Snowflake Analytics Reviews

We use it in the process of collecting access revenue data from various vendors. Our next step involves integrating this data into Snowflake tables, after which we present it through dashboards for visualization and analysis.

Snowflake Analytics serves as the primary data repository, fulfilling the data warehousing requirements of our customers. Therefore, all incoming data from our vendors is stored within Snowflake as part of our data warehouse.

Snowflake Analytics efficiently handles large-scale data analytics, which has been crucial for managing our current database volume. We're satisfied with the refresh rate and the loading process of new data into the tables, as it aligns with industry standards, leaving us with no complaints in this regard.

We have designated specific roles for administrative tasks, which are concealed from regular developers and production support personnel. Access control mechanisms are employed to grant access only to individuals who require it for administrative purposes, and they are the biggest benefits.

The transition to Snowflake has impacted our customers' overall data strategy and cost management significantly. Initially, we were using Redshift with AWS integration, but encountered challenges with scaling and compute costs. High data volumes led to increased costs for the entire solution. Therefore, we migrated to Snowflake, resulting in a reduction of costs by approximately thirty percent.

The most valuable features primarily revolve around data ingestion, such as copy commands. Additionally, we find Streams to be particularly useful in our operations, as they provide convenient functionalities.

The Snowflake features I find most beneficial for data analysis are primarily related to analytics, particularly their features like materialized views and queues, which are especially useful for dashboarding purposes. Another noteworthy addition is their new solution, hybrid tables.

The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms.

I have been working with it for three years.

In terms of stability, over the past three years, we haven't experienced any glitches in the production environment such as database unavailability. We have had a consistent and reliable performance without any issues, so there are no complaints in this regard. I would rate it eight out of ten.

It provides excellent scalability. I would rate it eight out of ten. The majority of our current clientele utilizes Snowflake, comprising approximately fifty to sixty percent of our client base. The rest of them still operate on legacy systems, with a gradual migration process underway. Our customers are enterprise businesses. 

If I were to rate the support on a scale of one to ten, it would fall around six. Occasionally, there are delays in receiving responses, while at other times, we receive prompt assistance.

Neutral

The initial setup is complex and can be challenging to configure. I would rate it three out of ten.

For a medium-complexity dashboard, the deployment process typically takes around an hour to implement all the features. This estimate applies to mid-range features, excluding high-end functionalities. Our deployment process utilizes Git actions, seamlessly integrated within Git. Each deployment involves creating a pull request, through which Git actions are triggered to deploy changes to the master branch.

The pricing is on the higher side. I would rate it seven out of ten.

Since Snowflake primarily functions as a data warehouse, it is well-suited for OLAP solutions. Despite incorporating some OLTP features, Snowflake remains best suited for OLAP purposes. My advice to anyone considering Snowflake is to carefully evaluate the cost in comparison to their current solution. They should assess whether they would derive significant benefits from switching to Snowflake, especially if they are currently using platforms like Databricks or BigQuery. Comparing costs beforehand is crucial before making the transition to Snowflake. Overall, I would rate it seven out of ten.

We have chosen Snowflake to meet customer expectations. Some customers expect a partnership, while others primarily use migrations. They are using streaming data from an on-premises tool. The customer uses an existing system and partners with on-premises partners; we migrate them into the cloud system.

We migrated from Oracle and Zoom to Snowflake using the AWS Group.

One of the key advancements in Snowflake Analytics is data sharing. There are real-time data and survival protection data. If you want the same data, you have to migrate it. You have to move it over to the other place. So, you can now have a copy of the data. You can use it for UAT and development activities.

The backup features are a major aspect of our use of Snowflake for streaming processes. It plays a significant role in our migration process due to the need to export customer data. While the existing system operates primarily in batch mode, the migration process for exporting customer data can now be accomplished using a streaming process.

Implementing everything on-premise is challenging because it require proper support from advisors, DBAs, and others. However, with cloud implementation, there are fewer technical barriers.

I have been using Snowflake Analytics for over a year.

The product is stable.

I rate the solution's stability as seven out of ten.

Our customer's daily transaction data amounts to 75 million records. Previously, we worked with the same customer using a red chip. They're using automatic node increase. We spend a lot of money for the storage activities.

The problem has been completely sorted out with auto-scaling. It automatically adjusts the storage capacity, which means the customer doesn't need to overspend, particularly on storage.

We have proper support. It analyzes user greetings and reports and addresses the issue.

Neutral

We are using the vendor or tenant. We can automatically connect to a source system. There are different connections available. Default connections are deferred. There are multiple source system connectors. We can use those connectors to pull data from various source systems, whether on-premise or in the cloud. Previously, the job was to use the existing built-in character to pull the data, do some budgeting, perform more strategic actions, and load it into the attack.

Initially, we need to set up applications and other necessary components; then, it becomes easier to migrate other things. On-premises setups are more rigid, making deviating from the initial plan challenging. Therefore, we cannot proceed with that approach. Instead, we can opt for the CACH model, which focuses on development and implementation, particularly in the latest tools and technologies.

Snowflake Analytics’s technical and domain aspects are crucial. Both are the most important for improving other products and adding customer value. In such cases, even a few customized elements can take us far. We have some use cases in the requirements we implemented in the process. So, we are using various technologies. The most important things we need to build are customer tools.

Many new features have been added to Snowflake, making it a completely cloud-based system. It's a modern data warehousing solution that incorporates many new technological aspects. 

I recommend Snowflake. If you opt for the red chip, you'll have to spend more on storage. You can avoid these additional costs. It supports data injection and retrieval from various sources, ensuring rapid integration. It offers seamless transition support if users migrate to other platforms like Azure or AWS. There are no limitations on promoting various concepts.

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

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.

It is a cloud data warehouse. We use the solution in our organization for setting up data warehouses, building tables, and querying from the tables.

The product lets us build tables.

Time Travel and Snowpipe are good features. The rest of the features are standard.

The tool must provide better machine-learning functionalities.

I have been using the solution for five years. I am working with the latest version of the solution.

The solution is fairly stable. It doesn't crash. We don't get any incidents in Snowflake. I rate the stability a ten out of ten.

I rate the product’s scalability a six or seven out of ten. Scaling costs a lot of money. It could be improved. Less than ten users use the product in our organization for demo purposes. We use it once a month. We do not have plans to increase the usage in the future.

The technical support is not very good. I rate the support a two or three out of ten. The tool does not provide a straight support line that we can use unless we're a very big partner. It's very hard to get support from the team. We have community support, but it's not good for big incidents.

Negative

We also use BigQuery and Redshift. We have different customers that use different data warehouses. We use Snowflake when our customers advise us to use it. Snowflake is more stable than BigQuery.

The tool is quite easy to set up. I rate the ease of setup an eight out of ten. It's a cloud-based solution. We're partners of AWS, GCP, and Azure. We use all three of them. It takes half an hour to deploy the solution. To deploy the product, we must create Snowflake accounts and set it up with the cloud provider of our choice. Then, we get an email with our access to Snowflake, and we can start using the data warehouse and do anything with it. It's quite easy to use.

The deployment can be done in-house. We need one person to deploy the tool and two or three people to maintain it. People maintaining the solution monitor the costs and ensure the warehouse is the right size.

The product must improve its pricing model. We must scale it up considerably to get a good value. The tool is quite expensive. The product must provide a pay-as-you-go pricing option. I rate the pricing an eight out of ten. If we run big workloads in Snowflake, it will be more expensive than both BigQuery and Redshift.

People considering the solution must evaluate it against all the other options to see whether they need to use it. They must look at the alternatives depending on their cloud provider. If they're on Microsoft Azure, then Snowflake is the best option. However, if they're using Google Cloud, they should consider BigQuery. If they’re on AWS, they must consider Redshift. The tool has helped us with a few customer case studies. Overall, I rate the product a seven or eight out of ten.

I use Streamlit apps and Snowflake Analytics for analytics, data warehousing, migration, creating reports, and segregation. It is used to create reports for analytics for users.

We don't have to transfer the data as Snowflake's data sharing capabilities allow us to handle data more efficiently. It stands out against the other tools in the market.

The most important feature is the warehouse functionality. Increasing the warehouse size helps cater to large amounts of data without worrying about performance issues.

When we insert data into a large table, it takes a lot of time. This performance issue can be improved.

I have been using Snowflake Analytics for almost four years. I have used it myself.

If you increase the size of the warehouse, the performance is taken care of. It scales well with large data volumes.

We don't handle the price part. However, our customer is happy with our services regarding Snowflake Analytics.

I would recommend Snowflake Analytics, even with the performance issue related to large table insertions.

I'd rate the solution eight out of ten.

Our customer use cases vary. Some are doing AI models to pick up trends in the telco, finance, or insurance sectors. Our insurance clients use it more as a data warehouse or data lake for their customer data.

It's the ease of use and the speed with which you can get access to the data. That's really what's added a lot of the value.

It's cloud-based technology, so they can spin it up a lot faster. It's quick and easy to use, quite flexible, and meets all the requirements for a data lake and data warehouse. You don't have to go through the old-school, long process to get a data lake or data warehouse going. I think the main important thing is that you can easily share data from Snowflake.

The elasticity and the scalability are quite valuable. And also, the ability to manage the cost.

Snowflake Analytics should probably have more built-in tools for master data management, data purification, and enhancement. Instead of using third-party tools, it would probably be good if it could be internal to Snowflake.

So, to have integrated capabilities for master data management.

I have been using it since 2021. I work with the latest enterprise edition, AWS version.

I would rate the stability a ten out of ten, where one is low and ten is high stability.

I would rate the scalability a ten out of ten, from one to ten, where one is low, and ten is high scalability.

We've got four clients in South Africa that are using it. One is medium, and the other two are enterprise.

The internal support is really good. They do obviously rely on partners like us to service the client. So it's a good mix of partner and Snowflake to provide the support.

Positive

I would rate my experience with the initial setup of Snowflake Analytics nine out of ten, where one is difficult, and ten is easy to set up.

If your models are not set up efficiently, you could end up with performance and cost issues. So, you need to use the Snowflake expert reviews to ensure your models are optimal. So you have to be wary of that.

It's only on cloud. It takes a few hours to deploy it, regardless of the size of the company. 

We do follow the Snowflake guidelines and process, but our approach to our use cases and projects is an agile methodology.

It's value for money.

I would rate the pricing of Snowflake Analytics a two out of ten, where one is cheap, and ten is expensive.

Especially when compared to competitors and what they offer, the pricing is good.

Users pay for credits. It's on a consumption basis.

Snowflake facilitates AI-driven projects for our customers.

Overall, I would rate it a ten out of ten, without a doubt. I think when it gets to cloud data warehouse and data sharing, it's the best.

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 use the tool for data warehousing, analytics, and machine learning.

The most valuable feature of the solution Stems from the fact that the tool offers a virtual warehouse along with multiple other features.

Snowflake's Snowpark is an area of concern where improvements are required. Snowflake's Snowpark is a pretty new concept. Snowflake's Snowpark should improve my adding new libraries and getting the codes deployed quickly.

I have been using Snowflake Analytics for more than five years. I am an end user of the product. I am also a developer who builds platforms for others with the help of Snowflake Analytics. There is no version of the product in general, as it is a full-fledged product.

It is a stable solution. Stability-wise, I rate the solution a ten out of ten.

The scalability of the product is pretty good. It is a highly scalable product. I rate the product's scalability a nine out of ten. Users can scale up or scale out the solution.

I use the solution daily in my company.

Presently, there aren't many uses of the solution in my company. In the future, I believe that the number of users of the product in my company will increase.

My company is in contact with a technical person from Snowflake Analytics, who helps us. I rate the technical support a seven or eight out of ten.

Positive

I have experience with Oracle, Microsoft SQL Server, and AWS.

My company started to use Snowflake Analytics since we did not want to have an on-premises product. Snowflake Analytics was also cost-effective for our company.

I rate the product's initial setup phase an eight or nine on a scale of one to ten, where one is a difficult process, and ten is an easy process. The setup process is straightforward.

The deployment requires users to have an account, and since it is a SaaS solution, Snowflake Analytics manages all the Infrastructures based on which the tool provides the user login details. The user creation is also pretty smooth with the product, so a user can start using it quickly.

The solution is deployed on the cloud services offered by AWS.

The solution can be deployed in a week, along with the licensing part.

For the deployment of the product, one architect from Snowflake, one person from AWS, and two admin developers, are required.

For my current project, a third-party involvement was there in the installation phase of the product. Previously, I was involved in the product's installation process, which involved the licensing part and setting up of the environment.

I rate the product price a seven on a scale of one to ten, where one is low price, and ten is high price. There are some additional costs based on the number of users or data warehouses.

Against Snowflake Analytics, my company has an option called Databricks. For our company's primary use cases, we prefer Snowflake Analytics.

Snowflake Analytics has supported our company's data warehousing and analytics part by helping us reduce our overall costs involved in such areas. The product also offers a better performance than the on-premises tools that are available.

Snowflake Analytics has improved our company's data-driven decision-making processes since the go-to-market timeline has been reduced with the use of the product. Earlier, the go-to-market timeline was such that my company used to take over a month to get new data for the end users. My company can get the data within a week or before, allowing users to use it for their analytics. The tool has reduced the go-to-market timeline.

The benefits from the product's scalability and performance features stem from the fact that it helps with the costs and reduced go-to-market timeline to get things up and ready for the end users, which is drastically much less than what the on-premises products offer.

The product's maintenance can be taken care of by two people.

The scalability of the product has impacted our company's data-driven decision-making since I have a lot of data with me currently. For initial loads, my company uses small warehouses, but after a while, it will be big warehouses. Based on the aforementioned details, my company makes data-driven decisions, like how much warehouse size we need or whether we need to scale up or scale out.

Snowflake Analytics is better than the other platforms that work on either a cloud or an on-premises version. Snowflake Analytics can improve in areas like scalability and in the way the queries are written.

Snowflake Analytics is pretty easy to use with the connectors for integration with the tools and systems in my company.

Others who plan to use the solution can benefit from it. I recommend others consider or try out a PoC phase or exploration process of the product to see whether the tool fits their use cases.

I rate the overall tool an eight out of ten.