Snowflake Professional, Scientific, and Technical Services 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.

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.

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.

Snowflake is giving a very robust cloud-based data warehousing solution with all the benefits of going to the cloud. Data sharing is one critical area in data management and Snowflake's innovative way of sharing data without physically moving it as a great feature. This is the exact needs that many of my clients had and Snowflake was just about a perfect fit!

Snowflake can be based on any of the three major cloud service providers namely GCP, Azure and AWS. So it is easy for organizations to stick to their preferred CSP and avail Snowflake services. So it is well suited for clients with any of the CSPs in their landscape and wants to make use of features of a robust cloud based data warehousing solution.

Snowflake within the organization is used as a warehouse location of data that is both refined and curated in nature and able to be used in sourcing for product use cases or offered in the various methods of data exchange. It solves for a single location source of data truth, and is the landing place of finished data engineering product for various use cases.

Snowflake data exchange is a great product with its competitors slowly following suit creating their own marketplaces of sort. The fact that the core data warehouse services on Snowflake are in most cases more ahead of other tools tied with the ability to drive marketplace exchanges from a B2B and B2C use case allows you to not only use your warehouse solution for building core data platform enterprise architecture, but it also provides a different way for you to look at delivering products to existing and prospect clients.

Snowflake was chosen as our data warehouse to replace Amazon Redshift. It is used by all areas of the company including data science, product teams, sales, marketing, operations, finance, executives, etc. It sources data from various cloud data sources and is being fed from integration tools (Fivetran, Stitch) or ETL (Matillion).

It's easy to scale up/down or expand/shrink. However, without any tuning capabilities in SQL, more materialization of the data needs to be done to use the database cost effectively. It is time to look into other services that don't charge arm and leg. Customer service is slow to respond.

Snowflake is acting as a company-wide Data Repository. With its cloud architecture and scalability, it addresses our storage, warehouse, and computation problem at the same time. Snowflake has powerful group roles and policy, which makes it beneficial for enterprise edition and usage. The ability to change the computational resources anytime is one of the biggest advantages. We have from manager to analyst been consuming the Snowflakes as a data repo. We have around a few hundred TB of data stored and it works smoothly on data growing exponentially too.

Snowflake acts as a single platform for both data storage and warehousing needs. For deployment purposes, it has the best group policy management and the best UI I've encountered personally. It also accommodates direct connection with AWS and Azure, which is another advantage. The only scenario where Snowflake would require second thought would be data that has PII information, as it doesn't have encryption options for such data points.

We use Snowflake on our Data Engineering team to house our data warehouse and offload a substantial amount of our ETL processes to Snowflake. Snowflake enables us to offer ultra-fast querying to the business to quickly gain insights into our current data set.

Snowflake works extremely well for storing a data warehouse as the querying is optimized for larger tables that are filled in batches. It also works extremely well with unstructured data and has basically replaced any need we previously had for NoSQL databases. Snowflake does not perform well for transactional databases.