Snowflake Data Cloud [Private Offer Only]

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.

I primarily use Snowflake  for hosting and analyzing data. It acts as a data warehouse where data is stored, analyzed, and moved from stage to stage, ultimately exposing it to end users. Additionally, there is an increasing trend in implementing AI capabilities, allowing me to write SQL queries for insights into structured and unstructured data.

The independence of the compute and storage within Snowflake  is key. The integration with third-party solutions like DBT, Airflow , and Fivetran  is highly beneficial. The scalability options it provides, addressing issues without tying workloads into one virtual machine, enhance functionality. The fast pace of delivering new AI features also brings excitement about future possibilities. Further, being able to perform AI and Machine Learning in the same location as the data is quite advantageous.

There is a need for a tool to help me estimate the cost of using Snowflake. Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users. It would also help if Snowflake provided clear guidelines on how requests impact warehouse size.

I began using Snowflake in 2021.

Snowflake as a SaaS offering means that maintenance isn't an issue for me, and I have not experienced any cases where it was down.

While Snowflake provides the ability to scale resources, the expected return on investment is not always achieved. The billing doubles with size increase, but processing does not necessarily speed up accordingly.

The technical support from Snowflake is very good, nice, and efficient. I rate it ten out of ten.

Positive

Setting up Snowflake in 2021 was challenging, especially due to the strong security requirements at the enterprise level. It involved back-and-forth communication with Snowflake and Azure  support. However, the documentation has improved over time, which would likely streamline the process now.

I assume I achieve a certain level of return on investment, though I am skeptical about the calculations. However, I am generally happy after adopting Snowflake.

It is complicated to understand how requests impact warehouse size. Unlike competitors such as Microsoft and Databricks , Snowflake lacks transparency in estimating resource usage.

Snowflake's main competitor is Databricks . Databricks was initially built for big data and machine learning, and then moved to SQL capabilities, while Snowflake followed the opposite trajectory.

New users should not proceed on their own without leveraging the experience of others who have already implemented Snowflake. Establishing a framework for operation and change management is crucial. Define a clear operating model for Snowflake adoption, and start with a small warehouse to adjust as needed. I rate Snowflake a 9.5 out of ten because there is room for expecting further improvements.

Public Cloud

Microsoft Azure 

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.

Public Cloud

Amazon Web Services (AWS)

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.