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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 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.
