Snowflake AI Data Cloud

What do you like best about the product?

Snowflake is our datawarehouse that hosts all of our data from several sources that we ingest from. The UI is amazingly easy to use. Features like Cortex and Snowsight are super useful for stakeholders and users who are not very technical.

What do you dislike about the product?

The cost has been on the higher side, it is definitely related to the huge volumes of data we have.

What problems is the product solving and how is that benefiting you?

Snowflake is solving the problem of fragmented data and inconsistent reporting by centralizing data from many sources into GitLab’s Enterprise Data Warehouse, which serves as a common source for KPIs, analytics, dashboards, and reporting.

For me, that means:

Faster access to trusted, governed data.
More consistent metric definitions and business logic.
Easier reconciliation across systems such as Salesforce and Zuora.
Less manual data extraction and file-based analysis.
Better support for self-service analytics through Tableau and AI-enabled data exploration.
Overall, Snowflake lets me spend less time assembling data and more time improving data quality, governance, and decision-making.

We provide permissions to the roles and schemas, and based on that, we provide a group to the application team. They sign up for those groups and are able to access the tables and views. This is how the process that I worked on.

Our major use case for Snowflake Data Cloud is finance-related. We were uploading SQL files to Snowflake, and from there, the end users were able to see the figures inside the Snowflake dashboards.

I am using Snowflake Data Cloud for data enrichment, basically to deploy some SQL files to check the availability of data inside Snowflake.

It helps to improve strategic planning with this data enrichment.

The biggest advantage of Snowflake Data Cloud is that we can use it for querying, and it is serverless. This makes it less costly compared with others.

The Data Sharing feature in Snowflake Data Cloud does help with our data accessibility, and it includes data masking.

The benefit of data sharing is that people cannot see the masked data, so we need to provide some ownership-related privileges. It provides a secure way that we can store our data.

Regarding the security part, it significantly impacts our data protection. The security impact is that we provide separate groups to the users who sign up for that particular role within that group. Using that IAM role, they can integrate and execute security-related SQL tasks. From a security perspective, we are not giving direct access to the users.

I have not noticed any disadvantages for Snowflake Data Cloud, but if I do, I will let you know.

The connection requires time, and the integration part support would be a six. The documentation might not be very straightforward; these are all the negative parts that we can see.

I started using Snowflake Data Cloud three years ago.

Snowflake Data Cloud is about 99% stable and reliable.

In terms of scalability, we are able to scale easily.

The support for concurrent users and workloads in Snowflake Data Cloud is important for us. If we face any related dashboard configuration issues or warehouse creation-related issues, we contact the Snowflake team directly, raise a request, and get the information from them on what exactly we need to focus on and how to resolve the issues. Additionally, if there is anything related to permissions, such as ownership and masking-related policy permissions, we can reach out to them directly.

For technical support, I give it six points out of ten, where ten is the best.

I give it six points because sometimes the documentation is not very good, so we need to wait to connect with them. After that, their answers are okay and good, but the call waiting time is really high.

The installation of Snowflake Data Cloud requires following the documentation provided by the Snowflake team. It is fine, medium level—it is not very high level or something.

The most difficult part with installation is managing credentials. We need to create accounts and sign up for those things; it takes a lot of time and causes confusion when making a connection between Snowflake Data Cloud and other integration tools.

I see value for money here because it saves our time and money, though I am not a budget manager.

We experience savings of about 15%.

The price for Snowflake Data Cloud is reasonable and not very expensive.

I hear that in the coming months and years, there is a tool that is emerging, but I am not sure of the name. Compared to Snowflake Data Cloud, that tool stack is very good for my upcoming project. They are discussing that point, but I am not recalling the name.

In terms of features, I am part of the enablement team, so I can provide access. The development will take care of all these things. I am not the administrator for that.

Regarding the machine learning part, machine learning is a different approach, so the MLOps team will use that particular aspect to deploy their Step Functions related tasks. For their machine learning, it involves Step Function and Lambda related tasks, as well as SageMaker related tasks. I am from the operations team, and this MLOps team will use SageMaker. What we do is create a role and policy, and we provide some permissions to them. Also, if they want to use SageMaker tools, we will whitelist at the policy level and give some enablement to them so they can use that particular tool stack to deploy their pipelines from the MLOps perspective.

Snowflake Data Cloud is one of the best for me because I am using it. In summary, I would give Snowflake Data Cloud around eight points on the scale from zero to ten, where ten is the best.

Public Cloud

Amazon Web Services (AWS)

Our main use case for Snowflake was to ingest data, use it for search, analytics, and data sharing with our customers; however, in reality, those use cases did not work.

When it came to our challenges with those use cases, Snowflake oversold their capabilities dramatically and told us we could simplify our Databricks implementation. In the end, this was confirmed by Snowflake architects that Snowflake ingestion would not work for our use case because Snowflake is not sophisticated enough. This resulted in us losing significant time, spending considerable money, and ended up with a product that was not a good fit for us after making a substantial financial commitment. When it came to search, we thought we could use it based on the initial conversations with the sales representatives, but after we purchased the product and made the capacity commitment, we were told that capability was not available. The end result was that we have a very expensive data lake that does not support things like Iceberg as it should.

Snowflake's data sharing capability with other enterprises is the best feature they offer, which is excellent for us as a software company. Everything else they offer can be done better with other platforms at a cheaper rate. Snowflake's data sharing capability stands out compared to other platforms because it is native to Snowflake. If your customer has Snowflake, it is valuable. You can send your data into Snowflake and then the customer receives it automatically.

However, Snowflake has been the biggest mistake we have made since we started the company. It was a drain on the team from a technical perspective and almost from an emotional perspective. A specific example of how it was a drain on the team includes that we were promised a significant amount of things pre-sale around access to solution architects, support to get us on the project, and technical abilities to simplify our infrastructure. All of these technical abilities were significantly oversold. When it came to setting up meetings and getting help, it took months to get meetings with people who could actually assist us. In the end, when we got the meeting with someone who actually had the technical ability to do the work, he confirmed that they could not actually help us. Since then, Snowflake has only held our feet to the fire for the capacity commitments we made on the commitments that they made to us pre-sale.

Lessons learned about our main use case or how Snowflake performed in those areas include that the sales representatives do not really understand the product, and that the solutions consultants basically read internal marketing materials and internal AI to tell you what to do without really knowing how the things work. A specific example is when they told us they fully support Iceberg, and then they have over three thousand words of bullet point text with a link to reference articles with all the limitations of their Iceberg support. This resulted in us having to build almost significant modifications in order to use Iceberg.

Snowflake can only be a data lake to share data with external people and external companies. Beyond that, I believe you are wasting time and money.

We have used Snowflake for three years.

Snowflake is stable.

Snowflake's scalability is such that customer support is an atrocious experience, filled with many challenges, whether it is getting a meeting to fulfill the promises they made, getting them to prepare for that meeting, or getting them to actually help or have the technical abilities to assist. We receive a lot of corporate marketing sent to us, for instance, claims that they fully support Iceberg, but ignoring the three thousand words of limitations that they do not actually support Iceberg. It was always a challenge to figure out what they really could do versus what they told us we could do. The process to use their technology was always one that was solely driven by sales and not by Snowflake's actual ability to deliver. We have encountered this repeatedly over the last three years.

Customer support is an atrocious experience, filled with many challenges, whether it is getting a meeting to fulfill the promises they made, getting them to prepare for that meeting, or getting them to actually help or have the technical abilities to assist.

We did evaluate a couple of other databases and data lakes before choosing Snowflake. I cannot remember what they were, but this was back when we were on Azure as well.

Deploying Snowflake in my environment is really easy to set up. If you want it to do sophisticated things for a SaaS company, it does not do them.

Snowflake's configuration process is straightforward and very easy.

In the end, Snowflake cost us more money. One of the things they said they could automate or improve for us was the ingestion of data and eliminate our Databricks requirements. In the end, we did not move to Snowflake to do ingestion, but they admitted that they could not actually eliminate any steps and would actually add steps to the process and add costs to the project. Snowflake undoubtedly cost us more money and provided almost no value to us.

We find the metering and billing experience for Snowflake to be expensive because there are many limitations to what it can do. When we compared performance-wise and cost-wise to Bedrock for some of the AI work we are trying to do, Bedrock was much more performant and better priced.

We found Snowflake's Cortex tools and some of their AI tools to be not as good as Bedrock and much more expensive and slower.

My advice to others looking into using Snowflake is to look at DuckLabs and other tools. I would not recommend Snowflake on any level.

I purchased Snowflake through the AWS Marketplace. I have given this review a rating of one out of ten.

Public Cloud

Amazon Web Services (AWS)

What do you like best about the product?

Ease of use, simple UI, simple integration.

What do you dislike about the product?

Sometimes queries can take a very long time in larger databases.

What problems is the product solving and how is that benefiting you?

Centralized database, integrating with systems that run on old integration architecture.

What do you like best about the product?

I'm in RevOps, so not 100% your typical user. I mostly use Snowflake to pull datasets and stitch things together in SQL: pipeline questions, customer data, whatever came up that week.

The main thing I like is that I can just do it myself. Write the query, share them, look at what comes back, access them via CLI, drop a quick chart on it in the worksheet to see if the trend is actually there. Before, that meant exporting to a spreadsheet or asking someone on the data team, which usually meant waiting a few days for an answer I needed that afternoon.

I don't need anyone else for the everyday questions anymore. For me that's the whole value.

What do you dislike about the product?

Nothing really, I struggled a bit with the new worksheet structure but I got the hang of it now

What problems is the product solving and how is that benefiting you?

Answered that in the first q: clear, structured, sharable, easy to use data access to revenue data, customer, product data etc.

What do you like best about the product?

The way snowflake stores data through clod is the best part in the entire application where all the beginners also can use this snowflake application in easy way without confusion. Also AI/ Intelligence which provides all most all the answers for the user query.

What do you dislike about the product?

The part I dislike most about the Snowflake application is that it doesn’t allow an on-premises option and is only a cloud-based storage platform. Also, cost-wise it feels a bit high because it consumes more credits, and I’ve noticed low latency as well.

What problems is the product solving and how is that benefiting you?

In Snowflake, most of our integration problems have been solved through enterprise automation. It has also helped us address data storage issues in the iPaaS domain within our organization.

What do you like best about the product?

Ease of use, Everything at one place, Good functionality, Great integrations

What do you dislike about the product?

Worksheets shouldn't have been replaced, apart from that everything is good

What problems is the product solving and how is that benefiting you?

Ease of integrations from other platforms for our data

What do you like best about the product?

It is super helpful to connect the tools

What do you dislike about the product?

nothing to add. super helpful in our daily routine.

What problems is the product solving and how is that benefiting you?

connections, it quickly log in.

What do you like best about the product?

it eliminates local storage while maintaining security and safely protecting or data. It's also pretty speedy when and easy to scale. the UI is very simple to navigate and support is very good. My favorite piece though is the introduction of CoCo AI assistant.

What do you dislike about the product?

Snowflake is continuously changing and evolving so things that are disliked are generally corrected pretty quickly. The main annoyance now is remembering to switch AI models when starting new conversations with CoCo.

What problems is the product solving and how is that benefiting you?

Snowflake gets us quick access to our data and gives us the ability to dive into deficiencies and process opportunities.

What do you like best about the product?

I use Snowflake primarily for data warehousing and analytics, and I find it helps me manage large volumes of structured and semi-structured data efficiently with seamless scalability. I appreciate the separation of storage and compute, which allows me to optimize costs while maintaining performance, especially for running complex queries and supporting multiple workloads simultaneously. What I like most about Snowflake includes its ease of use, data sharing capabilities, performance, innovative features, and cloud-native design. The initial setup was fast and simple with minimal configuration, clear documentation, and smooth integrations, allowing the team to work on data pipelines and analytics right away. Additionally, Snowflake's scalability makes it easy for multiple teams to work efficiently without resource conflicts.

What do you dislike about the product?

I find that costs can spike unexpectedly, and I believe better forecasting tools would help. The advanced features have a steep learning curve, so guided workflows could ease adoption. Some integrations require custom work, and more native connectors would reduce the effort. Query optimization can be opaque, so more transparency would benefit advanced users.

What problems is the product solving and how is that benefiting you?

I use Snowflake to manage large data volumes efficiently, solving scalability challenges and performance bottlenecks. It eliminates data silos and simplifies cost management with its separation of storage and compute, facilitating complex queries and supporting multiple workloads.