Overview
What is Snowflake?
The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed,…
TrustRadius Insights
Snowflake: a tool for all-level analysts
Your data team will love Snowflake, just be sure to manage cost
Unleash the power of data with Snowflake - the cloud data platform built for the future
Don't debate, just migrate to Snowflake already.
Snowflake Review
Snowflake Review
Snowflake - a modern cloud based Datawarehouse solution with great features!
Cloud-based Data Warehouse solution provided as a Saas.
Snowflake enables data pull for all levels of analysts
What makes Snowflake amazing
Snowflake the MPP data platform of the future, today!
Scalable Cloud Warehouse Tool with Low Cost of Entry and Minimal Staff Training
Data Warehousing - Snowflake gives you the best options!
Managing data warehouse with Snowflake is amazing
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Pricing
What is Snowflake?
The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed, and compliant ways. With it, users can securely access the…
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Alternatives Pricing
What is Amazon Redshift?
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
Product Demos
Snowflake Must Know New Objects | Chapter-8 | Snowflake Hands-on Tutorial
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Snowflake Database/Schema/Table & Container Hierarchy | Chapter-7 | Snowflake Hands-on Tutorial
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Product Details
- About
- Competitors
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What is Snowflake?
Snowflake Data Marketplace gives data scientists, business intelligence and analytics professionals, and everyone who desires data-driven decision-making access to live and ready-to-query data from your ecosystem of business partners and customers, and from potentially thousands of data providers and data service providers.
Snowflake Screenshots
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Snowflake Competitors
- Amazon Web Services
- Databricks Data Intelligence Platform
- · Google BigQuery
Snowflake Technical Details
Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
---|---|
Operating Systems | Unspecified |
Mobile Application | No |
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(336)Community Insights
- Business Problems Solved
- Pros
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Snowflake is widely used by organizations as a data lake and data warehouse, allowing users to blend data from multiple sources and generate comprehensive reports without impacting transactional databases. Users appreciate the excellent built-in features of Snowflake, such as row-level security, data masking, and secure sharing, which enable them to implement robust security measures at the database level. The software's model of separating compute from storage and accommodating different workloads for different scenarios proves to be highly advantageous for users. Additionally, Snowflake serves as a central repository for data, providing a single source of truth and facilitating data engineering for various use cases. Users can easily connect with data service providers, make data-driven decisions, and create modern and integrated data applications. The cost-effectiveness of Snowflake simplifies online data handling and supply to customers. It is widely used by data analytics clients across various industries due to its efficient, scalable, and easy-to-use data environment.
Furthermore, Snowflake enables quick and effective data querying, allowing users to gain insights into program aspects and user behavior for smarter business decisions. Its architecture, scalability, and data sharing capabilities make it a reliable cloud-based data warehousing solution for managing and sharing data efficiently. Many organizations are leveraging Snowflake to build a centralized solution for their reporting and analytics efforts. Snowflake's separation of compute from storage and pay-as-you-go pricing model enable intelligent and efficient budget planning and use. Customers are also using Snowflake to drive machine learning projects and gain valuable insights into their business data.
In addition to serving as a primary data warehouse for organizations, Snowflake is also instrumental in powering dashboards, machine learning solutions, ad-hoc queries, daily KPI aggregation, predictive modeling, customer acquisition management, telemetry insights, and more. Data teams across different departments rely on Snowflake for their compute purposes. The software offers an impressive data compression rate and faster data retrieval compared to other solutions like Amazon Redshift, making it an ideal choice as an enterprise data warehouse. The ease of use and accessibility of Snowflake make it widely adopted across entire organizations, catering to users with varying levels of data science skill. From basic data munging and querying to deep machine learning analysis and real-time access to data, Snowflake powers the entire data warehouse for many companies, providing crucial analytical understanding of performance and influencing business strategy.
Intuitive User Interface: Users have consistently praised Snowflake's intuitive and easy-to-use interface, with many stating that it is beginner-friendly. The drag and drop feature for tables into queries has been particularly helpful for users when writing complex queries.
Advanced Security Features: Snowflake's security features have received high praise from users, who feel confident in connecting with numerous business partners due to the platform's advanced security measures and effective programming. This positive sentiment indicates that Snowflake successfully prioritizes data protection and privacy.
Seamless Data Integration: Users appreciate Snowflake's ability to integrate, analyze, and transfer data from multiple clouds. They find it easy to have a transparent idea about data extraction and transfer. This feature allows users to efficiently work with their diverse datasets across different cloud platforms without any hassle or complications.
Limited Reporting Tools: Some users have mentioned that the reporting tools in the software are limited, which has led them to rely on additional tools like Tableau for more advanced reporting capabilities. They feel that having more robust built-in reporting features would enhance their data analysis and visualization options.
Cloud-Only Data Warehouse: The fact that the data warehouse is entirely cloud-based has raised concerns for some businesses. They worry about potential issues with data security, privacy, and control. These businesses prefer an on-premises solution or a hybrid approach where they can have more direct control over their data storage.
Complexity of JavaScript Implementation: Users have noted that implementing stored procedures through JavaScript can result in unnecessary overhead if their team lacks expertise in this programming language. This creates challenges when it comes to developing and maintaining complex procedures, leading to frustration among users who prefer a simpler implementation process.
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Reviews
(1-25 of 36)Premier data platform warehouse solution!
- Snowflake scales appropriately allowing you to manage expense for peak and off peak times for pulling and data retrieval and data centric processing jobs
- Snowflake offers a marketplace solution that allows you to sell and subscribe to different data sources
- Snowflake manages concurrency better in our trials than other premium competitors
- Snowflake has little to no setup and ramp up time
- Snowflake offers online training for various employee types
- Too many tiers with different credit allotments for cost to run
- No ability to go down in tiers easily if you select a higher tier, but there is an easy way to upgrade to a higher tier level!
- Support engineers are tied to success of Snowflake working on more advanced use cases with client engineering teams. This is great but they should find ways to build standard known use cases and have a repo to support for clients to look through or get trained on so they aren't always needing an one on one engagement.
Snowflake: a tool for all-level analysts
- Query.
- Easy to use.
- Low requirements.
- Data type.
- Speed.
- Integration.
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.
- Security
- Scaling
- Support
- Separating compute from storage
- Flexible disparate compute models
- Detailed history of all your queries and activity, for analysis/review/troubleshooting
- Cloning, undeletion, sharing - all tied to how Snowflake stores data
- There is no support for triggers
- Materialised views are limited to only one table
- You can't create dynamic SQL in functions
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.
Unleash the power of data with Snowflake - the cloud data platform built for the future
- Ingestion from different cloud platforms like AWS, Azure and GCP
- Ability to store data in multiple formats including structured and unstructured
- Compute is dynamic. Ability to chose a compute based on cost and performance.
- Data visualization capability has improved though have lot of areas of improvement
- Data governance and catalog capabilities
Don't debate, just migrate to Snowflake already.
So far it's been very easy and very successful.
- Processing speed
- Scaling warehouses
- Ease of use
- Hard to say, they're adding new features all the time.
- I feel like Snowflake's documentation is a little too thorough, it can be hard to understand
- I'm not a fan of the web interface, I use DBeaver instead.
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.
Snowflake Review
- Speed
- UI
- Short keys
- Auto formatting
- Organizing multiple queries/projects
- Data sharing without physically moving data and without compromising security aspect
- Unlimited scalability and elasticity (with proven client examples)
- Almost no to very little maintenance (Snowflake takes care of it for you)
- Graphical user interface for developers to build their applications
- In-built CI-CD integration capabilities
- In-built data lineage capabilities
Cloud-based Data Warehouse solution provided as a Saas.
- Cloud-based Data warehousing
- Very fast data processing and Analytics
- easy to learn and work
- limitation with unstructured data
- while importing data from other sources only bulk load
Snowflake enables data pull for all levels of analysts
- data pull
- access management
- data transfer
- speed
- data type
- better integration
What makes Snowflake amazing
- Provide a front to make queries of different DBs in a single place
- Cross tables from different schemas
- Run complex queries faster than other providers
- Graphic outputs
- Dashboard creation
Snowflake the MPP data platform of the future, today!
- Massive parallel processing
- SQL for schema-on-read data lakes
- Secure and compressed storage for semi-structured data
- Support site
- Transparency on performance
- Full text search
- Efficient handling of large data sets in a scalable way
- Ability to connect from a variety of tools (Tableau, Metabase, JDBC, many more)
- Querying and output of data in JSON format
- Built-in query and export tools
- Security roles and access are hard to understand and manage
- Rollout of new features seems to heavily favor US West coast, everyone else has to wait
- Visual interface does not allow you to manage custom functions and routines. For those, it is code only. Management of tables and views is visual.
If your tools can only connect by JDBC (no native Snowflake connector) you may have some issues with large data sets. This is really a limitation of using JDBC, not Snowflake, but it is something to be aware of.
- User friendly.
- Easy to use even for beginners.
- Specifically, I like the drag and drop of tables into the query which makes it easier to write complex queries.
- I am not able to recover the query sheets which got deleted by mistake.
- Higher prices.
- Easier integration option with BI tools.
- This is a cloud-based application that provides me a secure platform for the easy sharing of data with my clients.
- I feel confident to connect with my numerous business partners just because of its advanced security and effective programming.
- Through this tool, I am capable of integrating, analyzing, and the entire data in a precise form and with analytics as well, due to which I always have a transparent idea about the data extraction and its transfer from multiple clouds.
- This tool is very much technical and proper knowledge is required, so mostly you have to hire an IT team.
- I wish if various videos could be available for basic quires like its initiation, then I think it would act as a guideline and would help the beginners a lot.
- Computation is handled under the hood, freeing resources that would be used for maintainence of clusters
- Handling large data scale and ingestion
- Ability to query large volumes of data with speed due to their unique architecture
- Snowflake UI can be clunky and breaks sometimes, which can be annoying
- Snowflake has to be paired with the Data Build Tool (DBT) to allow for templating and macro usage. No inbuilt solution.
- Snowflake Python connector development doesn't necessarily track popular packages such as Pandas as quickly as Pandas releases
- Could do with better machine learning capability over warehouse tables, but I assume this is coming soon
However, it does require a dedicated team and an upfront cost in setting up an structuring the warehouse. Some solutions such as AWS's Redshift or GCP's Bigquery could be a better alternatives if is already within the AWS or GCP ecosystem. Bigquery in particular has a low upfront cost and a better pricing model.
Best Analytics Database in existence
- storage
- compute
- query time
- admin maintenance
- lack of multiple indexes
- indexing by primary key is non performant
Able to be setup and administered by a lightly technical SQL user !
Performance performance performance
Able to query terabytes of data in seconds
- Snowflake is very easy to use and doesn't have heavy DBA overhead.
- Snowflake's ability to separate storage and compute is a radical departure from how databases of the past were built.
- Snowflake is very easy for customers to scale as their needs shrink and grow.
- Snowflake still has room to grow in the advanced analytics use cases with the addition of Python, Scala, or Java running natively on Snowflake virtual warehouses.
- Snowflake can be somewhat unintuitive for customers coming from a prior RDBMS background because some of the concepts are such a radical departure.
- Snowflake is still not especially suited for many database use cases, such as OLTP scenarios. It's hard to call this a con, since that's obviously not what the product was designed for, but users should be aware.
Snowflake is less suited to transaction processing scenarios and isn't a best choice to back up an online order processing system.
Is Snowflake really nirvana for data warehousing?
- Can scale up or across depending on workload type
- Separation of compute and storage
- Query Profiler gives you a pictorial idea of where the query is spending time
- Very expensive
- Needs to compute more to efficiently and cost-effectively deliver the data (needs to know your access patterns)
- Query history is tricky to play with and system catalog views are either nonexistent or not well designed
Snowflake has worked Great in our company!
- Snowflake computes really fast and can handle high volumes of data with ease.
- Snowflake has some features that have really helped DBAs and developers particularly with Time Travel, it has help us with many processes in our company where we had to rollback or re-apply sweep processes which would be time consuming.
- Snowflake has forced ETL developers to write better code (forcing ANSI standards) and avoided implicit conversions in specific data-types for different subject areas.
- Loading processes have improved. Prior to Snowflake, some ETL processes would take double the time to execute. With Snowflake those same processes finish in half the time.
- Integration with other tools, particularly with connectivity. We have observed metadata callbacks being generated which are time consuming and annoying. These can be avoided but extra work is required.
- Out of the box tools could be better. Specifically the querying tool is lacking a few features that other software offer for easy of use for users and developers.
- Not really a Con, but in Snowflake there's no concept for Indexes, maybe because they are not required but we have seen particular cases where we believe an index would help speed some of the queries being executed.
- In the cloud computing. Quick availability of data online.
- Data sharing across multiple departments and perhaps companies.
- Good for Archiving of data.
Snowflake is a future full stack database
- Hosted on cloud: Helps with scalability.
- Community is active and ever-growing: You will have someone from the community to help whenever you need it.
- The new functionality and thoughtful design are based on new world problems.
- Support for JSON and XML is one of the main advantages.
- Beta testing functionality needs to be properly tested and stick to the committed deadline set at the initial release.
- The ODBC connector could be improved to accommodate multiple roles.
- Updates on functionality need to be informed to the user. Just a document update might not be the right approach every time.
Amazing cloud-based DB
- 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.
A vast improvement to the cloud!
- 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.
The only reason why someone would is because it is pricy or if there isn't much data at one's organization.
Extremely fast and efficient data warehouse tool.
- 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.
- 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.
Snowflake's ease of use allows you to focus on what matters most - the data you're filling it with
- 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
Snowflake's 'data-warehouse-as-a-service' model lessens the maintenance tasks of optimization/tuning that have traditionally fallen to DBAs and ETL developers. There are no servers to manage, software to install, or indexes to tune. This allows data engineers and analysts to focus more exclusively on analytic tasks that will translate into growth for the company.