Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
$0.24
per GB per month
Snowflake
Score 8.7 out of 10
N/A
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.
Biggest advantage of Amazon Redshift is it's part of the aws ecosystem. When tuned well it is also very cheap compared to something like snowflake. And compared to spark or databricks, Amazon Redshift is a solid warehouse that's well suited for tabular data. We use it for user …
Amazon Redshifts has fewer features but at the same time, you also have some gains once it is running on AWS Cloud and it is really easy to set up. Besides that, in our case, it is a bit cheaper and we don't really need the extra features that you can find on Snowflake. Another …
Amazon Redshift, BigQuery, and Snowflake are all fully managed data warehouse services that are designed to handle large volumes of structured data and support business intelligence and analytics efforts. However, Amazon Redshift has the upper hand with its cost-effective …
We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014. Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure. Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a …
Redshift leapfrogged Hive back when Hive was trying to figure out how to implement indexes, providing a more stable, standardized (postgres), easy to use (any postgres client), easier to administer, and scalable solution for querying server logs and raw usage data.
Amazon Redshift is one of the fastest service offerings available in the market now. Plus you get an advantage of using a cutting edge compute service offering from AWS. Other technologies are fast but not as good as Amazon Redshift, I would say. Our business is interested in …
1. Redshift has better compression (automated) consuming less space then competitors 2. Automated Vacuum Delete for having consistent performance 3. AWS introduced ra3 node types for simple separation of compute and storage
Its definitely an improvement on all fronts for our business needs. Again, our MySQL server was really slow and we needed a more efficient solution. It was a major upgrade, but it is much more expensive than an in house server. It was expected but I'd say that lots of headaches …
Amazon Redshift supports multiple data formats including multiple structured data formats. And it is easy to implement a cluster if you do not have knowledge of data lake solution. Also when you do not need a lot of resources, you can just scale down so you do not have to spend …
Amazon Redshift has a better UI, hands down. And it is easy to integrate with bigger tools like Talend. It has many issues when it comes to understanding the architect perspective like Toad, which has a better UI for architect data together. However, that is because we are not …
We like Snowflake for its separation of computing and storage and also the separation of data warehouse different users. We replaced Redshift with Snowflake. However, Snowflake is great for its pay for performance kind of methodology.
Azure SQL Database was discarded because of a less attractive licensing, costs, plus its integrates poorly with many of the Azure offerings as say Azure Data Factory - it is not a true ETL yet. Also, the rest of the tools used were of Open Source type and it did not look like a …
It works on the cloud and we use the platform Dbeaver which is very unique and easy to maintain. There are very limited tools of this kind but the security issues are pretty high within those tools.
As our applications are hosted on AWS service, Redshift is the best option for us. Also, it provide a near to real-time performance on limited datasets and less complex queries. High availability is the major concern for any growing business and AWS is the best option for this. …
We are currently on Redshift, because it was out before Snowflake. However, Snowflake looks promising. It's the new shiny toy that gives options that Redshift does not provide for. The big thing is that storage and compute can be scaled separately, whereas you cannot do that in …
Most of our stack is on AWS, so while Snowflake and BigQuery was a viable option from a performance perspective, it was easier to integrate with RedShift. We considered hosting SQL Server on AWS or using Amazon RDS (Postgres or MySQL), however, the self-service aspect of …
Snowflake supports semi-structured data types and provided solutions to manage/process the semi-structured data. It supported sharing data between the different accounts and makes it easy in the scale and scale down process. Snowflake doesn't limit users on the database.
Amazon Redshift is much easier to set up and start using. It interacts well with the PostgreSQL client (psql) and shares certain basic data dictionary, and people familiar with PostgreSQL feel right at home. The cluster is part of AWS services offering, and it works well with …
Some organizations use PostgreSQL as an OLAP store. PostgreSQL offers a modern SQL dialect, data types, and features that Redshift lacks. RDS is a great managed PostgreSQL product. However, PostgreSQL is a poor choice for a data warehouse. It's row-oriented storage requires …
Since we switch from amazon redshift to Snowflake, we found Snowflake is much better than redshift in many ways, including the data integrate and data pull. However, comparing directly pull data from amazon s3, Snowflake is quite slow in terms of data pull speed and the more …
We use these tools for applications they are better suited for vs a Snowflake. For e.g. MS Fabric has powerful agentic AI capabilities; Redshift is our go to choice for the TMT vertical within the organization and Databricks is the default choice for AI/ML applications.
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can …
These are comparable products that can make sense depending on the specific needs of your organization. All are certainly serviceable and have varying pros and cons. Snowflake seems to provide the greatest degree of flexibility and easy scalability as new data gets brought into …
We needed scalability and a new way of organizing our data; Snowflake allowed us to have a clearer view of our data warehouses and schemas. Snowflake is also way superior in terms of speed and quick insights from the raw data you query, which is very valuable to us.
Snowflake has an attractive pricing model with auto-suspend and auto-resume and pay per use. AWS Redshift requires higher administrative efforts to maintain and scale the platform whereas with Snowflake those admin tasks are not needed or automatically taken care of.
We had a MS SQL server with over 2 TB of ram & 51 processors that we were using, that could no longer handle our workload. Snowflake can handle 3 times that workload with ease and efficiency.
Snowflake is much faster and easier to write queries and pull data. But the visualization part of Snowflake is not as good as them. Also, Snowflake only supports SQL queries but not python or other languages. So basically Snowflake is the expert in its field but not suitable …
We particularly liked Snowflake's security model as well as its unique storage (whereby everything is essentially a pointer to immutable micro-partitions, which is the key behind its zero-copy cloning, its secure sharing, its time travel, etc.). and also how it separates …
While Snowflake is more open to cloud eco system, SAP integrated well with SAP eco system products like SAP ECC or SAP S/4. So for people who have invested heavily in SAP eco system including SAP ECC or S/4, it makes sense to go with SAP DWC which is also evolving very rapidly. …
In my opinion, the other tools have similar and some different features; however, when I ran proof of technologies between Synapse and Snowflake. Snowflake did things better or just had functionality that the other tools did not. One that stuck out at the time was scale up …
Each of the other solutions were cloud vendor specific, Snowflake can ride on either Amazon Web Services, Microsoft Azure, or Google Cloud. The fact that they are ANSI-sql compliant and have an effective means of offloading data makes them portable and easy to sell to teams …
Azure and Snowflake compared very similarly, but Snowflake provided more options to integrate and connect with tools/companies that were not partners. It seemed to be a more flexible environment. The barrier for entry on Oracle and Google we just too complicated. In particular, …
I have had the experience of using one more database management system at my previous workplace. What Snowflake provides is better user-friendly consoles, suggestions while writing a query, ease of access to connect to various BI platforms to analyze, [and a] more robust system …
Snowflake has won the match because it is giving an excellent performance with its efficient features and reliable results. This is a totally secure program for our precious and important data.
Our initial data warehousing solution was Treasure Data. We had issues with the costly pricing model, which would be exhorbitant if we want to hold our data in memory and query using Presto. As a result, some heavy lifting was done in Hive (managed by Treasure Data); …
In my experience running the data management practice at InterWorks, we believe that cloud data warehouse products will eventually serve the majority of data warehousing use cases and power data analytics at most companies. Of this cohort, we believe that Snowflake is the best …
Redshift compute and storage can be scaled up/down together (though they added some features recently, they don't quite add up). I haven't tried Avalanche or Firebolt but would love to in the near future, due to their pedigree or revolutionary billing methods.
- Cost was the main aspect on the decision. - Performance was in par or better compared to other tools in the market. - Snowflake in my opinion stacks better than other tools I have used in the past.
Accommodates future data types such as JSON and XML. Scalability is another advantage. Pay per use is beneficial for organizations like yours. Direct connectors with AWS help us to go with it. No limit on user creation and clone data not eating up extra disk space are a few …
Compared to Amazon Redshift, Snowflake is slightly easier and faster to achieve ROI but based on the user's perspective, the two tools have very little difference since both are leveraging SQL to pull data from AWS S3. Snowflake is also working with Microsoft Azure but it is …
Our issue with Redshift was that it was very expensive. On top of that, queries were still slow and if we used more of Redshift's memory, then it would have cost even more. Snowflake is not cheap, but less costly for us. Plus, the performance was much better. Also, we got to …
Amazon Redshift and Snowflake are both data warehouse software designed to store data for querying, analysis, and extraction. Mid-sized businesses are the most frequent users of both data warehouse solutions. Large enterprises are more likely to use Snowball, likely due to its ability to work with many types of data. Smaller businesses tend to use Amazon Redshift due to its usability and affordable pricing.
Features
Amazon Redshift and Snowflake both offer essential data warehouse features, including storage and querying data, but they also have some standout features that set them apart.
Amazon Redshift provides end-to-end encryption and highly customizable security features at all price points. Additionally, Amazon Redshift offers many integrations with other technologies, particularly with technologies on the AWS platform. Lastly, Amazon Redshift requires less technical expertise to use as it has a usable interface and secure backup features.
Snowflake provides native support for JSON documents, allowing for built-in functions and querying for JSON data. Additionally, Snowflake includes robust automation tools for data vacuuming and compression, which can save time for users. Lastly, Snowflake can handle complex data types like arrays and objects, making it a great choice for businesses with a lot of complex data.
Limitations
Though Amazon Redshift and Snowflake both offer robust data warehouse features, they also have some limitations that are important to consider.
Amazon Redshift has limited JSON support. By default, it splits all JSON data into strings, which can make it difficult to query and analyze. Database management for Amazon Redshift mostly has to be handled manually, as opposed to Snowflakes automation features. Lack of automation may make Amazon Redshift a poor choice for companies with small technical teams that can benefit from automation. Lastly, Amazon Redshift provides limited support for complex data types like arrays and objects.
Snowflake offers advanced security features, but they are not available at every price point. Users with small data storage needs, but important security needs may struggle to find the right Snowflake package for them. Snowflake also lacks the seamless AWS technology integrations that Amazon Redshift provides. Businesses with few technical staff members may also struggle with Snowflake’s user interface and backup features.
Pricing
Amazon Redshift offers a two-month trial where users can see if they like the technology before purchasing. Beyond the trial, Amazon Redshift is priced depending on storage needs and feature requirements. Pricing can start as low as $0.25 per hour, and reach as high as $13.04 per hour.
Snowflake offers a one-month free trial for users to try the software. Pricing for Snowflake is dependent on storage needs, platform, and geographic region and starts as low as $2.00 per hour. Snowflake includes scaling pricing based on workload, so a business with a rapidly changing workload may have some savings with Snowflake.
If the number of connections is expected to be low, but the amounts of data are large or projected to grow it is a good solutions especially if there is previous exposure to PostgreSQL. Speaking of Postgres, Redshift is based on several versions old releases of PostgreSQL so the developers would not be able to take advantage of some of the newer SQL language features. The queries need some fine-tuning still, indexing is not provided, but playing with sorting keys becomes necessary. Lastly, there is no notion of the Primary Key in Redshift so the business must be prepared to explain why duplication occurred (must be vigilant for)
If you need a quick query, snowflake is the way to go. It's super simple and scalable; we were struggling before with Azure, and with Snowflake, everything runs smoothly, and we have more control over our schemas and warehouses. Snowflake, in my opinion, is the next step when you want to scale your business and manage data. If your company is still small, there may be cheaper options.
Redshift is fully managed. Small teams do not have the resources to maintain a cluster. CloudWatch metrics are provided out-of-the-box, and it is easy to configure alarms.
Redshift's console allows you to easily inspect and manage queries, and manage the performance of the cluster.
Redshift is ubiquitous; many products (e.g., ETL services) integrate with it out-of-the-box.
Writing .csvs to S3 and querying them through Redshift Spectrum is convenient.
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
It could benefit from adding data integrity and programming tools common to other database management systems.
Amazon Redshift is based on PostgreSQL 8.0.2. That version of PostgreSQL was released in December 2006. While PostgreSQL was much improved since then, the new features were not implemented in Redshift. Many basic features are missing from it.
Primary keys can be declared but not enforced. Referential integrity (foreign keys) can be declared but not enforced. UNIQUE and CHECK constraints are not supported and cannot be declared.
IDENTITY can be declared on a column, and Redshift will put unique values into it. However: IDENTITY values in the newly inserted rows won’t be incremental or sequential. To implement a sequential number, you need to write your own custom code.
There are no stored procedures in Redshift. We are writing SQL script files, and then parsing and running them one statement at a time from a Python program. This also enabled us to implement execution-time error logging.
In SQL scripts, to check for the row count of affected rows, a complicated join query against some system tables or views has to be executed.
Data Control Language (DCL) does not exist. No statements like IF, WHILE, DO, RAISERROR, etc.
On performance of views… Views do not “pass-through” a query parameter which is a potential problem for performance.
When selecting against a view with the WHERE clause outside of the view, the inner query of the view will be executed first without consideration for the WHERE clause, and only then the WHERE clause will be applied.
Certain clauses of SQL work many times faster than other clauses. So be careful and test your statements for performance earlier rather than later, especially if working with a large data set.
There was a situation when DELETE FROM JOIN was unacceptably slow. Replacing JOIN with the USING clause made DELETE instantaneous.
Do not force customers to renew for same or higher amount to avoid loosing unused credits. Already paid credits should not expire (at least within a reasonable time frame), independent of renewal deal size.
SnowFlake is very cost effective and we also like the fact we can stop, start and spin up additional processing engines as we need to. We also like the fact that it's easy to connect our SQL IDEs to Snowflake and write our queries in the environment that we are used to
Overall it serves all our aspects of data management like data cleaning, data manipulation, and data reporting on the cloud platform. We can create stored procedures and triggers in it very easily as all the options are self suggested in it. We can easily attach the results of ARS to the other tools as well for drawing the statistical results.
The interface is similar to other SQL query systems I've used and is fairly easy to use. My only complaint is the syntax issues. Another thing is that the error messages are not always the easiest thing to understand, especially when you incorporate temp tables. Some of that is to be expected with any new database.
The support was great and helped us in a timely fashion. We did use a lot of online forums as well, but the official documentation was an ongoing one, and it did take more time for us to look through it. We would have probably chosen a competitor product had it not been for the great support
We have had terrific experiences with Snowflake support. They have drilled into queries and given us tremendous detail and helpful answers. In one case they even figured out how a particular product was interacting with Snowflake, via its queries, and gave us detail to go back to that product's vendor because the Snowflake support team identified a fault in its operation. We got it solved without lots of back-and-forth or finger-pointing because the Snowflake team gave such detailed information.
We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014. Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure. Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a data store. [Amazon] Redshift was ideal with the price structure, performance and ROI[.]
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can buy according to your needs. And the integration options with various tools like Sigma are add-ons.