In the digital age, cloud platforms offer a host of business benefits, right from infrastructural support to data storage and native applications. With the right cloud platform, organizations can attain advantages like high scalability, low costs, and improved productivity.
The 2023 Cloud Adoption report from O’Reilly has some interesting findings:
- Two-thirds of companies are operating on the public cloud, while 45% are using the private cloud.
- Around 48% of companies plan to migrate 50% of their applications to the cloud in 2024, while 20% plan to move all their applications to the cloud.
- Only 5% of companies plan to switch from the cloud to on-premises infrastructure.
To meet their business requirements, companies must also choose the right cloud platform. What are the various parameters they must consider when making this choice? Here’s a detailed analysis:
How to choose the right cloud platform
In recent years, there’s been an exponential rise in cloud adoption in the BFSI and retail sectors. According to Deloitte, 4 in 5 BFSI executives are leveraging the cloud for application development, data analytics, and software engineering.
Depending on the industry’s needs, companies consider various factors when selecting their cloud platform. Here are some of the parameters to consider:
- Data security
Data-dependent companies must ensure complete security when choosing to store their confidential data on the cloud. When evaluating a cloud management service provider, evaluate their current security measures. Security features can include multi-factor authentication, data encryption, disaster management, and data backups. - Reliability and performance
Among additional factors for choosing a cloud security service provider, performance and reliability can directly impact business productivity and revenues. Unreliable cloud platforms may offer good response time but have exceptionally high downtime. Based on your business requirements, consider performance-related metrics such as:- Response time
- Data latency
- Throughput
- Scalability and flexibility
Scalability and flexibility factors are crucial for fast-growing companies with changing requirements. Besides enabling business growth, a scalable cloud platform is a must for companies with seasonal fluctuations in demand. To meet these requirements, the cloud service provider must fulfill the need for higher cloud storage and computing power without disrupting operations; for instance, a cloud platform that offers both horizontal and vertical scaling. Further, they must provide flexible offerings and pricing models, so that companies can customize their cloud solutions based on changing needs. - Costs
A CloudZero report revealed that 58% of companies adopting the cloud have no control over their cloud-associated costs. Since 2022, business disruptions due to high cloud costs have doubled from 11% to 22%. Cloud cost is a major factor when choosing the right cloud platform.
For a complete picture, consider the total cost of ownership (TCO) when choosing the platform. This includes pricing plans, data storage expenses, data transfer costs, and customer support.
Snowflake vs BigQuery – Which is better?
Among the leading cloud-powered data warehouses, Snowflake and Google BigQuery offer a host of business benefits and functionalities. Which of these cloud platforms is right for your business? Here’s a detailed comparison:
- Architecture
By decoupling data storage from computing resources, both Snowflake and BigQuery can seamlessly scale to meet mission-critical requirements.
With its three-layered architecture, Snowflake comprises the following layers:
- The centralized storage layer is used to store data records from cloud platforms like Google Cloud, AWS, and MS Azure.
- The multi-cluster compute layer enables massive parallel processing (MPP) clusters for quicker processing of complex queries.
- The cloud services layer comprises various cloud services including query management, optimization, and metadata.
On the other hand, Google BigQuery comprises the following layers:
- A serverless architecture that automatically scales (up or down) its resources based on computing needs.
- With its columnar format, BigQuery’s storage layer is optimized for analytical workloads, thus offering better performance over row-based formats.
- Slot-based compute layer allows users to run multiple queries with automatic scalability of the available slots.
- Query performance
Enterprises need high-speed query performance for effective analytics and decision-making. Both Snowflake and BigQuery offer good performance for a variety of workloads. For instance, Airbnb uses Snowflake for analyzing real-time booking data. Spotify uses BigQuery for analyzing massive volumes of streaming musical data.
Snowflake is suitable for running multiple workloads concurrently while maintaining isolation between workloads. With its built-in caching mechanism, BigQuery reduces the querying time, while its serverless architecture allocates more computing resources for processing queries.
- Data security
For effective data security, both Snowflake and BigQuery offer features for user authentication and network security. For user authentication, both these cloud platforms support key-pair authentication, MFA, and single sign-on (SSO). With regards to data encryption, Snowflake and BigQuery have strong encryption with the AES-256 standard.
For network security, Snowflake limits virtual private networks (VPNs) to subscribers of the Virtual Private Snowflake version. On the other hand, BigQuery allows every Google Cloud user to configure their VPN with GCP’s virtual private cloud service control.
- Pricing models
Both Snowflake and BigQuery cloud warehouses have different pricing models. While Snowflakes charges users based on execution time, BigQuery charges its users based on the consumed slots for its virtual CPU.
With BigQuery, customers follow the “pay-as-you-use” pricing model, where they are charged based on the consumed data volume and stored data. This fixed-price model is better for companies with fixed workloads or limited budgets.
On the other hand, Snowflake uses a seconds-based billing model, where computing costs are charged for every second. This pricing model is suitable for companies with short queries. Besides, Snowflake offers flexibility by separating computing costs from storage costs.
What’s the verdict?
For data warehouse modernization on the cloud, both Snowflake and Google BigQuery offer a reliable cloud platform with features like reporting analytics, scalability, and query optimization and performance. Which of these platforms is better? That is dependent on the business requirements.
Overall, our verdict goes to Google BigQuery for its capability to manage large-scale analytics and data exploration. Its serverless architecture is suited for scaling to high-volume datasets with lower overhead.
As a trusted cloud partner with Google Cloud, Onix can help you choose the right cloud platform based on your business requirements. Our professional team can also help you move to Google Cloud with our GCP database migration service.
If you are looking for a reliable Google Cloud partner, get in touch with us now.
Reference links:
https://qentelli.com/thought-leadership/insights/how-choose-right-cloud-service-provider
https://www.mparticle.com/blog/snowflake-vs-bigquery
https://www.luzmo.com/blog/snowflake-vs-bigquery
https://medium.com/google-bigquery/snowflake-vs-bigquery-a-comprehensive-comparison-9d30c99f1c1b#
