Snowflake Analytics Reviews

I primarily use it in the realm of machine learning products and general data analytics.

What I find most valuable about Snowflake is that it is an all-in-one platform that provides the capabilities needed for various analytics tasks, including data warehousing for machine learning. It is incredibly user-friendly and requires minimal maintenance, making it a convenient choice for handling data analytics and machine learning workloads efficiently.

One area that could benefit from enhancement is the user interface for more visual ESM features. Improving the visual representation of security components, like boxes and links, could make it easier to understand and manage security aspects within the platform.

I have been using Snowflake Analytics for three years.

It is a quite stable solution. I would rate it as a ten out of ten for stability.

Snowflake is scalable, but its limits are connected to the cloud resources it uses. These cloud resources can restrict how much data you can handle and store, affecting the extent of scalability. I would rate it as a ten out of ten for scalability.

I find Snowflake's technical support to be highly responsive. They are exceptionally quick to address issues, especially when they are critical, with response times typically less than thirteen minutes. I would rate the support as a nine out of ten.

Positive

Snowflake is a fully cloud-based service that doesn't require installation or complex configuration. It is an online platform where you define parameters and create users, tables, and objects without the need for extensive maintenance or operational tasks. Setting up Snowflake is typically a quick process, taking just a matter of minutes to prepare for use.

Snowflake can save a considerable amount of time, with potential time savings often reaching 50% or more for various data-related tasks. However, the exact percentage depends on the specific tasks and data complexity, so it can vary.

Snowflake offers a pay-as-you-go pricing model among other options. It is an affordable solution. I would give it a nine out of ten in terms of affordability.

Overall, I would rate Snowflake Analytics as a ten out of ten.

My company uses Snowflake Analytics for data lake, data warehouse, data mart, and other related stuff.

Features like the fact that the solution is very fast and available on the cloud are some of the valuable attributes of the solution.

We are satisfied with the solution since Snowflake brings in a lot of new features now and then.

The solution's high price can be an area of concern that needs improvement.

I have been using Snowflake Analytics for four years. My company has a partnership with Snowflake Analytics. I am using the solution's latest version.

Scalability-wise, I rate the solution a nine out of ten.

Around 50 users use the solution in my company.

Our company's clients consist of medium and enterprise-sized businesses.

The support provided by Snowflake is fine since they are fast and helpful. I rate the technical support an eight out of ten.

Positive

I rate the setup phase a nine or ten on a scale of one to ten, where one is difficult and ten is easy. The solution's setup phase was easy.

The solution is deployed on the cloud.

The deployment time taken for the solution was six months.

There was nothing much involved in the deployment process. The deployment process involved just exporting the base objects.

On a scale of one to ten, where one is a low price, and ten is a high price, I rate the pricing a seven. The solution's pricing is high.

There are no costs in addition to its standard licensing price.

After considering the pricing part of the solution, those planning to use it can start using it. The pricing of the solution falls on the higher side. Feature-wise, the solution is really good. If a company can afford the tool, the solution is good in its category.

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

We use it for our full application operational analytics, including building, storing, and analyzing the data. It provides us with detailed insight, making it easy to extract needed reports. Everything related to business intelligence, whether it's reporting or analytical modules, is stored on the Snowflake Analytics platform.

It's an intuitive platform that allows organizations to rapidly adapt, providing the ability to quickly analyze and make data-driven decisions. It ensures the optimization of the application development while maintaining the user-friendly nature of its UI.

Machine learning should be improved. Some of the solutions, such as Databricks, offer an option to directly write and integrate Python code on the spot, but Snowflake doesn't have that feature yet. Adding it would be useful, as it would facilitate building models.

I have been working with Snowflake Analytics for a year.

It is a stable platform.

It offers good scalability.

I am highly satisfied with their level of customer service and support. I would rate it nine out of ten.

Positive

The initial setup was done seamlessly.

There was no requirement for installation. They provided us with a link, giving us access to the cloud.

It is an expensive solution, but the kind of usability and flexibility it proactively provides for the organizations justify the price.

Snowflake Analytics is a really good choice for organizations that are looking at a cloud analytics platform for reporting, data modeling, and advanced analytics without machine learning requirements.

We use Snowflake Analytics for programs where we write stored procedures which are imported into tables and later used for generating reports via reporting tools.

Snowflake Analytics is flexible in nature, allowing for the addition of more data tables without affecting the main structure. It supports scalability by enabling the scaling of resources up and down depending on demand, which is facilitated through cloud computing platforms. 

Snowflake also helps in improving cost savings. Its structure aids in storing, managing, and analyzing large datasets in the cloud, using various relationships like one-to-one and many-to-one, ensuring consistency in the data structure.

Improvements are needed in importing and exporting large datasets to and from reporting tools. A better recording system for tracking changes to the data structure, including user IDs and timestamps, is needed for increased transparency.

I have used Snowflake Analytics for one year.

During the development stage, there were many maintenance issues, however, these were minimized over time.

Snowflake Analytics is highly scalable as data can be added to the structure without necessitating comprehensive changes. It handles scaling effectively, particularly in a cloud-based environment.

We did not need to escalate many questions to tech support compared to other tools, indicating minimal reliance on Snowflake's tech support.

Positive

I have experience with AWS as a cloud data warehouse.

The initial setup involved creating a Snowflake account and installing the ODBC driver, taking a couple of days. Moving from development to production took 24 continuous hours.

Four people were involved, with a lead taking charge, while I supported the task.

By using clones, we allowed multiple teams to work simultaneously on data without affecting the main dataset. This saved time and maintained consistency, though I am not sure about the financial aspects.

The project managers handled pricing. We minimized time requirements by using indexes and constraints. Clones and servers were used efficiently to address cost issues.

Snowflake requires skilled developers, unlike other tools. It is suitable for managing large, structured, and analyzable datasets. 

I would rate Snowflake eight out of ten.

In one project, the vendor also uses Snowflake for analytics, housing the data within their Snowflake instance. Previously, we retrieved this data via FTP and then employed a Python program to read and load it into our tables. However, by leveraging Snowflake, we can now directly access this data.

Snowflake provides multiple development tools. You can build applications with Snowflake and integrate them with Java and Python. You can write code in Python and Java and use secure features.

Some functionalities available through SQL are inaccessible in Python or Java within Snowflake. Snowflake must enhance its support for these programming languages to offer full flexibility and capacity, allowing developers to use them.

Currently, there are restrictions. For example, using Spark Engine with Snowflake allows you to write code but not call external functions or packages. Snowflake must improve its programmatic capabilities to support better engineers and developers, especially those transitioning from big data technologies.

I have been using Snowflake Analytics for two years.

Snowflake Analytics offers scalable data warehousing solutions that can effectively handle large volumes of data. Increasing the capacity of the data warehouse can enhance its scalability, making it more adept at capturing and managing big data.

The organization is completely focused on moving its processes and data to Snowflake. Once this is done and the business starts consuming and extracting data from Snowflake, value realization will occur.

Initially, we began with our SaaS platform on AWS. When transitioning data from AWS to Snowflake, we found a seamless integration between the two platforms, making it easier to access data from Snowflake. This integration retrieves and uses data, contributing to the wide acceptance and adoption of Snowflake Analytics.

The data creation and consumption teams have separate roles, allowing for a clear delineation of responsibilities. This ensures the consuming team only accesses the products or data relevant to their tasks. We maintain efficient data management and security by creating roles for the consumer team and providing access accordingly.

Data-driven decisions have improved significantly. Previously, we had multiple small copies of the data scattered across different locations. With Snowflake, we've consolidated everything into one golden copy of the data. Someone who needs a subset of that data can create views or access specific subsets directly. This setup allows us to track who has access to the data and its origin. Consumers don't need to go to multiple places to get the data. They know the central location to access or request the data and the available data products. This centralization will help in making more informed, data-driven decisions.

I recommend Snowflake Analytics if you are looking for a reliable and well-regarded solution. It has gained significant traction in the market. Snowflake is an excellent choice for analytics purposes. While companies need to research other available tools, Snowflake is a good option.

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

I like the integrations available in the product. We can send the data to our data warehouse if we want to transform it. I like the cloud solution. It's not necessary to use a physical server.

The UI must be improved. It would be pretty helpful to have some images showing what we are doing and how we use the tool.

I have been using the solution for two years.

I rate the tool’s stability a nine out of ten. We don't have any problem with data orchestration or data transformation. It’s pretty good.

I rate the tool’s scalability a nine out of ten. The tool is dedicated to the data team. Around 20 to 30 people use the product in our organization.

I have used Redshift and other data warehouses from Google. I used them to extract data in Oracle for 18 years while working in a telecom company. I prefer Snowflake over the others.

The initial setup was a little bit hard. It was especially hard to create a connection with the product team. The product team generated the data, and we had to create some tasks before they were sent to Snowflake. It was difficult because everyone was pretty new to the solution and did not have the necessary knowledge then. The setup was not intuitive. By the end, we understood how to deploy it. The product is deployed on the cloud. The initial deployment took one or two weeks. Later, it takes two or three days to change or update things.

We implemented the product in-house.

Using the product is more about our perception and technical skills in data modeling and querying. If we use it well, the tool runs well more than 90% of the time. It’s easy to use the solution because it has everything that we need. It performs well. People who want to use the solution must create a demo account and try using it. We must optimize our SQL queries. It is helpful to understand the volume of our data before writing codes. Overall, I rate the tool a ten 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. 

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.