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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I used it for a short period during a Workshop Conference
What do you like best about the product?
I recall that Snowflake was very simple to use and pick up, especially during the short period that the conference took place.
What do you dislike about the product?
I recall that it was difficult to find helpful resources online, so if I struggled I had a hard time finding a place to get help.
What problems is the product solving and how is that benefiting you?
I used Snowflake during a workshop conference where it was being taught to us. We used it to analyze weather forecasts and predictions.
Good ol' SQL in a serverless world
What do you like best about the product?
How easy it integrated intake of raw data, such as JSON files from S3 and their ingestion in other AWS services such as SQS
What do you dislike about the product?
At the time, how obscure was the documentation
What problems is the product solving and how is that benefiting you?
Offering of standalone data lakes
Easy interface, concurrency and performance
What do you like best about the product?
Mobilizes data and securely shares the data and executes extreme workloads.
Easy access to data clouds providing solutions for data lakes, engineering & warehousing.
Easy access to data clouds providing solutions for data lakes, engineering & warehousing.
What do you dislike about the product?
Can have must better user experience overall.
What problems is the product solving and how is that benefiting you?
Much structured dataset connect to dynamo and mango dbs.
I worked as intern in ericcson there I have got a chance to learn snowflake
What do you like best about the product?
SQL and easy access to host data for work
What do you dislike about the product?
The UI/UX is not that great and better learning programs can be offered
What problems is the product solving and how is that benefiting you?
Can not share about company use case
Snowflake: A Lifesaver for My Data
What do you like best about the product?
I have been a long time user of Snowflake, my journey has been good. I am not a super techie but when I started using Snowflake the adpatation was easy and smooth. It's very fast and helps pulling up reports. Being a cloud application, managing large servers or anything is easy. Its a great organiser where everything is at one place and sharing with other users or stakeholders is also easy. As a data warehouse it also eliminates the headache of running out of space.
What do you dislike about the product?
Honestly, the biggest problem I have with Snowflake is figuring out how much it’s going to cost us. You’ve got to keep an eye on how much data you’re using and how often, and it’s easy to go over budget if you’re not careful. Also, it’s not always the easiest to learn for someone who isn’t super technical. It feels like you need to spend some time getting the hang of things, especially if you just want to do basic stuff quickly.
What problems is the product solving and how is that benefiting you?
Snowflake helps us manage all our data in one place without having to worry about setting up big servers or dealing with a lot of tech headaches. Before, we were struggling with slow data processing and reports taking forever to load. Now, with Snowflake, things are way faster and smoother.
Cloud data warehouse deployable on AWS, Azure, or GCP with DBT integration for transformations
What do you like best about the product?
The scale-up and scale-down features in Snowflake,that adjust based on workload, save costs. We can run complex queries on large datasets with minimal tuning. The unload feature is very helpful for downstream teams to easily access data. Snowflake's permission control allows us to grant access only to specific users, maintaining strong security. Zero-copy cloning and time travel are standout features that enhance efficiency and data management.
What do you dislike about the product?
As it's pay per use model someone should continuously monitor the compute usage otherwise it can become expensive. I noticed some issues with concurrencies when running multiple large queries simultaneously.It can intergrate with ETL tools like DBT, Airflow but the native ETL capabilities are less.
What problems is the product solving and how is that benefiting you?
When we migrated from Oracle to Snowflake, it solved a lot of our problems. With Oracle, scaling up our data was difficult and expensive because it required more hardware. Snowflake, helped us scale up and down easily. Also, queries that took a long time in Oracle now run much faster in Snowflake, especially when we're dealing with big datasets. Another big thing is that we can easily handle semi-structured data like JSON, which was much harder to work with in Oracle. Overall, Snowflake has made our data management much smoother and more efficient.
Snowflake is like a lifeline for a Data Engineer
What do you like best about the product?
Snowlake UI is very friendly and easy to use. Snowflake provides many features such as auto scaling, time travel etc which are managed by Snowflake only and Data Engineers do not need to worry about it.
What do you dislike about the product?
Snowflake is self managed so we don't have much control on it and somtimes cost can be very high if edge cases are not handled properly.
What problems is the product solving and how is that benefiting you?
Snowflake is completey self managed platform means we don't need to setup anything to run our workload thus it saves time and money
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.
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
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