Snowflake Analytics Reviews

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.

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.

In my company, we use Snowflake Analytics for the data lake process and data mining and not for data analytics. We use the solution for reporting and management capabilities.

My team describes Snowflake Analytics as a good solution, and our clients are also satisfied with the tool. Developers also find the product to be good since they have been able to do what we have received as a request till now.

Considering everything I have accessed, the product's dashboard is good since it provides multiple good options, including customization options. We can create the permissions or roles allowing us to give what we want to someone else.

I cannot comment on the product's stability because we are still struggling with its performance. The issues with its performance might possibly be due to how its setup phase got carried out by our company, but I am unsure about it. I cannot blame Snowflake and its architecture or application since our side might have caused the issues because we are new to the tool.

Snowflake can consider the aforementioned details for improvement.

I have been using Snowflake Analytics for six months. I am using the solution's latest version.

Scalability-wise, I rate the solution an eight out of ten.

Direct users of the tool in our company are around 12 percent to 15 percent of the employees since we use one of our background services for data load.

There has not been much interaction from my end with the solution's technical support. I rate the technical support an eight out of ten.

Positive

The solution is deployed on the cloud. We have our own cloud, like a SaaS model, and it is a private cloud.

Snowflake Analytics is a good tool based on the PoC phase and how we have implemented it in our company. Based on the comments and feedback from many, it looks like a good tool. Any company can use Snowflake Analytics, and it can be helpful.

The product is good, depending on how the developer can make it usable.

Overall, I rate the tool a nine 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.

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. 

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.

Snowflake's automatic optimization and scalability impressed us during POCs. Now, we store our data lake data in Snowflake, seamlessly integrating it with Azure Databricks for business transformations, ensuring smooth analytics processes.

The most valuable features of Snowflake for our data analytics are its time travel capability, allowing easy data recovery, and its automatic optimization of partitioning and clustering. Unlike other platforms where upfront table definition is necessary, Snowflake adjusts these internally based on usage patterns, relieving us of performance issues and redesign headaches. This stands out compared to platforms like Redshift and Azure Synapse.

Another standout feature is its seamless data-sharing capability, which proved invaluable for sharing data with other teams within the same company who had their own Snowflake accounts.

Snowflake could improve in the areas of advanced machine learning AML and generative AI. While it excels in data warehousing, it lags in these areas compared to other platforms. Snowflake needs to evolve further to gain more traction in the data science space.

I have been using Snowflake Analytics for two years.

I would rate the stability of Snowflake around an eight out of ten. We experienced only one outage in the last two years, lasting less than 30 minutes.

I would rate Snowflake's scalability at around an eight out of ten. While it is an excellent product, other platforms like Google BigQuery and Azure Databricks are also catching up in the market.

The technical support from Snowflake was excellent. They responded promptly to emails and even provided advance notice of outages. I would rate the support at an eight out of ten.

Positive

We transitioned to Snowflake from Azure Synapse because of performance issues with Power BI live reporting. Compared to Redshift and Azure Synapse Analytics, my experience with Snowflake was much easier and user-friendly.

Setting up Snowflake Analytics is straightforward, and not complex at all. Around 20 team members, including five leads, were involved in the deployment, with a mix of offshore and client development teams. Our team focused specifically on building the pipelines.

Snowflake simplified our data warehousing and analytics workflow. We seamlessly integrated it with Azure Databricks, following straightforward documentation provided by Snowflake. With minimal technical effort, we created tables and loaded data, leveraging Snowflake's automatic optimization features for partitioning and clustering.

Snowflake's scalability ensured seamless growth without compromising performance. With minimal worries about infrastructure, I focused on designing data models, confident in Snowflake's ability to handle our expanding needs.

I would rate the ease of integrating Snowflake with our existing infrastructure between four and five out of ten. The technical team didn't take much time once we completed the POC. Within a month, we understood Snowflake, created tables, populated data, connected Power BI, and tested performance. After approval, discussions on data modeling took a couple of months, but integration remained seamless. 

Overall, I would rate Snowflake Analytics at an eight out of ten.

We use the product for revenue analytics.

Snowflake Analytics opened up larger possibilities from a business perspective for our organization. Its virtualization feature has helped us significantly.

The product's valuable feature is an ease of use. It can run complex workloads with varied compute.

The platform's data governance space needs more capability. There could be AI-driven features and human interactive queries, i.e., natural language queries rather than standard SQL.

We have been using Snowflake Analytics for a few years.

I rate the platform's stability a ten out of ten.

We have around 500 Snowflake Analytics users in our organization. It is a scalable product. We continuously increase the usage as our business expands.

The technical support services are good. However, there could be knowledgeable support and faster response.

Positive

The initial setup was easy. The initial sandbox workload was up and running within a day. The additional configurations related to single sign-on took less than a week to complete.

We have all essential implementation tools available in-house to deploy the product. We do not require any external deployment packages.

The product's pricing is subjective. Snowflake operates on a credit basis. Thus, we can get licenses on pre-purchase credit. We get better discounts on larger purchases.

We explored a few products before.

I rate Snowflake Analytics an eight out of ten. If there is a good amount of data sharing, Snowflake is the right platform. I advise others to start with a small amount of data and then optimize it to set up the right data size.