Snowflake AI Data Cloud

What do you like best about the product?

Depending on the size of your warehouse, we can handle a large amount of data without performance issues. This would be very difficult with an on-prem server.Further, the separation of storage and compute helps with resource management.Additionally, we're a Tableau shop, and Tableau has a built-in connector with Snowflake that is reliable and efficient.

What do you dislike about the product?

Cost management can be challenging. This is where the size of the warehouse comes into play (see What do you like best about Snowflake?). If you optimize the size of your warehouse small, costs will be smaller. Along these lines, it's best to understand credit usage early to make sure you're managing effectively. But overall, few negatives that can't be managed with some forethought.

What problems is the product solving and how is that benefiting you?

Our infrastructure is quite complex, but since Snowflake is fully managed, we no longer have to worry about provisioning or ongoing maintenance. This allows our data architecture team to focus on more valuable work, while our data analytics team benefits from uninterrupted access, as maintenance issues no longer cause downtime. Scalability is another key advantage—Snowflake efficiently processes large volumes of data without requiring our architecture team to oversee the infrastructure. This not only saves time but also enables quicker queries, even as our total data volume increases. Ultimately, this leads to faster project scaling, quicker insights, and allows us to devote less effort to managing our data strategy.

What do you like best about the product?

Snowflake’s ability to handle large volumes of structured and semi-structured data seamlessly is its biggest strength. The separation of compute and storage lets us scale resources independently, which improves performance during heavy reporting workloads. It also integrates smoothly with BI tools like Power BI and Tableau, making it easy to deliver insights quickly without manual data preparation.

What do you dislike about the product?

Although Snowflake is highly efficient, query costs can add up quickly if the virtual warehouses aren’t managed properly. Also, real-time data streaming capabilities are still somewhat limited compared to other cloud-native services. A more intuitive cost-monitoring dashboard and tighter integration with streaming platforms like Kafka would make it even more powerful.

What problems is the product solving and how is that benefiting you?

Snowflake has helped centralize large data sets from multiple sources—HR, finance, and donor systems—into one scalable warehouse. This unified platform allows us to run complex SQL queries, automate reporting, and generate real-time dashboards in Power BI. It also simplified data governance and eliminated storage constraints we had with traditional on-prem databases. Overall, it enables faster data-driven decision-making and supports analytics initiatives without extensive infrastructure management.

What do you like best about the product?

The most important part is we can do different types of big data engineering and is very easy to use. We can do the etl process in a single go. The code can be implemented easily in the process and we can connect snowflake support for any help. I mainly use it everyday as most of my work is dependent on it. It supports pyspark as a new feature.

What do you dislike about the product?

The cortex is newly integrated so many updates are yet to come. So still waiting for that.

What problems is the product solving and how is that benefiting you?

We are solving different types of business problems like data integration, data transformation and data security. Even we are performing data masking and integration for the third party business.

What do you like best about the product?

What I appreciate most about Snowflake is how many thoughtful, practical features exist beneath the surface that solve real operational problems:
The search optimization service has been transformative for high-cardinality lookups; queries searching by specific IDs or emails that used to take 30+ seconds now return in under a second, without any query rewrites or manual indexing.
I love the account-level result caching. It's not just that my queries benefit from caching; if anyone in the organization runs the same query, everyone benefits. This makes dashboards and recurring reports incredibly snappy without additional engineering effort.
The directory tables on external stages feature eliminates so much custom logic. It automatically tracks file metadata in cloud storage, making incremental data loading trivial without building elaborate file-tracking systems.
Finally, tag-based masking policies have scaled our data governance beautifully. Instead of applying policies table-by-table, we tag columns (PII, sensitive financial data, etc.), and policies auto-apply across hundreds of tables. It's governance that actually keeps up with our data growth.
It has these layers of operational intelligence that reduce busywork and let teams focus on actual analytics rather than infrastructure babysitting.

What do you dislike about the product?

The small file problem has been a real headache when I'm working with streaming data sources. Snowflake performs best with larger files (100-250MB compressed), but many of my real-world pipelines generate thousands of tiny files. This causes performance degradation and forcing me to build a separate compaction layer; adding complexity to what should be simple ingestion. UDF performance has been disappointing. Python and Java UDFs are noticeably slower than native SQL functions. For complex transformations, I've found it's often more efficient to process data outside Snowflake and load the results; which somewhat undermines the single-platform value I was hoping for. These aren't dealbreakers for me but they do require thoughtful architectural decisions

What problems is the product solving and how is that benefiting you?

Snowflake is primarily solving infrastructure complexity and scalability challenges in my analytics environment, and the benefits are tangible. My team focuses on building analytics rather than maintaining databases, which has freed up significant engineering time. With secure data sharing, I can provide partners and internal teams access to datasets without creating copies, managing APIs, or building custom access layers. This has dramatically reduced the time from "data request" to "data access" from weeks to hours. The core benefit is speed to insight with less operational overhead. I'm spending more time answering business questions and less time managing database infrastructure, which is exactly what I need from a modern data platform.

What do you like best about the product?

Separation of storage and compute, allowing independent scaling

What do you dislike about the product?

Pricing model can be difficult to predict for budgeting purposes

What problems is the product solving and how is that benefiting you?

Centralizes data from multiple sources into one platform