Snowflake Reviews

In terms of solutions such as security, automation, or monitoring, security is prime. In agentic, I am building the agentic assets. From a data migration perspective, we are building the multi-agent accelerator which will help to migrate legacy data to a cloud platform or new platform, such as Databricks and Snowflake. Everywhere security is a prime concern nowadays. Nobody will allow you without the security assurance.

Regarding assessing Snowflake's data sharing capabilities in terms of enhancing collaboration, currently there isn't end-to-end or B2B sharing of data.

We utilize Time Travel with Snowflake because this is a very useful feature. Everyone finds it crucial because in conventional data platforms, it's very difficult to handle these kinds of things. This feature is essential, though I don't have the use cases currently; it is just there for implementation.

Regarding Snowflake's automated scaling and suspension features, this auto-scaling is very significant. We had a comparison with Databricks and Snowflake a few months back, and this auto-scaling takes an edge within Snowflake; that's what our observation reflects.

If I were to rate support, I would say it's about an 8 or 9. There is always room for improvement. What things you are going with to ask the support and how we manage the relationship matters a lot. Overall, we are very happy with Snowflake support.

I have been familiar with Snowflake since the last three to four years because within Genpact we have started using it.

I am satisfied with the work of technical support from Snowflake; they are responsive and helpful.

Positive

This is a service industry, service company.

Regarding satisfaction with pricing for Snowflake, I'm not in detail about Snowflake. I'm managing all the agentic world for the last two years, GenAI agentic. I don't have much insight on that part. The actual licensing is managed by the COE leads working with the sourcing team.

I rate Snowflake 8 out of 10.

I have used Snowflake for an end-to-end data solutioning as a warehouse, including all its capabilities such as ingestion and any implementations that were needed.

Snowflake has evolved significantly over time. Initially, they started with scalability, but more recently, they have introduced streaming capabilities, real-time and dynamic tables, along with various connectors.

Snowflake is well integrated with other solutions. I have used the Snowflake Zero-Copy Cloning feature in the past while prototyping data in lower environments. This feature is helpful as it saves a lot of time during the data replication process.

I have utilized the Time Travel feature multiple times while conducting testing. I have typically used the Time Travel feature for testing to see what the state of data was at a specific point in time. This feature is helpful, and Snowflake also has good RBAC policies with masking capabilities and policies that can be implemented.

Snowflake's data sharing capabilities are good for enhancing collaboration within the team. Additionally, Snowflake has introduced Snowpark and AAML integration, which makes it feasible to perform CICD operations.

Snowflake is currently stable, but it can be improved further. They have a couple of information schemas that can track account usage. If more connectors were brought in and more visibility features were added, particularly around cost tracking in the FinOps area, it would be beneficial.

I think the pricing for Snowflake is fair, and customers need to cautiously monitor their usage, as that is the customer's responsibility.

I have worked with Snowflake for more than five years.

Snowflake is currently stable.

Recently, Snowflake has introduced streaming capabilities, real-time and dynamic tables, along with various connectors.

I have not had a direct interaction with Snowflake's customer service or technical support. I have only interacted with them for billing calls, and those interactions have been fine without any challenges.

Positive

I have not explored multi-cloud deployment in Snowflake extensively enough to understand how it impacts an organization's data strategy for cost, performance, or compliance. I can rate Snowflake an eight out of ten based on my limited encounters with their support team, as those interactions have been positive. My company are customers only, not partners of Snowflake. I would rate this product nine out of ten overall.

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.

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.

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.

I have been working for more than six months in my current field, and it is about a year going to happen.

Snowflake is highly stable and performs well even with large data sets exceeding terabytes, maintaining stability throughout.

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.

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.

Positive

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.

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.

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.

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.

As a Data Engineer, I primarily use Snowflake for data warehousing tasks as well as ETL processing, and sometimes I also use it for data sharing. I personally find Snowflake better than other tools because one of the biggest benefits is how compute and storage are separated in it, allowing different teams to run workloads independently without affecting each other's performance. In that way, I find Snowflake more useful than other tools.

For example, I am part of an ETL team, which is transitioning from Informatica to GCP B-Cloud, so there are a lot of transitioning and tech remediation happening presently in our project. We use Snowflake to verify whether the data has been loaded and shared with the business users daily through Informatica, checking how much data is shared. We also verify this using GCP to see how much data is sent to the users, allowing us to determine whether the transitioning is happening perfectly or if we need to add any more constraints. Additionally, for maintaining data warehouse tasks in our restaurant project, we receive data continuously and must produce sales reports for the business users at the end of the day by the EOD flag, utilizing data warehousing steps to store a huge amount of data from the past 10 to 15 years of sales data, so we use Snowflake for that.

My main use case is for data warehousing, ETL process, and data sharing; these are the main tasks where we use Snowflake.

One of the best features I appreciate is how the computing and storage are separate in Snowflake, so that in our project with multiple teams, around 15 to 20 teams, all of them can use Snowflake without affecting each other's work due to the separation of compute and storage. Another key feature is scaling and performance optimization; based on the amount of data we receive, we can easily scale Snowflake without requiring any special requests to be raised to the team. For instance, during weekdays, the data would be less compared to weekends, so we reduce the storage somewhat during weekdays, saving us a huge amount of money.

This separation of compute and storage allows us to scale compute resources independently based on our requirements while keeping storage costs negligibly low. The automatic scaling and performance optimization are very important for any data engineering tool, and Snowflake offers this, allowing it to scale down when the amount of data is less and to automatically scale up when the data is high. Additionally, I appreciate the special features such as Time Travel and secure data sharing.

Since we are using Snowflake, it has improved the speed and reliability of our analytics processes, which is key to any data engineering or data warehousing project. Prior to using this cloud data warehouse, reporting jobs often competed for resources, causing delays and sometimes making business users wait for more than hours to receive data during peak times. With Snowflake, different teams can separately use the same data without affecting one another, and the data sharing has become more user-friendly. The performance tuning and scalability have positively impacted our organization as well.

Before using Snowflake, business users received data during peak hours at around 9:00 p.m. to 10:00 p.m., causing a three to four hours time wastage, but since we transitioned to Snowflake, that time is easily utilized for other tasks. Now, during both peak hours and normal days, data is available to business users daily at 6:00 p.m., making that three to four hours available for analytics, helping them make better business decisions.

One main area for improvement in Snowflake is cost visibility and optimization; while it's flexible and scalable, costs can increase quickly if warehouses are left running unnecessarily or workloads are not monitored carefully, raising the costs of the tool. The automatic scaling should be more optimized to work well with varying data levels. Another improvement could focus on recommendation capabilities and integrating an AI tool for better user onboarding without extensive documentation.

All the performance is generally excellent, and we have never experienced any crashes. However, query optimization could use improvement, and adding built-in guidance for workload management would be beneficial. If Snowflake integrates with an AI tool, new users can navigate the tool more easily by prompting the AI.

I have been using Snowflake for the past one year.

All the performance is generally excellent, and we have never experienced any crashes.

The automatic scaling should be more optimized to work well with varying data levels.

Anyone with prior knowledge in SQL and data engineering can easily use Snowflake. To understand the tool better, you can go through the documentation provided on Snowflake's website or use tutorials on YouTube before utilizing the tool. The user interface is very user-friendly, making it easy for new users to find it useful. I would rate this product an 8 out of 10.

My main use case for Snowflake is building ETL pipelines and ad-hoc analytics on data that we have. A specific example of a pipeline and ad-hoc analytics task I have worked on using Snowflake involves working in both Snowflake and Tableau to build BI analytics solutions for our customers and for the company. Every Tableau report needs a data source which is built in Snowflake. This means that we perform analysis where our raw data is located. Then we aggregate it, building queries that query other queries. The data source that Tableau is consuming can have the data that is needed.

Snowflake offers a user-friendly interface that is native and to some extent simple. You do not need a lot of time to find features you need to use. I would also mention scalability as one of the best features. Additionally, what Snowflake is doing around AI with the Cortex solution looks quite powerful, even though we do not currently use it.

Snowflake has impacted my organization positively mainly in terms of collaboration and transparency. We have multiple departments, so we do not have free access to other teams or departments' knowledge base. However, the fact that we can see what is happening in Snowflake from other departments and teams helps our team build quality reporting.

Snowflake can be improved in terms of its recent switch to a new user interface, which feels less intuitive compared to the old one. Moving to this new interface is something that I would like to see presented in a different way.

I have been using Snowflake for a bit more than two years.

Snowflake is stable.

Regarding scalability, we do not usually think about problems around it, which means that Snowflake is performing very well on this front.

I have not needed to engage with customer support since Snowflake is a native, intuitive solution. I cannot really answer this question.

I did not previously use a different solution; Snowflake was the solution here from the beginning.

I am not the person who manages pricing, setup cost, and licensing. Our team is not limited in pricing. The only experience we have had in terms of running and reprocessing a large number of historical data is that we monitor it from time to time. Since nobody is asking, we feel that we are doing fine with the money we spend on our calculations.

At this point, we have not seen a return on investment. However, we are having conversations about allowing new users to try Snowflake and some features, such as Cortex agents. It would be better to revisit this question in a month or two.

I am not the person who manages pricing, setup cost, and licensing. Our team is not limited in pricing. The only experience we have had in terms of running and reprocessing a large number of historical data is that we monitor it from time to time. Since nobody is asking, we feel that we are doing fine with the money we spend on our calculations.

It is difficult for me to give an answer regarding evaluating other options before choosing Snowflake because I was not in this organization when it began to operate. This means I do not know the full history behind this decision.

My advice for others looking into using Snowflake is to try it out for yourself and allow different types of employees to use it. For example, project managers or people who are not technical should also be allowed to try it out, even if they do not have SQL or Python knowledge.

From my experience, Snowflake is the most stable, user-friendly, and simply the nicest tool that I have been working with, and I have had experience working with many databases and different solutions. I would rate this product a 9 out of 10.

I started working with Snowflake when I was with Fidelity Investments around 2016-2017. We used Snowflake on AWS cloud because Snowflake doesn’t have an on-premise offering. You need to use it with AWS, Azure, or Google Cloud. 

As a consultant now, I assist enterprise customers, though I don't have Snowflake deployments yet.

Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses. One key feature is the separation of compute and storage, which eliminates storage limitations. 

It also has tools for migrating data from legacy databases like Oracle. Its stability and efficiency enhance performance greatly. Tools in the AI/ML marketplace are readily available without needing development.

Cost reduction is one area I would like Snowflake to improve. The product is not very cheap, and a reduction in costs would be appreciated.

Snowflake is very stable, especially when used with AWS. It works best with AWS compared to Google Cloud and Azure.

Snowflake is very scalable and has a dedicated team constantly improving the product. There are no problems on the scalability side.

Snowflake's technical support is excellent. During my time at Fidelity, I received great support in migrating data to Snowflake, with quick responses and innovative solutions.

Positive

The initial setup was rated six out of ten due to the time required for migrating existing data to Snowflake. Configuration and data migration are major steps involved.

Snowflake's pricing is on the higher side, rated as eight out of ten. If there were ways to reduce costs, it would be a positive improvement.

Snowflake is a great solution if you have substantial data volume. For those considering Snowflake, be prepared for the necessary initial investment in time and resources. 

I rate the overall solution nine out of ten.

Positive

The main use cases for Snowflake Data Cloud are standard data warehouses.

Real-time data sharing capabilities are not something we require with Snowflake Data Cloud.

It's easy to use, which is a feature that really helps me out.

Snowflake Data Cloud is scalable enough for my data management processes.

The reduced infrastructure management is the big difference compared to on-prem warehousing with Snowflake Data Cloud.

Snowflake Data Cloud is good enough security-wise for my needs, and it can integrate in terms of networks and user management.

Integration with third-party tools is possible, not just Snowflake Data Cloud solutions.

Pricing is quite high for Snowflake Data Cloud, which is an area that could be improved.

Snowflake Data Cloud is still beneficial to use, but only if you can afford it.

It can be cost-effective if you're using Snowflake Data Cloud at an enterprise-level business.

I've been working with Snowflake Data Cloud for at least four years.

The deployment of Snowflake Data Cloud was a quite smooth process.

I have not contacted Snowflake Data Cloud technical support.

I've had no need for it, with no questions or issues.

In a couple of weeks, you can have a fully enterprise architecture with Snowflake Data Cloud.

I have not worked with anything else in the cloud as competition to Snowflake Data Cloud.

I worked with on-prem warehousing before Snowflake Data Cloud.

Snowflake Data Cloud is good, and I can recommend it to colleagues or friends, though it may depend on the use case.

On a scale where ten is a perfect solution and one is absolutely useless, I would rate Snowflake Data Cloud an eight out of ten.

Snowflake is used to create a data lake, and we have a consumer base where we create data. This data is consumed by different consumers and vendors, who have different needs in how they want to use it. We are from the data engineering side, and we put this data from various source systems in this data lake. We put the data into Snowflake and use connectors to connect to the data lake.

With Snowflake, we don't need any other ETL tool, which is the primary reason I started liking this tool. In addition to the database, Snowflake also provides ingestion capabilities. Currently, we are only using the database because we already have integrations with the IICS. The solution also provides data engineering capabilities, which we can leverage and utilize in the future.

The addition of more AI capabilities in Snowflake would help us more.

I have been using Snowflake for two years.

To the extent I use it, Snowflake is a very stable solution. I did not find any instability with the tool because we primarily use it to create our data lake, and it is available.

Snowflake is a scalable solution. Depending on your needs, you can scale it up or down. I find it quite flexible in terms of scalability. Around 50 users work with Snowflake in my process.

I also used Oracle. Snowflake is easier to handle than other solutions and has many things under one umbrella. I like Snowflake's data ingestion capability the most compared to other RDS and database vendors. In other solutions, you would need to design your integration methods separately. However, if you choose, it's not fake. Data ingestion and data lake creation can be easily achievable with Snowflake.

Oracle is less expensive than Snowflake. Snowflake provides better value at a little higher cost in the Snowflake. RDS is cost-effective but has fewer features than Snowflake.

The solution's integration aspect is good, and all the connectors are in place. I found Snowflake similar to RDS. We use it for both data in motion and data in transit. It looks like the tool handles the data quite securely.

We create ETL patterns. We ingest data from different source systems, and we have to create data pipelines. It would be useful if we could have AI features added to identify what I'm going to do with this data. It would be good if it could look at the data and help me create an automated pipeline instead of me creating a pipeline by myself.

I'm from a retail background. I completed my Oracle DBA training a long time ago, about 18 years ago. I was quite familiar with the Snowflake and relational database concepts since I had already completed the Oracle ops, DBA ops, OCP, and OPA courses. For me, it was a journey similar to when I shifted from Oracle RDS to Snowflake. Although I was quite familiar with most of the concepts, there were some learnings.

Whosoever is in the data field should at least try Snowflake once. They will then realize the best features in the solution and can continue using it.

Overall, I rate the solution a seven out of ten.

Mostly, we use it for the data warehousing side of use cases, where you have, like, a huge amount of data, and you are required to do reporting in terms of data science, data warehousing, or ad hoc reporting. The use cases we have used are, for example, data coming from MedTech devices, mostly sensor data, which we need to load in Snowflake and do data analytics. We have been using the tool for a couple of MedTech clients.
 

The most important part of the tool is that computing and storage are totally separated, and it keeps on evolving every two weeks, with the tool having releases. New features are coming up in the tool. With respect to AI, the tool is also progressing well. The scalability and performance are quite good. If you have data, like in CSV or any other format, you can load it very quickly and then do your analysis. Columnar database performance, scalability, and the addition of new features are a few useful features of the tool.

I think people do not want to create pipelines for many customers now. Normally, we have this layer architecture, like layer one, layer two, layer three, or layer four, where we have raw data, integrations, business data, and then semantic data, so we have to create various pipelines. People don't have to create or maintain pipelines since, in the future, if there are any changes in the source data, it should be very easy to configure and create the pipeline rather than the developer doing that for them. Though it may not be possible to make improvements based on the expectations of the people, considering the AI market, code generation can be simplified a little bit by using streams. People want to be able to develop the pipeline without involving many developers by doing some configurations and creating the pipeline. The customer expectation is that they don't want to create tables for each report, but what happens currently is that if you don't create that, then you have to run the query every time. Suppose I have created raw data, and I want to do some aggregation. In that case, if I don't create a materialized view or a table, I have to run those aggregate queries again and again, which will cost me the cost attached to Snowflake usage. From an improvement perspective, Snowflake can evolve in terms of writing costly, expensive queries with less cost and try to see if pipeline development can be made a little easier.

I have been using Snowflake for a year and a half.

There were use cases where there were only 10 to 15 users. There was one requirement where the customer asked for 3,000 concurrent users to try to get a real-time report from the tool, but then our company suggested that Snowflake was not the right choice for them because it is more kind of a data warehouse, and they were looking more into transactional reporting. For Snowflake-based projects where we have worked, it is more concerning a smaller number of users, like around 20 users. However, if a huge number of users are required, Snowflake is not the right choice.

My company has partnered with Snowflake. Normally, we reach out to the account manager or regional manager, and sometimes we get support. Most of the time, we ask for support from the architecture and solutions part of it to review it or for some workarounds. Right now, we have not gone for low-level technical support from Snowflake. Whatever we have worked on, we are able to manage.

I have been working all my life on databases, so I have almost twenty five years of experience in databases starting from SQL, Oracle8i, Oracle 9i to MySQL, SQL Server and Redshift. I have also used Solr and Elasticsearch, which are not databases but all data-related things I have worked on, including PostgreSQL.

The main thing about Snowflake is that it is totally outside the customer's cloud. If I am an AWS customer, even if Snowflake is hosting on AWS, it is on a separate account right now. If somebody has some critical data that cannot be shared outside the cloud, then such customers or people are a little hesitant to use Snowflake. Recently, there were some breaches or password issues, so security concerns like that are there. There is also the costing part attached to the tool. Now, people are looking into tools that are available at a lower cost and offer more user-friendliness. The tool is a good data cloud product, but it is a little bit outside the customer's environment, which makes it difficult to convince the customer to use it.

Speaking about the product's initial setup phase, I would say that the product is used just from the cloud. We have not installed it in any environment. I work with the tool's SaaS version.

The tool does add some value to the company. When it comes to pipeline development work, though customers expect it to be faster, I think if you have simple files, you can load them in a day and analyze the data. Productivity-wise, it is definitely much better compared to Redshift. Redshift Spectrum is catching up with Snowflake, but I have not explored it. To be very frank, I am not very familiar with Azure Data Warehouse, so I am not sure how it is different from Snowflake, but from what I have seen, it has been good in terms of productivity.

The pricing part is based on the computing and storage. The costs are different and then there are services costs as well. I have heard that Snowflake is costlier than Redshift or GCP BigQuery. A small customer may not go for Snowflake.

Speaking of how Snowflake enhances our company's AI-driven projects or analytics, I would say that the tool has features like Document AI and Snowflake Cortex. AI can be used if the tool is for very basic use cases, like anomaly detection or prediction. With simple use cases, you don't have to set up a big infrastructure. You just load data and use the tool's services. I have not used the tool for complex AI projects. I am not an AI person. Rather, I can be described as a data engineer or data architect. In our use cases, we have explored the AI feature of Snowflake more from document processing and doing a simple exploration of the feature. For customers, I have not used Snowflake's AI feature.

Speaking about how Snowflake's scalability feature impacted our data processing and analytics tasks, I would say that the tool has a virtual warehouse, so it really helps. You can scale based on your needs. You can change the warehouse sizing, which will help with the scalability. You can just increase the warehouse size, and it gets your work done.

There are various ways to integrate the tool. I think the tool has connectors also, but the external table is one way to load your data in Snowflake and start analyzing it quickly. Now, the tool also works with Apache Iceberg format, though I have not explored that. With respect to Snowpipe, getting data from CSV to Snowpipe are things we use, and they are all quite easy to use. In terms of native connectors to various data sources, though I have not explored them, I see the tool has support for various connectors. I believe that will be good. For most of the use cases, data is loaded onto S3, and then we use Snowpipe along with external tables and Snowpark ML to process the data.

Snowflake has something called Snowflake Horizon, which has bundled various features of data security, data governance, and compliance together, and they have come up with the package. The tool has very good data security in terms of masking data. You can have different roles and assign policies in terms of who you want to be able to see data of a particular department, so you can assign based on department ID that only certain people can see the data. I found good features in my various other cloud databases, and compared to them, Snowflake data security and data governance are quite capable.

I don't think it is difficult to maintain. As the organization grows, maintaining policies, user roles, and data masking policies might become a little tricky in Snowflake. In AWS, we have a well-architectured framework where you have a defined framework or pattern, and you try to reuse it and modify it as needed. I don't see such kind of information or patterns largely available in Snowflake. I think as an architect, if we have a well-architectured framework for Snowflake, it will be useful. In terms of maintenance, I think the performance and all is okay in the tool. Data governance and policy management are a little bit tedious for the tool.

I recommend the tool to others. People should only be okay with the product's cost.

I rate the tool an eight out of ten.