Most Helpful Review
The distributed architecture of Snowflake has the capacity to process huge datasets faster and allows us to scale up...
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
The most valuable features are powerful tools for ingestion, as data is in multiple systems.
The most valuable feature is the database.
It's good for storing historical data and handling analytics on a huge amount of data.
The ability to add multiple nodes without any restriction is the solution's most valuable aspect.
What comes with the standard setup is what we mostly use, but Ambari is the most important.
The best thing about this solution is that it is very powerful and very cheap.
The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics.
Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing.
I like the idea that you can assign roles and responsibilities, limiting access to data.
It has great flexibility whenever we are loading data and performs ELT (extract, load, transform) techniques instead of ETL.
The snapshot feature is good, the rollback feature is good and the interface is user-friendly.
The thing I find most valuable is that scalability, space storage, and computing power is separate. When you scale up, it is live from one second to the next — constantly available as you scale — so there is no downtime or interruption of services.
The initial setup is straightforward. You just need to follow the documentation.
They separate compute and storage. You can scale storage independently of the computer, or you can scale computing independently of storage. If you need to buy more computer parts you can add new virtual warehouses in Snowflake. Similarly, if you need more storage, you take more storage. It's most scalable in the database essentially; typically you don't have this scalability independence on-premises.
The most valuable features are the clustering, LS50, being able to change the size, the pay per use feature, the flexibility with many different sources and analytic applications.
As long as you don't need to worry about the storage or cost, this solution would be one of the best ones on the market for scalability purposes.
It would be helpful to have more information on how to best apply this solution to smaller organizations, with less data, and grow the data lake.
It would be good to have more advanced analytics tools.
The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment.
There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution.
In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency.
The upgrade path should be improved because it is not as easy as it should be.
We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it.
I would like to see more direct integration of visualization applications.
If you go with one cloud provider, you can't switch.
They do have a native connector to connect with integration tools for loading data, but it would be much better to have the functionality built-in.
Availability is a problem.
Maybe there could be some more connectors to other systems, but this is what they are constantly developing anyway.
The solution could improve the user interface and add functionality to the system.
The solution should offer an on-premises version also. We have some requirements where we would prefer to use it as a template.
Support needs improvement, as it can take several days before you get some initial support.
There are some stored procedures that we've had trouble with. The solution also needs to fine-tune the connectors to be able to connect into the system source.
Pricing and Cost Advice
This is a low cost and powerful solution.
There are no licensing costs involved, hence money is saved on the software infrastructure.
You pay based on the data that you are storing in the data warehouse and there are no maintenance costs.
The whole licensing system is based on credit points. You can also make a license agreement with the company so that you buy credit points and then you use them. What you do not use in one year can be carried over to the next year.
Pricing can be confusing for customers.
Answers from the Community
out of 30 in Data Warehouse
Average Words per Review
out of 30 in Data Warehouse
Average Words per Review
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Also Known As
|The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.|
Snowflake provides a data warehouse built for the cloud, delivering a solution capable of solving problems for which legacy, on-premises and cloud data platforms were not designed.
Learn more about Apache Hadoop
Learn more about Snowflake
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Software R&D Company34%
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Financial Services Firm15%
Software R&D Company39%
Financial Services Firm8%
Comms Service Provider7%