Snowflake Analytics Reviews

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.

We primarily use the solution for database analytics. We can use it for various things, including structured and voice data. We also have a Microsoft SQL server for ONTP we combine and manage all data. We get a data map and have a data layer. For reporting, we use Power BI.

Snowflake is a bit different from a traditional Oracle or Microsoft database. There's a different user access methodology. It's very flexible and gives you good data visibility. You can push back the data one week, one month, or one year.

We can store unstructured data formats and create columns for everything and analyze the data via graphs. You can create pie charts and various visual graphs to study your data. We can't do this type of visualization with any SQL server or Oracle database.

There is very good granularity. The solution scales well. At the end of the day, you can use and effectively manage all of your big data. 

The data-sharing capabilities are good. You can access the data and share it in one click. If you want to share it, for example, with Oracle (if you use it), you can export data to it and share it. You can also use email or other methods. You can manage and share across various schemas. 

Its performance speed is very good. There's no limits to the amount of data you can take on. 

I haven't noticed any limitations with the solution. 

There could be more analytics. We find that IBM has a lot of pro data analytics that we use. 

The distribution methodology isn't as strong as Bethesda or SAP HANA. It's not as strong as other competitors.

I've used the solution for about a year.

The scalability and flexibility are excellent. This is very important to have in a Snowflake database. 

Everything works fine, therefore, I haven't had much content with the technical support team. 

I do not have any information in regards to ROI. I'd need more time with the solution. 

I'd recommend the solution to other users. 

I would rate the solution 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.

We use Snowflake as a database or data warehouse. We rely on AWS S3 services for computing and storage. It provides security, including role-based access and data encryption, and is handled at the database level in Snowflake. We use DBT for the data loading process, applying business logic to create the final tables in DBT, which then load the data.

Some features include the ability to handle semi-structured data, such as JSON. It supports various database sizes. Also, storage and compute resources are independent.

I have been using Snowflake Analytics for seven to eight months.

It's all hosted on AWS because Snowflake doesn't have its storage. Both computing, such as clustering and storage, are managed through AWS services like S3. Since S3 already provides scalability, Snowflake leverages those built-in properties.

When we started configuring Snowflake for our project development, we contacted support. They provided guidance on implementing security measures, including multi-factor authentication. The vendor required us to use MFA because it adds an extra layer of security, ensuring the protection of both development and production environments.

To enable multi-factor authentication, we were asked to download and use a separate application to receive notifications and approvals. This added security measure was mandatory to enhance the system's overall protection.

Positive

I worked with Redshift, and it's introduced many new features since then. Most recent projects have used Snowflake or other databases like SQL Server. While Redshift is still an option, Snowflake has gained more popularity.

Both Redshift and Snowflake are columnar databases, and they share similar features, such as the ability to handle different database sizes. Redshift has incorporated these features in its newer versions. If a project is fully integrated with AWS, clients might prefer Redshift as their data warehouse instead of Snowflake. It depends on their specific needs and infrastructure.

The initial setup wasn't too difficult or straightforward. Some configurations, like setting up files, followed a typical procedure. Certain steps, such as enabling multi-factor authentication, are essential nowadays for adding an extra layer of security. This has become common in most software services to enhance security.

The configuration process wasn’t overly challenging. It took me around one and a half days to complete the setup. We were a team of three developers, and after configuring, we created user credentials and began development.

The main costs in Snowflake come from computing and storage. If you allocate more storage but store less data with minimal usage, you can opt for a smaller database size, such as extra small, to save costs. Computing costs depend on how long processes or queries run. You can manage optimization and performance tuning to reduce costs. For example, if a process is scheduled to run every hour, but data refreshes only twice a day, you can adjust the schedule accordingly. You only run jobs when needed, reducing computing time and overall cost.

In short, you can control costs by managing storage and optimizing query performance based on actual data usage. Multiple factors, such as query timing and data refresh rates, play a role in minimizing expenses.

If you're using a service like Redshift, the pricing would likely be comparable to Snowflake. With Snowflake, you're paying for the service and the underlying cloud infrastructure since these are separate costs. Snowflake operates as a software package that must be installed on a cloud platform like Azure, GCP, or AWS, meaning you also need to use their computing, clustering, and storage services. This makes the total cost dependent on both Snowflake and the cloud provider.

Snowflake offers extensive features with a data warehouse background, including full SQL support and compatibility with T-SQL; other systems, like Redshift, also provide many similar features. That said, Snowflake has continued to grow in functionality and market appeal, making it more widespread. To thoroughly compare the two, you must look closely at the latest features Redshift has added, as it may offer comparable capabilities depending on your requirements.

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

We use it for tasks related to aggregate functions and statistical analysis.

I appreciate its straightforward interface. The platform not only provides ease of use but also stands out for its speedy execution, conveying a sense of robustness and reliability that I find appealing.

One notable absence in Snowflake's offerings is an on-premises solution.

I have been working with it for one year.

I would rate its stability capabilities nine out of ten.

It offers excellent scalability. I would rate it ten out of ten.

I've also had experience working with Redshift and databases from Microsoft. 
I have a decade or more of experience working with Microsoft, and after such an extended period, there's a natural inclination to explore something different and try new things.

The installation process is seamless. You can create an account and instantly begin working since the platform is readily available on the cloud. This ease of access is particularly advantageous for beginners.

I have been using free trial version.

I would recommend it. Overall, I would rate it nine out of ten.

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.

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.

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.