Cloud data warehousing has transformed analytics workflows and delivers faster, cheaper insights
What is our primary use case?
Snowflake is primarily used to handle the data warehousing part, for creating data modeling, and also keeping the raw data and creating reporting data so that it is further used for data analytics.
Whenever we receive files from API integration from the front end, such as Excel files, HR data, sales data, or marketing data, we load those files into Snowflake. These files may be in non-SQL formats, such as Parquet files or text files. From Snowflake, we transform those files into tables. Once the tables are transformed, we create dimension tables according to the business needs, either in a star schema or Snowflake schema. After creating that schema, we create curated data on top of it. Once the curated data is created, we create a view query which is further used as reporting data analytics. We load the raw data, transform it, and then use it further as reporting data analytics.
Snowflake is utilized as a hybrid cloud. We primarily use it as a private cloud, but some components are hybrid.
What is most valuable?
Snowflake has a very wide variety of data integration capabilities. For workflows and data sources, we can capture semi-structured or non-structured data such as Avro, Parquet files, JSON files, and text files, or we fetch data from API integrations, such as Salesforce API or SAP data. We try to inculcate and grab all the data and then load it into Snowflake. Snowflake works as a very good tool because it handles and creates micro-partitions automatically. Whenever we create SQL queries, it automatically divides and runs the whole query itself on a multi-cloud, multi-cluster, and multi-cloud enterprise workflow.
The top features about Snowflake are first that it is hybrid cloud native. Everything is on cloud, so we have very zero maintenance for computational or in order to maintain Snowflake. Second, it is a very simple tool to understand with an architecture dividing the data layer, storage layer, computational layer, and the UI layer. This unique feature of Snowflake gives us very low costs. It is very helpful for SQL analysis. In Snowflake, we keep the data in a very low-cost manner, and if we don't compute it, we can also move it to cloud storage. Third, it is very elastic in scaling. Whenever a large amount of SQL or computational work is required, the warehouse automatically scales in and out. This is one of the best features in Snowflake that it manages automatically and runs on ACID properties: Atomicity, consistency, isolation, and durability. Each query works on isolation, hits isolation, and Snowflake very much optimizes performance. It is very simple to use.
The elastic scaling helps significantly; for example, if we get a large amount of data after a leave period, it requires scaling out the warehouse to a larger size. If we have a bulky query, the warehouse size increases automatically, dividing the query into micro-partitions for quick execution. If we encounter unexpected data, such as special characters, Snowflake creates zero-copy cloning, aiding in development and testing. Such cloning does not require physical storage, hence reducing storage costs.
Snowflake has positively impacted us by making everything cloud-native, significantly reducing the systems and application running process. The maintenance costs are low because Snowflake maintains a high amount of data efficiently at a low cost. There is also a massive scalable data warehousing performance. Whenever we need to increase SQL data, the data warehouse scales up or down based on our needs and query requirements. If the query is not heavy, the costs remain low, and the differentiation between storage and computational layers makes Snowflake very cost-effective compared to other data warehousing tools.
Snowflake has contributed to significant cost savings. Previously, we paid for both storage and computational layers, where unused data accumulated costs unnecessarily. With Snowflake, the unique approach to managing storage and computational costs allows us to transfer less frequently accessed data to Glacier, further optimizing expenses. Snowflake's performance enhances business efficiency, allowing timely achievement of targets. The analytics and transformations we execute using Snowflake are notably faster, providing significant advantages. The scalability is remarkable; marketing teams can analyze data on time-sensitive trends swiftly.
What needs improvement?
Snowflake is already quite improved, but they have recently introduced AI features. AI integration would be beneficial for direct data capturing from systems such as SAP and Salesforce to Snowflake as raw data and allow for efficient data warehousing.
Snowflake is very good overall, but it could improve documentation for supporting different structures. Even though Snowflake has a strong ANSI SQL presence, there are some features that do not perform optimally, particularly for Change Data Capture (CDC).
I do not rate it a 10 because there are still areas for improvement, such as AI integration and raw data capturing. Once those enhancements are achieved, the dependency on ETL would decrease, as would the scheduling aspect to streamline major data workloads within Snowflake.
For how long have I used the solution?
I have been working for more than six months in my current field, and it is about a year going to happen.
What do I think about the stability of the solution?
Snowflake is highly stable and performs well even with large data sets exceeding terabytes, maintaining stability throughout.
What do I think about the scalability of the solution?
Snowflake's scalability is excellent, depending on dataset sizes and query handling. It can scale from one engine to up to 32 engines seamlessly, maintaining performance.
How are customer service and support?
We interacted with customer support, and they were quite helpful. They listened to our issues and provided solutions, but we faced challenges acquiring documentation for various structures. We sought this documentation multiple times but faced difficulty in obtaining it.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Earlier, we were using on-premise data warehousing with Hadoop, but it was complex and high-maintenance. The setup was also prolonged and costly. Therefore, we transitioned to Snowflake, which allows for rapid setup.
What's my experience with pricing, setup cost, and licensing?
For pricing, setup cost, and licensing, everything is managed smoothly. Regarding licensing, it is inexpensive. The setup cost is low, mainly due to AWS Marketplace; we only need to pay for serverless configurations. When it comes to cloud support, the setup cost is very cheap compared to other platforms, such as Oracle or PostgreSQL, which typically require higher costs. Snowflake's handling of computational resources and hardware is excellent, making it one of the most affordable data warehousing solutions on the market.
Which other solutions did I evaluate?
Before choosing Snowflake, we evaluated other options such as Redshift and Databricks. What we observed was Redshift's architecture seemed coupled, leading to slower scaling and limited concurrency issues requiring extensive maintenance. Redshift's clustering methods increased complexity in data management. In contrast, we found Snowflake offers fully managed services with micro-partitioning and independent scaling, making it a more desirable option.
What other advice do I have?
One more feature is Time Travel, which maintains historical data. For transient tables, it is retained for seven days, but for permanent tables, it is stored for 90 days. If we want to keep historical data beyond that, we can extend it via Time Travel. If we accidentally delete some tables, we can retrieve that data within 90 days, or if that does not happen, we can reach out to Snowflake support team for retrieval known as Fail-safe travel. Additionally, Snowflake offers very secure data sharing, ensuring no data movement among different applications while collecting data into one place for transformation and loading into data warehousing. The storage, security, and compliance are all managed well within Snowflake, and we just need to maintain the security policies as per administration.
One thing is very low maintenance. The infrastructure has transitioned to cloud, which is very manageable. Snowflake gives us very high concurrency, and we can predict our costs effectively. The enterprise data analytics we perform—whether BI reports or analytics—everything is very helpful with Snowflake.
My advice is to leverage Snowflake for its scalability, efficient query handling, user role assignments, and direct integrations with platforms such as SAP and Salesforce. The decoupling of computational and storage architecture is a key feature, allowing us to pay based on actual usage. Features such as zero-copy cloning and Time Travel enable easy data recovery and improve overall data management. Additionally, automatic tuning enhances query performance and sharing securely aligns with stringent security policies.
I believe we have covered almost everything. To summarize, Snowflake offers scalable storage that adjusts automatically, high concurrency, and virtually no downtime. It continually enhances performance by improving cluster configurations. I rate this product an 8.5 out of 10.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Effortless, Scalable Data Warehousing with Snowflake
What do you like best about the product?
I like best about Snowflake is how it makes advanced data warehousing simple, fast, and scalable without heavy infrastructure work.
What do you dislike about the product?
If warehouses are left running or queries are poorly optimized, credit consumption increases fast. Cost control needs discipline.
What problems is the product solving and how is that benefiting you?
Traditional databases struggle when data and users grow. Snowflake’s separation of compute and storage lets me scale performance instantly without impacting other users.
Benefit to me: Faster queries and no conflicts between ETL jobs and BI dashboards.
Best Cloud Data Architecture with Impressive Results
What do you like best about the product?
It has best architecture for cloud data , result oriented
What do you dislike about the product?
Time taking for output and it's autoupdate
What problems is the product solving and how is that benefiting you?
Cloud based data storage , data sharing
Intuitive Framework, Needs Script Flexibility
What do you like best about the product?
I use Snowflake for pulling, cleaning, and required functions, and I find it easy to use and implement SQL queries with this platform. I appreciate the framework, which is easy to understand, and the layout it displays is great, with the left side full of tables, the center for queries, and the bottom running the query and displaying the output. The initial setup was pretty chill, and I would rate my likelihood to recommend it as a 9 out of 10.
What do you dislike about the product?
I will say, for dynamic scripts especially columns - it won’t automatically add new columns in dynamic scripts. We can only do this for rows, not for columns. It should be flexible in the script to allow these kinds of dynamic scripts.
What problems is the product solving and how is that benefiting you?
I find Snowflake easy to use for implementing SQL queries and working with its intuitive layout, which displays tables, queries, and outputs clearly.
Scalable and Easy-to-Use Cloud Data Platform
What do you like best about the product?
What I like best about Snowflake is its **simplicity and scalability**. It separates compute and storage, which makes performance tuning easy and allows workloads to scale independently. The platform is easy to manage, requires minimal maintenance, and supports fast querying with strong security and data sharing capabilities, making it very efficient for modern data analytics.
What do you dislike about the product?
One drawback of Snowflake is cost management, as usage-based pricing can become expensive if queries and compute resources are not carefully monitored. Additionally, there is limited control over low-level system tuning compared to traditional databases, which can be a challenge for highly specialized performance optimizations.
What problems is the product solving and how is that benefiting you?
Snowflake solves problems related to scalability, data silos, and complex data infrastructure management. By providing a fully managed, cloud-native platform with separate compute and storage, it simplifies handling large and diverse datasets. This benefits me by enabling faster analytics, easier data sharing across teams, reduced operational overhead, and the ability to focus more on data insights rather than infrastructure maintenance.
Effortless Scalability and Integration for Fast-Paced Analytics
What do you like best about the product?
Snowflake stands out for its ease of use and instant scalability—we can right-size compute in seconds without complex infrastructure, while maintaining strong governance and encryption for sensitive safety data. The multi-cloud flexibility and tight integrations with BI/ETL tools mean our data engineering and analytics teams move faster with less friction.
What do you dislike about the product?
Everytime, we load the data for analytics purposes, it has to be loaded completely. It is not good for few use cases like Sensor data wherein realtime decision making is required. Also, it has cost structure which is complex. We don't know we are really spending money on.
What problems is the product solving and how is that benefiting you?
Previously, data was scattered across spreadsheets and multiple systems, making reporting slow and error-prone. With Snowflake, we can ingest data from Azure Data Factory, maintain clean schemas, and scale compute instantly for analytics. This enables faster incident trend analysis, proactive risk identification, and compliance reporting. The biggest benefit is time-to-insight—regulatory requests that used to take days now take hours, and dashboards in Power BI update in near real-time for site leadership
Cloud-Native Analytics Made Effortless and Scalable
What do you like best about the product?
Snowflake is a cloud-native data platform that removes infrastructure management and simplifies analytics.
It separates storage and compute for flexible scaling and cost efficiency.
With automatic optimization, strong security, and easy data sharing, it makes working with large data fast and reliable.
What do you dislike about the product?
Snowflake can become expensive if warehouses are left running or queries aren’t optimized.
It also gives less control over low-level tuning compared to traditional databases.
Vendor lock-in is another concern since Snowflake features are tightly tied to its platform.
What problems is the product solving and how is that benefiting you?
Snowflake solves problems like infrastructure management, scalability, and slow analytics on large datasets.
By separating storage and compute, it allows fast, flexible scaling without impacting other users.
This benefits me by reducing operational effort, improving query performance, and enabling quicker data-driven decisions.
Effortless Analytics and Fast Scaling with Snowflake
What do you like best about the product?
Snowflake is very easy to use and quick to implement compared to traditional data warehouses. I like how compute and storage are separated, which makes scaling simple and cost-efficient for different workloads. Integration with BI tools and cloud services is smooth, and query performance is consistently strong. Because of its simplicity and reliability, it’s used very frequently for analytics and reporting.
What do you dislike about the product?
Cost can become difficult to track, especially with frequent queries and multiple users running workloads at the same time. Some advanced features require a deeper understanding to optimize properly, and debugging performance issues isn’t always straightforward. While support and documentation are good, fine-tuning usage to control costs takes effort.
What problems is the product solving and how is that benefiting you?
Snowflake solves the challenge of managing and querying large volumes of data without worrying about infrastructure. It allows us to scale compute independently for different teams, avoid performance bottlenecks, and run analytics workloads efficiently. This reduces operational overhead, improves query performance, and enables faster insights for reporting and business decision-making.
Outstanding Performance and Simplicity for Modern Data Platforms
What do you like best about the product?
Snowflake’s performance and simplicity stand out. Features like automatic clustering, micro-partitioning, and zero-copy cloning reduce operational overhead while still delivering high query performance. The platform is easy to manage compared to traditional data warehouses.
Finally, Snowflake’s strong ecosystem and cloud-native capabilities—including secure data sharing, time travel, and seamless integration with tools like DBT and cloud services—make it highly effective for building modern, scalable data platforms.
What do you dislike about the product?
What I dislike about Snowflake is primarily related to cost predictability and control. While the platform scales very well, it is easy for costs to increase unexpectedly if warehouses are not carefully managed or if inefficient queries run at scale.
Another limitation is reduced transparency and control at the infrastructure level. Compared to open-source or self-managed systems, debugging deep performance issues or understanding low-level execution behavior can be challenging.
Lastly, for certain use cases, vendor lock-in can be a concern. Snowflake’s proprietary features and SQL extensions make migrations to other platforms more complex, which requires careful architectural planning upfront.
What problems is the product solving and how is that benefiting you?
Snowflake solves the problem of scalability and operational complexity in data warehousing by providing a fully managed, cloud-native platform that separates compute from storage. This eliminates the need for capacity planning, infrastructure management, and manual performance tuning that are common with traditional warehouses.
It also addresses performance bottlenecks and concurrency issues. Multiple teams can run ETL jobs, analytics, and ad-hoc queries simultaneously using separate virtual warehouses without impacting each other. This directly benefits me by ensuring consistent query performance even during heavy workloads.
Additionally, Snowflake simplifies data sharing, governance, and reliability through features like secure data sharing, time travel, fail-safe, and automatic optimization. As a result, I can focus more on data modeling, pipeline reliability, and analytics use cases rather than infrastructure maintenance, leading to faster development and more dependable data delivery.
Easy Setup and Fast Performance, but Room for Improvement
What do you like best about the product?
What I like most about Snowflake is its ease of use and quick implementation. It is easy to set up, requires minimal infrastructure management, and offers excellent performance with automatic scaling. Running and managing queries is simple and efficient.
What do you dislike about the product?
One downside of snowflake is the cost, which can increase quickly if usage is not monitored properly. The separation of compute and storage can also make billing a bit confusing for new users.
What problems is the product solving and how is that benefiting you?
Snowflake helps solve problems related to managing large volumes of data, slow query performance, and data silos across teams. It allows us to store, process, and analyze data in one centralized platform without worrying about infrastructure management. This improves reporting speed, enables faster insights, and helps teams make better data-driven decision while scaling easily as data grows
