- Reporting of real-time operational data.
- Improved data integration with multiple formats and data sources.
- Integrated data processing and streaming ETL.
Customers want retailers to give the greatest e-commerce services. E-commerce enterprises should upgrade and change their offerings to meet the wants of their customers. Real-time product, service, discount, and offer recommendations are critical for consumer engagement. Azure Synapse can meet these standards while also providing real-time recommendations to customers in a safe and secure manner.
This article provides an overview of the Snowflake and Azure Synapse cloud data storage concepts. The primary goal of this paper is to establish the key distinction between Snowflake and Synapse, which is completely accomplished. Other topics covered in this section include the benefits, limitations, similarities, and use cases of both cloud databases.
The main takeaway from this article is that both Snowflake and Synapse are extremely strong tools. It is difficult to pick the best one out of the bunch. Snowflake has some flaws that Synapse can exploit and vice versa. Both tools have major advantages that make them popular and appropriate for commercial needs.