Mostly, we use it for the data warehousing side of use cases, where you have, like, a huge amount of data, and you are required to do reporting in terms of data science, data warehousing, or ad hoc reporting. The use cases we have used are, for example, data coming from MedTech devices, mostly sensor data, which we need to load in Snowflake and do data analytics. We have been using the tool for a couple of MedTech clients.
Snowflake AI Data Cloud
SnowflakeExternal reviews
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Offers good performance and is not difficult to maintain
What is our primary use case?
What is most valuable?
The most important part of the tool is that computing and storage are totally separated, and it keeps on evolving every two weeks, with the tool having releases. New features are coming up in the tool. With respect to AI, the tool is also progressing well. The scalability and performance are quite good. If you have data, like in CSV or any other format, you can load it very quickly and then do your analysis. Columnar database performance, scalability, and the addition of new features are a few useful features of the tool.
What needs improvement?
I think people do not want to create pipelines for many customers now. Normally, we have this layer architecture, like layer one, layer two, layer three, or layer four, where we have raw data, integrations, business data, and then semantic data, so we have to create various pipelines. People don't have to create or maintain pipelines since, in the future, if there are any changes in the source data, it should be very easy to configure and create the pipeline rather than the developer doing that for them. Though it may not be possible to make improvements based on the expectations of the people, considering the AI market, code generation can be simplified a little bit by using streams. People want to be able to develop the pipeline without involving many developers by doing some configurations and creating the pipeline. The customer expectation is that they don't want to create tables for each report, but what happens currently is that if you don't create that, then you have to run the query every time. Suppose I have created raw data, and I want to do some aggregation. In that case, if I don't create a materialized view or a table, I have to run those aggregate queries again and again, which will cost me the cost attached to Snowflake usage. From an improvement perspective, Snowflake can evolve in terms of writing costly, expensive queries with less cost and try to see if pipeline development can be made a little easier.
For how long have I used the solution?
I have been using Snowflake for a year and a half.
What do I think about the scalability of the solution?
There were use cases where there were only 10 to 15 users. There was one requirement where the customer asked for 3,000 concurrent users to try to get a real-time report from the tool, but then our company suggested that Snowflake was not the right choice for them because it is more kind of a data warehouse, and they were looking more into transactional reporting. For Snowflake-based projects where we have worked, it is more concerning a smaller number of users, like around 20 users. However, if a huge number of users are required, Snowflake is not the right choice.
How are customer service and support?
My company has partnered with Snowflake. Normally, we reach out to the account manager or regional manager, and sometimes we get support. Most of the time, we ask for support from the architecture and solutions part of it to review it or for some workarounds. Right now, we have not gone for low-level technical support from Snowflake. Whatever we have worked on, we are able to manage.
Which solution did I use previously and why did I switch?
I have been working all my life on databases, so I have almost twenty five years of experience in databases starting from SQL, Oracle8i, Oracle 9i to MySQL, SQL Server and Redshift. I have also used Solr and Elasticsearch, which are not databases but all data-related things I have worked on, including PostgreSQL.
The main thing about Snowflake is that it is totally outside the customer's cloud. If I am an AWS customer, even if Snowflake is hosting on AWS, it is on a separate account right now. If somebody has some critical data that cannot be shared outside the cloud, then such customers or people are a little hesitant to use Snowflake. Recently, there were some breaches or password issues, so security concerns like that are there. There is also the costing part attached to the tool. Now, people are looking into tools that are available at a lower cost and offer more user-friendliness. The tool is a good data cloud product, but it is a little bit outside the customer's environment, which makes it difficult to convince the customer to use it.
How was the initial setup?
Speaking about the product's initial setup phase, I would say that the product is used just from the cloud. We have not installed it in any environment. I work with the tool's SaaS version.
What was our ROI?
The tool does add some value to the company. When it comes to pipeline development work, though customers expect it to be faster, I think if you have simple files, you can load them in a day and analyze the data. Productivity-wise, it is definitely much better compared to Redshift. Redshift Spectrum is catching up with Snowflake, but I have not explored it. To be very frank, I am not very familiar with Azure Data Warehouse, so I am not sure how it is different from Snowflake, but from what I have seen, it has been good in terms of productivity.
What's my experience with pricing, setup cost, and licensing?
The pricing part is based on the computing and storage. The costs are different and then there are services costs as well. I have heard that Snowflake is costlier than Redshift or GCP BigQuery. A small customer may not go for Snowflake.
What other advice do I have?
Speaking of how Snowflake enhances our company's AI-driven projects or analytics, I would say that the tool has features like Document AI and Snowflake Cortex. AI can be used if the tool is for very basic use cases, like anomaly detection or prediction. With simple use cases, you don't have to set up a big infrastructure. You just load data and use the tool's services. I have not used the tool for complex AI projects. I am not an AI person. Rather, I can be described as a data engineer or data architect. In our use cases, we have explored the AI feature of Snowflake more from document processing and doing a simple exploration of the feature. For customers, I have not used Snowflake's AI feature.
Speaking about how Snowflake's scalability feature impacted our data processing and analytics tasks, I would say that the tool has a virtual warehouse, so it really helps. You can scale based on your needs. You can change the warehouse sizing, which will help with the scalability. You can just increase the warehouse size, and it gets your work done.
There are various ways to integrate the tool. I think the tool has connectors also, but the external table is one way to load your data in Snowflake and start analyzing it quickly. Now, the tool also works with Apache Iceberg format, though I have not explored that. With respect to Snowpipe, getting data from CSV to Snowpipe are things we use, and they are all quite easy to use. In terms of native connectors to various data sources, though I have not explored them, I see the tool has support for various connectors. I believe that will be good. For most of the use cases, data is loaded onto S3, and then we use Snowpipe along with external tables and Snowpark ML to process the data.
Snowflake has something called Snowflake Horizon, which has bundled various features of data security, data governance, and compliance together, and they have come up with the package. The tool has very good data security in terms of masking data. You can have different roles and assign policies in terms of who you want to be able to see data of a particular department, so you can assign based on department ID that only certain people can see the data. I found good features in my various other cloud databases, and compared to them, Snowflake data security and data governance are quite capable.
I don't think it is difficult to maintain. As the organization grows, maintaining policies, user roles, and data masking policies might become a little tricky in Snowflake. In AWS, we have a well-architectured framework where you have a defined framework or pattern, and you try to reuse it and modify it as needed. I don't see such kind of information or patterns largely available in Snowflake. I think as an architect, if we have a well-architectured framework for Snowflake, it will be useful. In terms of maintenance, I think the performance and all is okay in the tool. Data governance and policy management are a little bit tedious for the tool.
I recommend the tool to others. People should only be okay with the product's cost.
I rate the tool an eight out of ten.
Generates metrics efficiently, but the integration process needs enhancement
What is most valuable?
The platform's most valuable features include its ability to effectively summarize and manage large datasets, allowing multiple teams to analyze and generate insights. Its integration with data lakes for business impact analysis, performance metrics, and KPIs is particularly important.
What needs improvement?
Improvement is needed in integrating external tools, such as data catalogs, which can be complicated due to differing formats and usage across departments. The goal is to enhance collaboration and streamline workflows.
What do I think about the scalability of the solution?
The product's scalability is crucial for managing petabyte-scale data generated daily across various regions, allowing for efficient data validation and handling.
How was the initial setup?
The primary challenges during the initial setup were the high pricing and uncertainties regarding future costs associated with data usage.
The deployment involved consultation among managers, agreement on on-site requirements, scale calculations, and collaboration with engineers for setup approval.
I rate the process a seven out of ten.
What other advice do I have?
Snowflake is integrated through a complex workflow that involves collecting data on the publisher side, using tools like Airflow and Kafka for batch jobs, and frequently importing data into the product from various sources, including S3 and Data Lakes. It creates a smooth data pipeline.
I rate it a seven out of ten.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Cloud based data warehousing
What do you like best about the product?
Scalability, Performance,Ease of use,Data Sharing, Integration and Zero management
What do you dislike about the product?
Cost, Complexityfor New user and data transfer cost
What problems is the product solving and how is that benefiting you?
Managing data warehousing solutions for our Data Products
Best SaaS data warehouse software
What do you like best about the product?
1. If you know SQL commands then zero learning curve.
2. Very easy to learn and master
3. Very good Analytical tool
4.Snowflake Copilot is extremely helpful for analysis and trending. It can geneate queries for you and recomend query modifications.
5. Auto space management reduced lot of Admin work
2. Very easy to learn and master
3. Very good Analytical tool
4.Snowflake Copilot is extremely helpful for analysis and trending. It can geneate queries for you and recomend query modifications.
5. Auto space management reduced lot of Admin work
What do you dislike about the product?
Its pricing model is high. It is difficult to control the access at table level or object level
What problems is the product solving and how is that benefiting you?
Easy administration , easy to scale , SAAS , Easy to learn and master
Snowflake - A Full Fledged Data Ware house
What do you like best about the product?
I worked with many data warehouses but I feel like comfortable with Snowflake. I see everything put on built in within it. I can execute SQL query, I can use python script and I can create Tasks, workflows and many more withing snowflake.
I feel like there is no dependency of any other tool to make a complete data requirement except the data source.
I feel like there is no dependency of any other tool to make a complete data requirement except the data source.
What do you dislike about the product?
As Far I used till, I see no dislike in my perspective.
What problems is the product solving and how is that benefiting you?
I yse Snowflake in my day to day work. I use for Data Ware house. We store the raw data into Snowflake in different Schemas and transform and curate it into different schemas.
"Snowflake: Scalable, Secure Cloud Data Platform
What do you like best about the product?
Important point that I like is snowflake is very much easy to use we use SQL language to perform data integration and manipulation inbuilt ETL and warehousing concepts are best and it's ui is super smooth.
What do you dislike about the product?
Pricing should be a factor for user that due to its premium features little expensive and need more learning materials guides for certification
Certification price little high for snowflake
Certification price little high for snowflake
What problems is the product solving and how is that benefiting you?
Data integration, ETL , Analysis, Data warehouse it's a cloud based platform so any system you can work and the team will work simultaneously so it's always beneficial for organisation.
Best Data Warehousing Tool
What do you like best about the product?
Very easy to use, fast, and efficient. Loved it.
What do you dislike about the product?
I found no downsides to Snowflake until now.
What problems is the product solving and how is that benefiting you?
Helped me in building and maintaining my client's datawarehouse.
Most easiest platform to edit and write sql query
What do you like best about the product?
Compared to other competitive platform easy to write query and data is very strongly encrypted
What do you dislike about the product?
Technical glitches and non-uniformity between public and personal workspace
What problems is the product solving and how is that benefiting you?
As a finance analysis I expect the data that I use to extract query should have strong security and snowflake performs this effectively than others
Great product
What do you like best about the product?
Aggregates various data sources to enable querying across these datasets more quickly and efficiently.
What do you dislike about the product?
Effectively migrating our existing databases to Snowflake takes time and resources
What problems is the product solving and how is that benefiting you?
Able to efficiently and quickly query across multiple large databases to train and validate ML models without having to wait tens of minutes for the query to run and without having to worry about Postgres inconsistency errors when querying an active read replica. Also able to easily keep track of and share queries with others.
Amazing utility for data management and analysis
What do you like best about the product?
As a data scientist, I've explored various tools and platforms designed to enhance data management and analysis, among which Snowflake has stood out for its user-friendly interface. It allows for easy use, even from those with minimal AI background. It's also quite scalable in terms of performance as the data warehousing capabilities can process the vast amounts of real-time data I have. Finally, Snowflake's support and community are incredibly helpful. Any issues or questions are quickly addressed, and the wealth of resources available online makes troubleshooting and learning new features straightforward.
What do you dislike about the product?
One of the primary concerns might be cost. The pricing model is based on compute and storage usage can become expensive, but no way around this
What problems is the product solving and how is that benefiting you?
Warehouse for processing real-time time series data
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