Snowflake Reviews & Insights

We work as a team setting up the Snowflake environment for our clients, which includes setting up production, development, and testing environments, setting up the role-based access control, and implementing masking policies. We create pipelines using tools like Azure Data Factory, airflow, and Matillion to bring clients' raw data into a Snowflake. Then, we create procedures and tasks on top of them to clean that data and transform the data for reporting purposes, and then we use the Snowflake consumption layer for all our reporting purposes and create reports out of it. We also use Snowsight for some of the Snowflake usage reports, such as cost monitoring and query monitoring. We have also made use of the dynamic table tables where we had a requirement to refresh the tables on an hourly basis so that we don't have to create multiple elements like task stream. The dynamic table can take care of everything.

Snowflake is well suited when you have to store your data and you want easy scalability and increase or decrease the storage per your requirement. You can also control the computing cost, and if your computing cost is less than or equal to 10% of your storage cost, then you don't have to pay for computing, which makes it cost-effective as well.

We use Snowflake as a data warehouse and as an analytics environment. It stores data captured rom production and transformed data tables built for various use cases. These data sets are then piped into other BI tools for self serve analysis or queried directly within Snowflake to answer various analyses / business questions.

It works really well if you want one data warehouse that is very flexible, has many capabilities, and can serve multiple different use cases. It works great for a scaled tech company, but imagine for other businesses in different industries or different scales there could be other solutions that make more sense.

we use it to find information on orders and supply chain issues. we also use it for various AI/ML and data science projects.

good for structured data, not so good if the data is unstructured. seems well suited for most companies that have / had traditional ERP setups.

We use Snowflake daily, where all of our data is stored. It is straightforward to use and gives you a lot of flexibility to import your tables for custom analysis; it also provides more advanced functions, such as LISTAGG, which is not available on other cloud platforms.

If you need a quick query, snowflake is the way to go. It's super simple and scalable; we were struggling before with Azure, and with Snowflake, everything runs smoothly, and we have more control over our schemas and warehouses. Snowflake, in my opinion, is the next step when you want to scale your business and manage data. If your company is still small, there may be cheaper options.

We use Snowflake as our central data warehouse where we collect raw data from various different source systems and combine, harmonize, and transform them into reporting-ready formats. This helps us to better understand behavioral patterns amongst our customers, better understand the financials, the usage of our products, Go To Market strategies etc.

Very well suited for small and large scenarios. Very well suited for structured and semi-structured (e.g. JSON) data. Maybe less ideal for photo / video materials as there are cheaper storage alternatives.

At Numerator, Snowflake is used as a cloud based data storage platform and it also provides robust data warehousing solutions. The amount of data at Numerator is at a very large scale and it is growing very fast, Snowflake manages this large volume of data very efficiently. We also use Snowflake for analytical purposes, it provide us a platform to run SQL queries to analyze our data and create dashboards.

Snowflake is very well suited for all data storage and data warehousing needs of your business. As Snowflake is a cloud based platform, so you only pay for what you use, independent of the scale of your business. Snowflake can be used to store your data securely. Snowflake is slightly less appropriate for analytical purposes because it provides very limited data visualization options with very less feature.

We have used Snowflake to ingest, store and process data from the customer devices telemetry data for various insights. Ease of creating a pipeline from multiple sources without worrying about the amount of data and scalability of compute for different departments helped us a faster go to market solution. Continuous discovery is the core of our team means fail fast with minimum cost, with Snowflake we are able to do quick proof of concept and align project goals to the organization strategy

Data Security while ingestion and during storage, ability to support multiple data formats, dynamic compute to support varied use cases, low cost of maintenance and operations.

We use Snowflake in our organization as both our data lake & data warehouse. Instead of ETLing from source to a data lake to then architect our data, we have made internal schemas that we can ETL the data into. We then use an on-prem SQL Server to execute our SSIS packages, which support a DSN connection.

So far it's been very easy and very successful.

I am over our HR data, and we use Workday for our HR management system.

I have a script in place that runs reports on Workday and saves the results as CSVs. I can then use stages in Snowflake to insert these CSVs into Snowflake, then I can insert or truncate and replace these staged tables into a final schema. Then once these are in a schema I can reference them and build out my data models. In addition to ingesting CSVs, Snowflake has the ability to write a CSV file to our Amazon S3 bucket.

Ingesting these CSVs, transforming the data, then delivering it to a destination would've involved so much more coding than my current process if we were on any other platform.

I work on data analysis of multiple projects and use snowflake to write queries, pull data, and do some analysis. Typically, I use Snowflake to pull data from database in order to check the current status or pull experiment results for some tests we run. I like to use app. Snowflake instead of the main site.

Snowflake is a very good tool for analysis, easily used.

Our company has grown organically and by acquisition, and as a result we have a series of disparate software products with around 800 databases across different clouds and different databases - SQL Server, MySQL, Postgres, AWS, Azure, etc.

My challenge was to implement an awesome and consistent reporting suite that allowed customers full access to all of their data across all of our products. While the software development team is working on integrating all these disparate products, that's a significant multi-year challenge.

Implementing a data lake in Snowflake greatly empowered my team to make tremendous reporting, blending data from many sources, without adding any load to the transactional databases. What's more, Snowflake has a vast array of excellent built-in features such as row-level security, data masking, secure sharing, and other items that allowed us to push security right down to the database level so we secure data no matter how customers access it, without relying on complex where clauses, etc., in queries.

The Snowflake model of separating compute from storage and allowing us to have differing workloads for differing usage scenarios is also very helpful.

Snowflake is definitely suited for analytics, querying, reporting purposes.

It is not so well-suited for being your operational database. You could, but given it charges based on consumption, you wouldn't want to have it as a 24x7x365 thing.

We've also found once we adopted Snowflake we began to find many more areas where it solved problems for us - for example, secure sharing, data science explorations, training ML models, etc.