Snowflake Analytics Reviews

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.

The solution is a flexible data warehouse. It can be used for computing and storage. There's also the potential for automation. With Snowflake, you can use the same copy in multiple places.

Once your data is on Snowflake, you can have that data differential to reportings. You can start using the analytics on top of it as well.

The data integration is great. We don't have to do data pipeline creation or time travel testing. 

The storage is good.

It's got good optimization.

We really like the engineering and the data within Snowflake.

The performance has been good.

We like how we can have one copy that can be used in multiple places. 

We haven't seen any areas that are lacking. I can't recall coming across missing features. Over the last two to three years, we haven't had issues. 

I've used the solution for almost three years now.

The solution is stable. A lot of it comes pre-configured and we haven't had any issues with it. There are no bugs or glitches and it doesn't crash or freeze.

With Snowflake, you are using the cloud, storing data in a cloud. It's scalable for that reason. It's not a problem if you need to have more data or more space.

Typically, the client will have their own support team. We don't really deal with the support side of things. 

My understanding is that their support is pretty good and clients are happy with their level of service. They work hard to resolve issues. 

Positive

The deployments vary. We first need to understand what the deployment is, and how much data will be pulled. When it comes to implementation, sometimes clients do some revisions and that must be taken into account as well. 

You need some expertise. Setting up Snowflake, the installation, configuration, and everything, are all separate skills altogether. 

That said, it's not overly difficult. Most of it the things you're doing are automatic value configuring for the grid function.

We are able to handle the initial setup for clients. We collaborate with Snowflake.

We haven't witnessed any ROI, however, it's not really a goal of the solution. 

The cost of the solution depends on how much you are using it, and therefore, the pricing can vary from company to company.

We are partners with Snowflake. 

In the last four or five years, Snowflake has actually blown up. Before that, it was quite low in terms of visibility and potential. We have now done multiple engagements on Snowflake.

Right now the snowflake is adding the multiple clients we using all editions.

We're using a variety of clouds, including AWS and Azure. 

I'd rate the solution nine out of ten. While we've done many complex implementations, Snowflake always has a way of simplifying things. 

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.

We use Snowflake Analytics for data cataloging, data science, and managing sales development.

It is quite a convenient tool and has a user-friendly authentication feature.

The platform could work easier for AI implementation compared to Databricks.

I have been using the platform for six months.

We have ten Snowflake Analytics users in our organization.

Compared to Snowflake, changing the data and gaining insights from the application development process while working with Databricks is simpler. It has a more user-friendly and accessible toolbox.

I rate Snowflake Analytics a seven out of ten. It is a good and useful platform. I recommend it to others. We can automate data pipelines using it. I advise others to make sure it meets their business requirements.

There are a lot of use cases for Snowflake. We can use it to build data pipelines. We can also use it to process terabytes of data to generate real-time analytics.

The computational power of Snowflake is very good. We get results very fast. Snowflake also has very good connectors for building pipelines. It makes it easy to connect to different cloud solutions.

Snowflake should include a WHERE clause for building data pipelines. When we create pipelines, Snowflake does not provide the WHERE clause. So the data transformation does not take place. We have to dump data as it is and transform it into the pipeline. If the WHERE clause is included, it will be very beneficial in the future.

I have been using Snowflake for around two years.

Snowflake is a stable product.

Snowflake is a scalable solution.

Snowflake’s technical support is good.

The initial setup was very straightforward. It is not very complex. If you are building something which requires customized queries or stored procedures, it is a little complex.

Initially, we faced some syntactic problems during setup. Then, there were some concerns about the WHERE clause. Apart from these, we did not face any other problems.

I went through the product documentation and deployed the solution myself. The deployment took one or two days. There were many different phases in the deployment strategy. I joined the team during deployment.

Using Snowflake, we can dump data through data pipelines and generate analytics. I do not belong to the sales team, so I do not know about the pricing information of Snowflake. I rate Snowflake an eight out of ten.

We use Snowflake Analytics to organize data. We create a fact and dimension table, relate it using its cardinality, load it from the source and use it for reporting.

It helps with business intelligence by providing analytics that can be reported.

There are issues while loading data from Snowflake Analytics to the Power BI reporting, and it doesn't get loaded directly. Sometimes we need to use queries to fix it, but it affects our reporting. Snowflake Analytics should be easily loaded into the reporting tool because it is the final end tool.

We have been using this solution for four months.

I cannot speak to stability because our data is not yet 100% loaded. It is a continuous ongoing process.

It is a scalable solution. In my organization, about four people, along with our clients, use Snowflake Analytics. So about four to eight people are using it. So far, it is not difficult to maintain the solution.

The initial setup was straightforward, neat, understandable and clear. We did the deployment in-house.

I do not have details about the licensing costs.

We did not review any other solution before Snowflake Analytics.

I rate this solution an eight out of ten. It is a wonderful tool. It easily organizes data, makes it accessible, loads it from the pipeline and helps with reporting.

It's primarily used for data migration, platform organization, and advanced analytics. As an Elite Partner, we provide consulting services to various customers.

It's a fast, 100% SaaS data cloud offering, which is unique in the market. Snowflake is available on all three major cloud providers: AWS, Azure, and GCP.

The UI could be more user-friendly.

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

I would rate the stability a ten out of ten. 

I would rate the scalability a ten out of ten. 

The support is very good. The documentation is very robust, a large online community and a dedicated support team is available.

Positive

I have experience with Microsoft Azure. I was working on the on-premises, and I was looking for a SaaS offering, that's why switched to Snowflake. 

Deployment is straightforward as it's a SaaS product. No maintenance required.

Overall, I would rate the solution a nine out of ten. I would definitely recommend using this solution. 

One advantage is that installation is unnecessary since it's cloud-based. You subscribe to a Snowflake instance, configure it, and start using it. It's very user-friendly and allows you to scale up or down based on usage.

I don't see many drawbacks with Snowflake Analytics, but it's not as mature as other tools. It is evolving and needs to integrate various features, like data loading and analytics, better. These components are not fully connected, so the tool should become a more integrated application.

The solution is costlier than Azure or other tools like Pentaho, which are cheaper but require more effort in ETL or ELT development and integrating all the tools.

Snowflake Analytics integrates well with the data, keeping everything within the same network and premises, eliminating network latency, and ensuring fast performance. Scalability is a strong point, as the compute and storage layers are separate, allowing independent scaling. This elasticity means that as more users or developers work on it, Snowflake can handle any load by leveraging CPU and GPU capacity.

I rate the overall solution an eight out of ten.