Snowflake Analytics Reviews

Using Snowflake, I can say it is built as a robust solution. Also, its analytical functioning is really quick.

Using Snowflake to load data from different sources is a valuable feature of the solution.

The solution needs to consider including some updates in the future. There is a need to add some new features to the solution to facilitate my work with Snowflake.

I have been using Snowflake Analytics for a year and two months. I'm using it because I'm a data engineer, and so I am a customer of Snowflake.

In my experience, I didn't face any crashes or anything related to data. So I think it's stable.

It's a scalable solution because you can analyze a huge amount of data in the solution. In my company, around ten developers are using the solution.

The initial setup is easy because it is a cloud solution. So you don't need to install anything. You can just connect to your account and write SQL queries or write the procedures. So you can manipulate it on the website directly, and also it's user-friendly.

The solution has some gold and premium plans, which I read in its documentation.

I would tell others to use the solution if it is available for them on the cloud since I think it's very easy to manipulate data and work with it. Also, it is not very difficult because it is based on SQL queries, JavaScript, and Python.

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

We use the product to generate insights. 

I am impressed with the product's data-sharing feature. 

The tool should support EIM use cases. I guess the product is already working on it. I look forward to seeing inbuilt AI generative tools in the solution's future releases. The tool's price can be a little lower. The solution's on-premises support is also very limited. We have to rely on other support services to deploy it on-premises. 

I have been working with the solution for four years. 

I would rate the product's stability a nine out of ten since it is stable and reliable. 

I would rate the tool's scalability a nine out of ten. My company has more than 1000 users for the tool. 

The solution's support responds fast. 

Positive

I would rate the solution's setup a nine out of ten. Being a SaaS product, the setup is straightforward. The solution's setup becomes complex only when you need to create a specific cloud platform which is virtual or public. If you have a CI/CD pipeline ready, then deployment can be automated. The tool's deployment doesn't take more than a day to complete. 

The solution's price is high and I would rate it an eight out of ten. 

I would rate it an eight out of ten. The solution is very easy to use. The tool is a SaaS product. You can leverage your overall data analytics strategy with the solution. It has a lot of features and you shouldn't consider it for data storage alone. In the future, we will be able to leverage the solution of ML-oriented used cases as well. 

We use the Snowflake platform for analytics and for data integration. We are partners with Snowflake. I'm the managing director. 

The solution auto-scales and provides concurrency. You can have different virtual warehouses without them impacting each other. It's flexible and infinitely scalable because it's on the cloud. 

For modeling of data as well as for AI and predictive analytics, they need to improve their integration into different Python and Jupyter notebooks. That is lacking but I understand they're working on it. 

The solution is stable. 

The solution is easily scalable. 

Technical support is good, they'll escalate tickets if need be and they'll get engineers on board if that's what's required. 

The initial setup is straightforward, just click the button. 

The cost depends on the size and on what you're using. I've had customers who spend a million dollars a year and customers that spend $20,000 a year. If you're not using it, it shuts off and you're not paying anything. 

I would recommend starting small and growing into it. Don't overbuy. If you're going to get discounts and you're going to buy a block of credits for the year, make sure you need those blocks because you're not going to peak until the second or third year of implementation. It's easier than a traditional environment and allows for focus on the value that can be provided.

I rate this solution 10 out of 10. 

Snowflake Analytics is used for data warehousing, reporting, and analytics. 

The most valuable feature of Snowflake Analytics is its performance.

Snowflake Analytics can improve the integration with machine learning tools and AI and it will make the solution more usable.

In a future release of Snowflake Analytics, transactional processing should be supported natively within the solution. The transactional process means, for example, you go to a bank, you draw money, you deposit money. These are all real-time transactions. Thousands or millions of people who draw money and deposit money participate in using transactional systems, such as ATM withdrawals. We want this to be supported by the Snowflake system.

I have been using Snowflake Analytics for approximately five years.

Snowflake Analytics has high availability.

The scalability of Snowflake Analytics is excellent.

We have approximately 1,000 users using this solution.

The technical support from Snowflake Analytics is outstanding.

The initial setup of Snowflake Analytics is easy. It took three to six months to do the entire deployment.

I rate the initial setup of Snowflake Analytics an eight out of ten.

There are 10 to 15 people required for the implementation of Snowflake Analytics. We did the implementation ourselves.

We use two to three people that support the solution. However, we have full support from Snowflake.

The cost of Snowflake Analytics is low, any small organization can use it.

I rate the price of Snowflake Analytics a four out of five.

I can advise using Snowflake Analytics only if you have your analytical use case or data warehousing need, where you have large data volumes. The use of Snowflake Analytics with smaller volumes and for transactional systems will not help organizations at this point in time.

I rate Snowflake Analytics a nine out of ten.

One of the valuable features is the solution’s time travel capability. The loading process exhibits characteristics of low coding for similar loads. Unlike before, where extensive scripting was necessary, Snowflake Analytics simplifies tasks such as file exposure through SQL, a familiar and flexible language for all. Even common commands like the copy command become more straightforward, making coding in this solution more accessible and efficient.

The solution’s interface is good but it could be improved.

I have worked with the solution for almost three years.

The solution is highly stable.

The solution is highly scalable. We cater to enterprise businesses. I rate it a perfect ten.

The initial setup is straightforward, and the deployment process is quick and efficient.

The solution's pricing is affordable.

I recommend the solution. Overall, I rate it a perfect ten.

Snowflake replaces enterprise data warehouses that have traditionally been on-premise and had to be managed by the enterprises themselves. We are a services company and we create these data platforms for our enterprise customers in a variety of industries including health care, education, travel and tourism. We create and build services around Snowflake and part of my job is to assess its suitability for our enterprise customers. We are partner of Snowflake. 

The solution is completely managed and that's fantastic. Its a true SaaS kind of Data platform. You can create analytical products as a SaaS offering, nothing needs to be installed and no licenses needed. The solution can run on AWS, Azure and on Google Cloud and you can choose the underlying cloud on which Snowflake creates your databases, uses different services, stores your accounts and stores your data. It offers high flexibility. It offers computation completely decoupled from the storage so you pay fees for computation cost only, and storage is very very cheap. For example, within the enterprise data warehouse, if the total compute time is two hours a day but the traditional setup has to be maintained and managed round the clock, and the data also has to be permanently stored, them moving to Snowflake would ensure that the enterprise pay much less for storage, and pay for only 2 hours of computation. Performance-wise also it's a good solution. 

In your data platform, if you have a lot of computations, Snowflake can becomes costlier with. There can be some complexity when designing your warehouses or enterprise data, but that is based on your requirements and not the underlying warehouse that you choose.

I've been using this solution for about 4-5 years.

It's extremely stable because it's backed up, load-balanced and has the elasticity that the underlying cloud (AWS, Azure, GCP) provides. 

The solution is scalable and has the concept of creating virtual resources, so as an example, I can buy 10 gigabytes of data capacity, then decide to process that data every day, Snowflake then takes care of creating multiple databases/storages, and scales by itself. I don't need any calculations of load or throughput worked out beforehand. For most other similar products, such as RedShift or Azure Synapse, you have to work out your average load and peak loads beforehand , and provision the warehouse and other services. In Snowflake, you don't need to do that, it auto-scales up and down.

Customer service has and support are at par with any professional Data Platform service.

Positive

The initial setup is straightforward. You buy an account, and add the number of accounts you need, and that's it. It takes about five minutes. 

The solution can become expensive when you increase your computation. So one has to design the solution very carefully and optimally. Leverage it heavily for Storage, but for compute, make sure the resources (e.g. Virtual Warehouses, Snowpipe, Streams, Tasks etc.) as well as the transformations are used in the most optimum way. For example, during the design time, the technical team has to wisely think if your data marts can be Views or Tables (View consumes compute, but Tables consume Storage), how frequently your data marts need an update, whether you should write lesser but complex queries of more but simpler transformation queries and stored procedures and many other things. But its ease of use, decoupling of Compute from Storage, ability to share data, and highly managed behavior make it worth the money.

I highly recommend Snowflake wherever possible. 

I rate this solution 9 out of 10. 

We use this solution to move on-prem data to a cloud data warehouse where we have a requirement for more computation storage or when we need a plugin to work with specific technologies like ML, AI and Data Science. 

The advanced features like time travel, zero copy cloning and scalability have been most useful. Snowflake requires zero maintenance for Data Warehousing on the cloud system. It is more secure compared to any other data platforms that we have used. The tri secret key is the feature that gives a lot of confidence to customers. 

Moving data from legacy systems to Snowflake is not that easy. There are some cases where processors are not actually compatible with Snowflake. If Snowflake could offer compatible versions of other legacy system objects, that would be more convenient for anyone to move to Snowflake. 

When comparing MySQL with Snowflake, MySQL is faster in executing the complex queries compared to Snowflake. This is the only factor where Snowflake lags behind competitors in the market.

I have been using this solution for four and a half years. 

This is a scalable solution.

The customer service and support for this solution is good. We had previously failed to retrieve data after the time travel period. We had to raise a ticket with technical support team and they helped us. They charged us for this but were able to help us retrieve it.

Positive

The initial setup is straightforward. The beauty of Snowflake is that you can select the configuration you need. For example, if you have a requirement of a one terabyte load with high availability and 10 users, Snowflake will set it up for you to match these requirements. 

The ROI you receive using this solution depends on your use case and if you are starting from scratch or migrating from a legacy system. We have experienced a 40 to 50% improvement in ROI compared to legacy systems.

When using Snowflake, you pay based on your usage. They calculate how much CPU has been used. If you use excess warehouse storage, you are charged one credit per hour. If you are in Asia, you are charged $3 per credit. If you have 10 users running parallel with the same excess, you will be charged $30. 

Pricing for storage is calculated in the same way. You will pay upfront for the storage that you know you will need to consume. There is this option of prepaid consumption as well as post paid consumption. If you pay upfront, you will charged $20 per hour, per terabyte. If you paying after use, you will be charged $40 per terabyte.

There is close to zero maintenance required for this solution. 

You don't need to use traditional warehouses, vacuuming or indexing. A database administrator or system admin is not required.

I would rate this solution a nine out of ten. 

We wanted a solution which is on cloud and gives us data warehousing and analytics capabilities, so these are our use cases for Snowflake Analytics.

Snowflake Analytics improved our organization because it was capable of doing what we needed. They are also coming up with more characteristics or more features, but at this point in time, what's currently available in the solution is very convenient for us to use. We've incorporated the features of Snowflake Analytics according to our business needs.

What we found most valuable in Snowflake Analytics are its attributes that are very convenient for business use such as data sharing, cloning, time travel, and fail-safe. It's a good product all in all.

A room for improvement in Snowflake Analytics is Spark, particularly its connector for Spark.

An additional feature I'd like to see in the next release of the solution is built-in analytics.

I've been using Snowflake Analytics for more than two years, and I've used it within the last twelve months.

Snowflake Analytics is a stable solution.

Snowflake Analytics is a completely scalable solution.

The technical support for Snowflake Analytics is responsive, and on a scale of one to five, I'm rating support a four.

The initial setup for Snowflake Analytics was easy. The deployment time for the solution was, on average, five to six hours, and it was dependent on the user history created for a particular sprint.

As of now, I have not seen ROI from Snowflake Analytics.

I'm unable to give information about the licensing fees for Snowflake Analytics because it's the infrastructure team who takes care of licensing completely.

I have experience with Snowflake Analytics, and I'm using its latest version. The solution is deployed on the public cloud, on AWS.

There are two users of the solution within the company, and we have two people, particularly IT analysts, in charge of the deployment and maintenance of Snowflake Analytics. There's a plan to increase usage of the solution within the company.

My advice to others looking into implementing Snowflake Analytics is that if you are looking for a stable solution with good features for upcoming business problems, you can go with cloud data warehouse technology such as this solution.

I would rate Snowflake Analytics eight out of ten. I'm not giving it a perfect score because there is always room for improvement, for example, built-in analytics and its Spark connector.

My company is a partner of the vendor and implements Snowflake Analytics.