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Filter 60 vetted Snowflake reviews and ratings
Reviews (1-19 of 19)
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.
- Extremely intuitive and easy to use querying language.
- Allows high performance querying of large data sets with very little setup and configuration.
- Interfaces very easily with AWS S3.
- Works seamlessly with both structured and unstructured data sets.
- Very granular security.
- IDE is OK but can be a bit clunky given it's web-based.
November 19, 2019
A vast improvement to the cloud!
Snowflake is being used on top of our Azure data warehouse. It solves our issue of queries running at the same time and has drastically improved our server performance. All of our reports connect to Snowflake and they all run very fast. It is currently being used across the entire company.
- You are able to adjust the power that you give a query. For the big queries, you can give more CPU to it so it runs faster.
- It can now sit on Google Cloud, S3, and Azure.
- Concurrency is a thing of the past.
- I'd love to see a desktop tool for Snowflake. Currently, it is all web-based.
- Database navigation can be smoother. It's not as efficient as a SQL workbench.
- Row-level security is possible, but it was difficult to figure out.
March 14, 2020
Snowflake enables data pull for all levels of analysts
My company switched from Amazon Redshift to Snowflake a while ago, and we got a huge improvement in data to pull by using Snowflake. The tool is widely used in multiple departments within the company and people with all level of data science skill and retrieve the data easily by using SQL.
- data pull
- access management
- data transfer
- speed
- data type
- better integration
March 03, 2020
Snowflake: a tool for all-level analysts
We use Snowflake to query data from AWS S3. SQL query is easy to use for all level of data analysts/data scientists and as a result, Python or R is not required to pull data from Snowflake. Snowflake is widely used in my organization from customer acquisition to customer management.
- Query.
- Easy to use.
- Low requirements.
- Data type.
- Speed.
- Integration.
It is used by the whole company. We went with Snowflake because it is faster than Vertica and easier to manage different warehouses with.
- Great for managing different warehouses.
- Quick and efficient.
- Doesn't have the function of auto-fill.
July 31, 2019
Extremely fast and efficient data warehouse tool.
Snowflake was implemented around 6 months ago to replace Redshift as the SQL level that allows us to query our data. Snowflake enables us to query our data quickly and effectively to get insights into various aspects of the program as well as various aspects of our users' behavior. It allows us to make smarter business decisions by making that data more readily available and reliable than our previous solution was.
- Resources that scale up and down automatically to ensure that queries run quickly and efficiently without paying for computing power that is not being used
- Much more reliable than our previous software
- No noticable limit to query size
- Runs very quickly
- The SQL syntax used is different from Redshift in a number of ways. Would be nice to have more readily available help documentation around the differences built into the system.
- Would be awesome of there was a way to see relations between tables more effectively.
June 10, 2019
Snowflake's ease of use allows you to focus on what matters most - the data you're filling it with
My company adopted Snowflake as our first cloud-based data warehouse. It is being used as a central repository for all company data from each business unit for the purposes of business intelligence.
- Ease of use
- Separation of storage and compute resources
- Simple to scale up or down with virtual warehouses
- Built-in support for the most popular data formats
- Standard SQL dialect
- Robust function library
- Lacks support for common table expressions
- Lacks support for correlated subqueries
- Better technical support for customer identified bugs
- Clearer pricing model
June 06, 2019
Snowflake - flexible and isolated workloads
Capital One has many LOBs (lines of business). I have supported IAM and Commercial LOBs. They are using Snowflake as a data warehouse solution. The team is also using Redshift. The team noticed the concurrency limit with Redshift. Snowflake is a simple architecture with isolated compute capabilities. Redshift does not separate storage and computer capacities. Based on the workloads, the capacity is scaled up and scaled down using the option: Dataware. Right now, Snowflake is the heart-cake requirement for the company.
- The beauty of Snowflake is virtual warehouses. This provides an isolated workload and capacity (Virtual warehouse ). This allows separating or categorizing workloads and query processing according to our requirements.
- Snowflake provides a unique architecture that supports structured and semi-structured data. provides the ready-made solutions to process with the semi-structured data such as JSON, Avro, ORC, Parquet, and XML
- SQL kind queries make easy to write/develop the code. Provided features such as materialized views to optimize the query processing and loads
- Snowflake database is managed by snowflake team. This simplifies the DBA job from maintenance kinds of tasks such as patchworks and regular upgrades.
- Performance degradation happens when many users are on the system at the same time.
- Snowflake support is slow often.
- Snowflake provides Virtual warehouses for having performance. however, it may cost more.
Snowflake is being used across our whole organization. It addresses the need to combine disparate data sources into one single data source and discover insights into our business that would not be easily possible with lots of disconnected data sources. It also delivers world-class query performance and the ability for different business divisions to query their own data quickly and create reports in a fraction of the time as opposed to a traditional data warehouse solution. We are also using it to drive machine learning, and the data scientists are gaining insights into our business data that would not be possible otherwise. It is addressing multiple business problems, from distribution of our products all the way through to billing.
- Reporting queries run in a fraction of the time that they would in our production systems. For example, we can take the original MS SQL reporting queries , that used to take hours to run in our OLTP databases, and convert it into snow SQL, and run almost the exact same query in Snowflake in minutes, if not seconds.
- Having all our different data sources in one data warehouse database means we can start looking for links between data sources and different business units to tie all our data together. We can see from when a customer was dialled, through to when/if they bought a product, to when they were actually billed, and identify where in the process we are most efficient, and where we can improve our services and product offerings.
- Having a truly automated database system, without the need to create indices or maintain them, means we can spend more time in gaining insights into our data and getting actual results/data out, rather than spending time managing and maintaining the solution.
- There is no easy way to schedule any type of task.
May 04, 2019
Cloud-Based Data Warehousing Done Right
We replaced our RDS Postgres based DW with Snowflake. We use it as the main data source for all analytics components within our SaaS product, internal reporting, and ad hoc analysis by power users. We are now also able to analyze JSON without the need for transformation by easily replicating S3 buckets with Snowpipe.
- Provision compute resources instantly and autoscaling. True elastic, pay as you go pricing model.
- Secure Data Sharing. No other vendor offers this. This is big if you have the need to do a lot of data extracts.
- Ability to segregate clusters of computer resources (warehouses) by use pool. You can give power users access without the fear of slowing down critical applications.
- Cloud first architecture offers simple integration with other cloud-centric technologies/tools like S3 storage, streaming/replication brokers like Kafka, Alooma and cloud base BI tools.
- Big Data analytics capabilities with the familiarity of ANSI SQL. Short learning curve.
- Compilation times on somewhat complex queries is high. We use materialized views to address this problem and take advantage of caching, but we believe there is room for improvement here.
- SLA's dependent on the big cloud player (AWS, Azure, GCP). If they have interruptions, you have interruptions. This is the current reality of cloud computing.
We use Snowflake as a virtual warehouse to hold our data in a layer which is organised in a way that is ready and efficient for analysing. We drop data into it with a scheduled ETL tool, and then use a BI tool to refresh the data extracts overnight each night.
- Scales up and down seamlessly
- Only charges when it's being used
- Easy and transparent to use
- Coding language is a little tricky in places, not quite the SQL used elsewhere
- Setting up not quite as seamless as expected
We use Snowflake for basic data munging from OLTP databases, APIs, etc. It is only used in our sizable Data department. It solves the problem of spinning up and managing a DW quickly without dedicated DBA support.
- A nice UI with options to see the SQL/Code to automate steps in the UI.
- Has great SQL features like LISTAGG or Count Distinct, which go above and beyond Oracle and MS SQL.
- It's very fast!
- The SQL editor. The worksheets are nice, but code editors today have auto-fill, debug highlighters, etc. They are working on this I'm told.
- Worksheets (where you edit SQL) cannot be exported today.
- Occasionally a bug is introduced during releases. It's no big deal.
A few months ago, we'd decided to migrate our on-premise BI stack to cloud as on-premise solutions were not able to meet growing demands. Clearly, we were looking for cloud-only solutions. After extensive research and POC of several tools, we've on-boarded Snowflake. Snowflake is primarily used by the BI developers as our primary data warehouse and the resulting data is used by the whole organization via the dashboard or Excel extracts. Impressive data compression rate and faster data retrieval make it the best choice as an enterprise data warehouse.
- Impressive data retrieval and data compression ratio.
- Semi-structured data (JSON, XML) can be loaded as is and retrieved in a tabular structure on the fly using the flatten function.
- Zero-copy cloning is an excellent feature which saves hours to refresh latest data in development instance.
- Extensive usages documentation with examples makes development easy.
- Stored procedures are implemented through JavaScript. Would be an additional overhead if your team doesn't have expertise.
- No option to run multiple queries and analyze the results set in the same console window.
May 21, 2019
Snowflake Performance for Large Company
We are using Snowflake as our data warehouse. We currently are loading in our tables and data from an old IBM System to Snowflake. It is currently being used by a few departments but is planning to be used across the whole organization.
- Performance is extremely fast
- Pay for what you use
- Simple and Pleasant User Interface
- The mouse pointer can be laggy at times when hovering over a preview of a table
- Make it easier to switch between data warehouses
It is used by Data teams across different departments of the company for compute purposes.
- On demand and instant spin up of compute instances is fantastic
- I like the browser based tabs which save queries
- Easy connection to Python and BI tools
- SQL editor can be better with autocomplete functions
April 25, 2019
The best data warehouse on the market
We use Snowflake across our entire organization. We selected Snowflake to replace Redshift as our data warehouse. All of our data now funnels into Snowflake and users are able to query it and draw insights that they have never had before. Additionally, we were able to build and release a reporting dashboard for our external clients to see all of their historical data with us in one place.
- You only pay for the resources when you are using them
- ANSI SQL compliant
- Great documentation
- Native Apache Spark connector
- Does not support Dynamic SQL
- Right now you can write User-Defined Functions (UDFs) in pure SQL or Javascript. I would love to see support for something like Python
- We have been seeing more downtime lately as of writing this review
August 06, 2018
Snowflake - a very cost effective JSON querying tool.
Snowflake is currently being used to ingest daily JSON files exported from an analytics package into S3. We use Snowflake for Ad Hoc Queries, aggregating daily KPIs and pushing that data into a SQL Server, as well as creating Tableau extracts for our dashboards. We have also started using it for deeper Machine Learning types of analysis - such as creating predictive models.
- Process Engine control - we can stop/pause/start engines for various tasks.
- Processing speed is adequate unless there are many users on at the same time.
- Web interface is easy and intuitive, like the fact that your queries are automatically saved in tabs.
- Very limited amount of tabs - saved queries, which requires us to store the code somewhere else and re-use existing queries.
- Performance can really be a problem if there are many users on the system at the same time.
- SnowFlake support sometimes can be hard to reach.
Snowflake is currently used by our business intelligence team. The data from Snowflake is used by entire organization in the form of Tableau reports/dashboards etc. Snowflake database is used to deliver information for the following:
- Data science projects for decision making on products, game performance, user acquisition etc.
- Marketing Programs. Data is used to target users for marketing special offers.
- Make changes and improve the game interface for the user.
- Data science projects for decision making on products, game performance, user acquisition etc.
- Marketing Programs. Data is used to target users for marketing special offers.
- Make changes and improve the game interface for the user.
- Ad-hoc extracts for functional teams such as marketing, product and third party vendors.
- Truly elastic & highly optimized SQL database
- Minimal management overhead
- Support for both structured and semi-structured data
- Uses standard SQL
- Seamless integration between file system & S3
- Documentation
October 11, 2017
Your DWH entirely on the cloud!
The analytics and reporting team uses Snowflake as their primary Business Intelligence (BI) tool. Combined with several reporting and charting tools, Snowflake provides insight across all departments in the organisation. Snowflake helps provide the business with crucial analytical understanding of performance which, in turn, influences business strategy for the entire organisation.
- Easy to set up and get running.
- Great support.
- Integrates well with Amazon AWS.
- Charting features could be better.
- Reporting tools are not very extensive, creating a need for combining with other tools like Tableau.
- Data Warehouse entirely on the cloud might be a problem for some businesses.
Snowflake Scorecard Summary
About Snowflake
Snowflake is the eponymous data warehouse with an emphasis on analysis, from the company in San Mateo, California.
Categories: Data Warehouse
Snowflake Technical Details
| Operating Systems: | Unspecified |
|---|---|
| Mobile Application: | No |