Snowflake Reviews

We use the solution to build the pipelines in stream sets, including data source, data warehouse, and destination endpoints.

The solution's most valuable features are storage, run time, scalability, and minimum query time compared to other vendors.

The solution's stability needs improvement.

I have been using the solution for seven or eight months.

Recently, I encountered an issue with the solution's data warehouse. The resource monitor had exceeded its quota. I rate its stability as an eight.

I rate the solution's scalability as a nine.

We use Hive and Hadoop as well. Snowflake is more stable and scalable.

The solution is more straightforward to use than the other IDBMS tools. It has a simple query process. Its computing time is less as well. One can easily have access to it. I rate it as a nine.

The platform's most valuable features include its ability to effectively summarize and manage large datasets, allowing multiple teams to analyze and generate insights. Its integration with data lakes for business impact analysis, performance metrics, and KPIs is particularly important.

Improvement is needed in integrating external tools, such as data catalogs, which can be complicated due to differing formats and usage across departments. The goal is to enhance collaboration and streamline workflows.

The product's scalability is crucial for managing petabyte-scale data generated daily across various regions, allowing for efficient data validation and handling.

The primary challenges during the initial setup were the high pricing and uncertainties regarding future costs associated with data usage. 

The deployment involved consultation among managers, agreement on on-site requirements, scale calculations, and collaboration with engineers for setup approval.

I rate the process a seven out of ten. 

Snowflake is integrated through a complex workflow that involves collecting data on the publisher side, using tools like Airflow and Kafka for batch jobs, and frequently importing data into the product from various sources, including S3 and Data Lakes. It creates a smooth data pipeline.

I rate it a seven out of ten. 

The solution has use cases related to retail stores and sales.

The most valuable features of Snowflake are that you have to pay per usage, and you don't have to worry about the maintenance of the data warehouse because it is on the cloud.

The solution’s pricing could be cheaper. It would be helpful if Snowflake could create good reports instead of using Power BI reports.

I rate the solution a nine out of ten for stability.

Snowflake is a scalable solution. We have four to five customers for Snowflake who use it regularly.

The solution’s initial setup is straightforward.

The solution's deployment in a development environment takes only a couple of minutes.

Users have to pay a licensing fee for the solution, which is expensive.

Snowflake is deployed on the cloud. The solution is providing HIPAA compliance, which is sufficient. Users looking for a pay-as-you-use product available on Azure or AWS should consider Snowflake.

Overall, I rate the solution an eight out of ten.

We're using Snowflake for Power BI Cloud. We had a cloud version of Snowflake, so we were connecting to the Snowflake database and importing required tables into our system, Power BI Desktop. From there, we linked those tables and created a semantic layer, an internal layer between the frontend and backend, and then we tuned the data. Then we used both the tables to tie into the dashboards that we developed. The dashboards show the sales information or marketing information.

It's a cloud solution.

I like the entire database. This is the advanced version of the cloud version, so it's really a flexible tool. If you have it implemented at home, you can access it from anywhere.

Sometimes when I'm trying to refresh the data, my different application or tool has to connect to its backend database through the connection I create. Sometimes, I face some issues like not having permissions. These aren't as crucial, but there are common errors sometimes where the database is down, or a table is nullified and a new table is added and you are not given access to that. With those errors, you don't have permissions.

I have been using Snowflake for a year.

It's stable.

The scalability is good. It handles a lot of data, and the processing speed is very high.

It's straightforward. You have to have a rule, database names, and a schema name.

When somebody deploys it and gives me the URL and the required tables to use, I use the URL and configure it from the frontend side, reporting side that could be more like Power BI or Tableau, and I start using it.

I would rate this solution 8 out of 10.

We are also using Apigee we have various consumption patterns, data enrichment, and few shedding of the data, and everything goes into Snowflake. If it is multiple consumers, it goes into AMQ, Kafka, or multiple streams to consume. There are specific APIs that we offer after we send the data into the S3 bucket. We have Apigee APIs for consumption, and there are three to four different patterns. For example, we enrich the data, flatten it, and structure everything before the customers going to go into Snowflake. 

There are going to be specific clients who need specific data from the overall data lake, those are going to be exposed as APIs. We have multiple customers needing the same data and for this, we move them into the streaming Kafka.

Apigee does not communicate directly with Snowflake. We have data registration, and everything is coming into something that is called the trusted bucket. The  Apigee interface API is written off the S3 bucket. The S3 bucket data is moved into the Delta Lake, and where the data are stored from the Delta Lake, it sends it to Snowflake. We have Apigee going to Delta Lake and S3 bucket, but  Apigee does not go to Snowflake, these are two areas where it goes to. 

We have Kafka consuming directly off Delta Lake, and it sends data to Kafka through the AMQ. We have its setup, and we have interfaces that come directly to Snowflake to pull the data. It is then flattened and enriched, and it is used for many purposes, such as reporting.

Snowflake's most valuable features are data enrichment and flattening.

I have used Snowflake within the last 12 months.

The complexity of the initial setup of Snowflake depends on the use case. However, Snowflake itself, we don't set it up. The difficulty comes from the ingestion patterns, depending on what data I'm putting in, what kind of enrichment, and what additional value we have to add. However, it does tend to get complex because we have a lot of semi-structured data which we need to handle in Snowflake. There have been some challenges.

Snowflake has multiple implementations. For example, it can be implemented on Amazon AWS and on-premise. The data between these two cannot work together because they have different time zones. That's where the integration can be difficult because it is similar to them being on separate islands, they are completely separate. At some point, everything is going to go into the Amazon AWS Snowflake, but right now there are two islands that are completely different. We have to pull the data out and send it out again separately through a different pipeline.

In the future, this type of implementation should be easier. The integration could be better. 

I rate Snowflake an eight out of ten.

We use the solution for a data warehouse and we generating reports and dashboards.

Snowflake has improved my organization because of its high performance compared to the old way we used to operate with Microsoft SQL Server. We are migrating everything from SQL Server to Snowflake. It used to take a lot of time to query the database but now it is done a lot faster, we receive millions and billions of reports. This is a major benefit because it is our major use case.

The most valuable features are sharing data, Time Travel, Zero Copy Cloning, performance, and speed.

The solution is very easy to run the queries. We have a built-in query optimizer in Snowflake that works very well.

The UI could improve because sometimes in the security query the UI freezes. We then have to close the window and restart.

There should be an IDE concept similar to the Java IDE or Eclipse feature. I should be able to see all of the functions available on a particular object. Every time we need to go to the Snowflake documentation and look if there are any methods we need. It is hard to remember everything, go and search, and use that that eventually found method. If it was possible to list out all the methods and functions available in an object that would help the developer's a lot.

In an upcoming release, we should be able to send or receive data from external systems but this is not able to be done. There should be built-in logging and monitoring features, we should not need to be dependant on third-party solutions, such as Splunk. There should be more DevOps features to reduce the usage of third-party tools. If these features were part of Snowflake it would be a fully functional complete solution.

I have been using Snowflake for approximately two and a half years. 

They claim zero maintenance support and from my experience, I would agree with that statement. When I was on a previous project we had a lot of support for the Netezza platform we were using. We had approximately twelve people, three onsite and seven offshore. When we migrated from Netezza to Snowflake we reduced the number of people required and kept only some of the team as developers. There is very little support required for this solution. Stability is very good in SnowFlake.

The scalability is built into this solution as being on the cloud. It is able to scale in all directions. Additionally, they have a multi-cluster warehouse, and based on the business use case it is very good.

There are approximately 4,000 portals. However, we do not know how many users our clients have that are using their portals.

We are building new data warehouses and we are migrating from SQL Server to Snowflake.

The support is very good. We create tickets and they respond with a solution.

We were using SQL Server previously and we switched because of the increased performance, multi-clustered shared environment, scalability, and we wanted to use a cloud-based solution.

Everything with the installation went smoothly. I believe when I joined the company Snowflake was already here. They bought the Business Edition that is encrypted everywhere because they are a financial insurance company and most of them choose the Business Edition because of the security.

The company I work for used SnowFlake integrators for implementation assistance.

I have evaluated Eclipse and IBM Netezza.

The solution is very easy and flexible to integrate with any type of API.

I rate Snowflake a nine out of ten.

It is used in my company as well as in my client's company. We are a system integrator, so naturally, we need to have the centers of excellence and competencies in Snowflake.

The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management.

It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure.

There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm.

The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical.

The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was.

I have been using this solution for close to three years. I kept a tab on Snowflake and its progress since it came into the market.

Personally, I have worked extensively with Oracle, SQL Server, and Teradata. SQL Server has the Fast Track Data Warehouse (FTDW) appliance. Oracle has both the database and the appliance. I haven't worked on Parallel Data Warehouse, which is a big one offered by Oracle. Teradata is an appliance in itself. There is also Metadata. I haven't worked on DB2. 

All of these had their own lacunae. Data warehouses had their own problems. There were failures, challenges, and difficulties in adoption, and all of these have been addressed by Snowflake a big way. It has tried to marry the best of both worlds in terms of turnaround time, scalability, adoption, and seamlessness.

I hail from a classical data warehouse background. Snowflake has been kind of a silver bullet. It is trying to meet the best of both worlds. I wish I could do much more on Snowflake, but I'm tied up with many other things, which is why I'm not able to concentrate that much, but it is an exceptionally good technology.

Its initial setup is very simple, which is its plus point. It is not at all a problem. You only need to understand a bit of the cloud ecosystem. When Snowflake is on Azure or AWS, you need to understand

• What exactly is happening?
• How these two are handshaking with each other?
• What part Snowflake is playing?
• How Azure or AWS is complementing it?

If these things are clear, the rest shouldn't be a problem.

This could be something that might be debated upon, but Snowflake has two parts to it. One is the data warehouse itself, and the other one is the cloud. It is important to know about the cloud in terms of:

• How a cloud functions?
• How a cloud orchestrates through its services, domains, invocation of services, and other things?
• How a cloud is laid out?

For example, let's take AWS. If AWS is invoking Lambda or something else, how will S3 come into the picture? Is there a role of DynamoDB? If you're using DynamoDB, how would you use it in the Snowflake landscape? So, cloud nuances are involved when we speak of Snowflake, and there is no doubt about that, but a more important area on which Snowflake consultants need to focus on is the core data warehousing and BI principles. This is where I feel the genesis of Snowflake has happened. It is the data warehouse on the cloud, and it addresses the challenges that on-prem databases had in the past, such as scalability, turnaround times, reusability, adoption, and cost, but the genesis, principles, and tenets of data warehousing are still sacrosanct and hold good. Therefore, you need the knowledge or background of what a data warehouse is expected to be, be it any school of thought such as Inmon school, a Kimball school, or a mix. You should know:

• Data warehouse as a discipline.
• The reason why it was born.
• The expectations out of it in the past.
• The current expectations.
• What being on the cloud would solve?

These things on the data warehouse side need to be crystal clear. The cloud part is important, but it is of lesser essence than the data warehouse part. That's what I see, personally, and I guess that's the way the Snowflake founders have built the product.

As a data warehouse, I would rate Snowflake an eight out of ten.

I have used it in my previous company. It was just a SQL server data warehouse using reporting tools on top of it. It was an on-premise SQL server environment, and it was a typical data warehouse use case, but we wanted to do things faster and more cost-effectively. 

We used it to modernize our data warehouse. We didn't want to invest more in on-premise servers, and we were looking for a way to quickly get more data joined together. 

It had definitely improved the way our organization functioned at the time.

The overall ecosystem was easy to manage. Given that we weren't a very highly technical group, it was preferable to other things we looked at because it could do all of the cloud tunings. It can tune your data warehouse to an appropriate size for controlled billing, resume and sleep functions, and all such things. It was much more simple than doing native Azure or AWS development. 

It was stable, and their support was also perfect. It was also very easy to deploy. It was one of those rare times where they did exactly what they said they could do.

Their strategy is just to leverage what you've got and put Snowflake in the middle. It does work well with other tools. You have to buy a separate reporting tool and a separate data loading tool, whereas, in some platforms, these tools are baked in. In the long-term, they'll need to add more direct partnerships to the ecosystem so that it's not like adding on tools around Snowflake to make it work. They can also consider including Snowflake native reporting tools versus partnering with other reporting tools. It would kind of change where they sit in the market.

I have been using this solution for about three years.

We didn't run into anything. We had outages for a couple of seconds, but they were related to Amazon or AWS. They weren't related to Snowflake.

We scaled it a little bit. We didn't have a lot of data to scale, as a lot of companies do. We only had a couple of terabytes of data, which is insignificant for a cloud platform. 

The development team had three or four people getting data in. Then report people were also using the platform, but they didn't really have to know that it was Snowflake because they were going at it through a reporting tool. There were probably 30 or 40 people writing queries against our reporting tools, which were, in turn, using Snowflake.

They were really good. They were very responsive. There were never any issues with them. I would give them a ten out of ten.

I've used a lot of different data warehousing solutions at different companies.

It was easy as pie. In a couple of hours, it was up and running, and we were loading the data in. We had a fairly senior developer for that. He knew SQL server and queries very well. If you're used to developing in any type of SQL environment, you can jump in and use Snowflake really quickly.

It is per credit. It has a use-it-as-you-go model. We bought a chunk of 20,000 credits, and they were lasting us for at least a year. We didn't have the scale of data like a much larger company to consume more credits. For us, it was very inexpensive.

Their strategy is just to leverage what you've got and put Snowflake in the middle. It doesn't make it expensive because most of the organizations already have reporting tools. Now, if you were starting from scratch, it might be cheaper to go a different way.

If time to value is your primary goal, then I would recommend going for Snowflake over one of the other cloud providers.

I would rate Snowflake a ten out of ten. It is one of the few products in which everything demos well. It actually did everything they showed in the demos. We really couldn't find any gotchas in it. It kind of delivered as promised.