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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I use snowflake almost everyday to work with big datasets cleaning, querying, and analyzing. What i really like is how easily it connects with tools like tableau and looker for reporting, and even google sheets when someone just. The interface is clean and and easy to use, and bring able to run queries right in the browser makes my work faster. Getting started with it was very straightforward too. Review collected by and hosted on G2.com.

Honestly, the toughest part with snowflake is keeping costs under control. If a query isn’t optimised just exploring data can get expensive fast since uo’re charged gets processed. Snowflake is that it feels a bit easier to mange costs you only pay for the compute you actually use, and it can auto suspend when not in use. That makes it less stressful to dig into data without constantly worrying about the bill. Review collected by and hosted on G2.com.

Snowflake takes away the complexity of managing infrastructure and lets us focus purely on data. The separation of storage and compute means multiple teams can run queries without impacting each other’s performance. Its auto-scaling and pay-as-you-go model help control costs, and features like time travel, secure data sharing, and native integrations with BI/ETL tools make it a powerful platform. The ease of use is a huge upside—no database tuning or maintenance overhead required. Review collected by and hosted on G2.com.

While Snowflake is extremely powerful, pricing can become tricky to estimate when multiple teams are running large compute-heavy queries, especially without strict governance. The learning curve for cost optimization and warehouse sizing can be steep. Additionally, customer support response times could be faster, and some advanced features may require higher-tier plans. Review collected by and hosted on G2.com.

What I like most about Snowflake is how easily it handles large volumes of data with great performance. The ability to separate storage and compute is a huge advantage, as it allows us to scale resources up or down based on demand. It integrates smoothly with BI tools and supports secure data sharing, which makes collaboration across teams much easier. Overall, it’s a very reliable and flexible data platform. Review collected by and hosted on G2.com.

The main drawback is that costs can increase quickly if workloads are not monitored closely. The pricing model requires careful management to avoid surprises. Additionally, for new users, some of the advanced features require a learning curve. However, once you get familiar with the platform, it works very effectively. Review collected by and hosted on G2.com.

I like Snowflake for its ease of use—the interface is intuitive, and writing queries is straightforward. Ease of implementation is great too, as connecting to BI tools and setting up warehouses is quick. The platform offers a large number of features, including time travel, cloning, and data sharing. Ease of integration with ETL tools and other cloud services makes workflows seamless. It’s very reliable, and customer support has been responsive when needed. Because of its speed and scalability, I use it frequently for daily analytics tasks. Review collected by and hosted on G2.com.

The main challenge is cost management, as frequent queries or large warehouses can get expensive if not optimized. Some advanced features take time to learn, and initial setup for certain integrations can be tricky. While customer support is helpful, detailed guidance for complex scenarios could be improved. Overall, it works well, but careful monitoring is needed to avoid overspending Review collected by and hosted on G2.com.

What I love about Snowflake is how quickly it made working with large datasets feel effortless. I’ve run queries on billions of rows and still got results in seconds. The way it separates storage and compute means I can scale up for heavy workloads without overpaying when things are quiet. The interface feels familiar for anyone who knows SQL. I also appreciate how easy it is to share data securely with partners without the usual back-and-forth of exporting files. Review collected by and hosted on G2.com.

The main challenge I’ve faced is keeping compute costs in check. If you’re not careful with warehouse sizes or forgetting to suspend them, the bill can surprise you Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The cortex is newly integrated so many updates are yet to come. So still waiting for that. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Incredibly easy to use. Little DB admin work is ever necessary.

Ability to scale your compute needs instantaneously.

Integrates with a massive list of 3rd party tools. Virtually every SaaS product that can integrate with a DB has a native Snowflake integration.

Huge ecosystem of ready-to-serve 3rd party data sources. Review collected by and hosted on G2.com.

Snowflake is expensive.

It would be helpful if Snowflake integrated AI into the database more to help laypeople with performance tuning, query optimization, warehouse & cost management, etc. An inexperienced user can easy blow a lot of cash in Snowflake - without even realizing it - until the bills come due. Review collected by and hosted on G2.com.