Premier data platform warehouse 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 …
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,…
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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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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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Webinar: Snowflake on Azure: Modern Data Analytics
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Snowfall Projector Light
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Snowsight - Snowflake Modern Web UI | Chapter-5 | Snowflake Hands-on Tutorial
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Snowflake Database/Schema/Table & Container Hierarchy | Chapter-7 | Snowflake Hands-on Tutorial
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DesignsBySiCK.com Wash Away Fibrous Free Standing Lace FSL Snowflake demo
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Product Details
- About
- Competitors
- Tech Details
- FAQs
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 Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.
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 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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Product Introduction
Snowflake Competitors
- Amazon Web Services
- Databricks Lakehouse Platform
- · Google BigQuery
Snowflake Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
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 Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.
Amazon Web Services and Databricks Lakehouse Platform are common alternatives for Snowflake.
Reviewers rate Support Rating highest, with a score of 9.8.
The most common users of Snowflake are from Enterprises (1,001+ employees).
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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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January 24, 2024
Premier data platform warehouse 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 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.
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.
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
March 09, 2023
Unleash the power of data with Snowflake - the cloud data platform built for the future
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
- 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
August 10, 2021
Snowflake the MPP data platform of the future, today!
Snowflake is used as a scale-out data lake and data warehouse at Spireon. The separation of compute from storage enables the company to provide timely and scalable insights. The confluence of streaming and batch processing at a pay-as-you-go pricing model aids in being intelligent and efficient on budget planning and use.
- 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
Snowflake is used as the central data warehouse for data coming from all divisions of the College. Once in Snowflake, we use a variety of business intelligence tools to pull the data and do reporting and analysis. It also allows us to spend as we grow our data needs, rather than spending a huge up-front investment.
- 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.
Snowflake was our data warehousing solution of choice, which we migrated from Treasure Data. It is used across the whole organization. It is used as a source of data for the entire organization, powering our dashboards and machine learning solutions.
- 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
April 30, 2021
Best Analytics Database in existence
Powers our entire data warehouse
- storage
- compute
- query time
- admin maintenance
- lack of multiple indexes
- indexing by primary key is non performant
Snowflake is a modern cloud data platform that InterWorks refers, resells, and implements for our data analytics clients. We also use it to back our internal data analytics initiatives within InterWorks. Customers of ours use Snowflake across every vertical and industry for any problem where they need an efficient, easy to use, scalable data environment.
- 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.
January 05, 2021
Is Snowflake really nirvana for data warehousing?
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).
- 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
December 28, 2020
Snowflake is a future full stack database
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.
- 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.
November 25, 2019
Amazing cloud-based DB
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 20, 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.
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.
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
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
May 05, 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.
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.
January 25, 2016
Why I love Snowflake!!!
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